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Variability is emerging as an integral part of development . It is therefore imperative to ask how to access the information contained in this variability . Yet most studies of development average their observations and , discarding the variability , seek to derive models , biological or physical , that explain these average observations . Here , we analyse this variability in a study of cell sheet folding in the green alga Volvox , whose spherical embryos turn themselves inside out in a process sharing invagination , expansion , involution , and peeling of a cell sheet with animal models of morphogenesis . We generalise our earlier , qualitative model of the initial stages of inversion by combining ideas from morphoelasticity and shell theory . Together with three-dimensional visualisations of inversion using light sheet microscopy , this yields a detailed , quantitative model of the entire inversion process . With this model , we show how the variability of inversion reveals that two separate , temporally uncoupled processes drive the initial invagination and subsequent expansion of the cell sheet . This implies a prototypical transition towards higher developmental complexity in the volvocine algae and provides proof of principle of analysing morphogenesis based on its variability .
‘The phenomena are always the same , and this is what matters to us , but their variations , for the greater or for the lesser , are beyond count . ’ Thus opined Xavier Bichat in the account of his investigations into life and death [1] and thereby spelt out how , to the present day , questions in developmental biology and cell sheet folding in particular are commonly approached: the vast majority of analyses average their experimental observations and seek to derive a model , biological or physical , that explains this average behaviour . In so doing , they discard the variability or deviations from average behaviour that are observed in experiments . A certain amount of noise is , however , unavoidable in biological systems; indeed , it may even be necessary for robust development , as demonstrated , for example , by Hong and colleagues [2] , who showed that variability in cell growth is necessary for reproducible sepal size and shape in Arabidopsis . The natural question of how to use this variability to infer developmental mechanisms appears to lie in uncharted waters , however . This is the question that we explore in this paper to provide proof of principle of analysing cell sheet folding based on its variability . Cell sheet folding pervades multicellular development , and its general principles have been established in a large body of previous work: local cellular changes can produce forces that are transmitted via cell–cell connections along the cell sheet and drive its global deformations [3 , 4] . Simple events of cell sheet folding , such as ventral furrow formation in Drosophila [5 , 6] , can be driven primarily by cell shape changes . In more complex metazoan developmental processes—such as gastrulation [7 , 8] , optic cup formation [9 , 10] , neurulation [11 , 12] , and related processes in vivo [13] and in vitro [14]—the effect of such cell shape changes is overlaid by that of other cellular changes such as cell migration , cell intercalation , cell differentiation , and cell division . Owing to this complexity , and in spite of significant progress in identifying the molecular components involved , the correspondence between local cellular changes and global deformations of cell sheets remains poorly understood . Biological analyses of morphogenesis are complemented , at a more physical level , by a whole host of mechanical models of morphogenesis . The first of these represented cells as discrete collections of springs and dashpots [15]; they were soon followed by elastic continuum models [16 , 17] . Notable among this early modelling of morphogenesis is , for example , the work of Davidson and colleagues [18 , 19] , who combined models of several mechanisms of sea urchin gastrulation with measurements of mechanical properties to test the plausibility of these different mechanisms . These models heralded the emergence of a veritable plethora of mechanical modelling approaches over the subsequent decades [20] , though the choice of model must ultimately be informed by the questions one seeks to answer [21] . More recent endeavours were directed at deriving models that can represent the chemical and mechanical contributions to morphogenesis and their interactions [22] and at establishing the continuum laws that govern these out-of-equilibrium processes [23] . However , all of this but emphasises a rather curious gap in the study of development: the importance of quantifying morphogenesis and its variability has been recognised [24 , 25] , but analyses of the variability of development have been few and far between . What experimental data there are on the variability of the mechanical properties of cell sheets suggest a large amount of variability ( [26] and references therein ) . The variability of the cell sheet deformations during development is even more unexplored , and accounts of this variability—e . g . , in the loach Misgurnus fossilis [27 , 28]—have often been merely descriptive . In this paper , we present the first comprehensive analysis of this variability in cell sheet folding and the lessons that can be drawn from it . The experimental system in which we explore these questions of variability is the multicellular green alga Volvox ( Fig 1A ) , of which Julian Huxley said that ‘In some colony like Volvox , there once lay hidden the secret of the body and mind of [humans]’ [29] . Indeed , Volvox and the related volvocine algal genera have been recognised since the work of Weismann [30] as model organisms for the evolution of multicellularity [31–33] , spawning more recent investigations of kindred questions in fluid dynamics and biological physics [34] . Being able to reproduce asexually , Volvox is perfectly suited for studying nongenetic sources of morphogenetic variation among genetically identical individuals . In the asexual life cycle , the cells of an adult Volvox spheroid ( Fig 1B ) are differentiated into biflagellate somatic cells and a small number of germ cells , or gonidia , that will form the next generation [31] . The somatic cells in the adult are embedded in a glycoprotein-rich extracellular matrix [35 , 36] . The germ cells undergo several rounds of cell division , after which each embryo consists of several thousand cells arrayed to form a thin , spherical sheet confined to a fluid-filled vesicle . Cells are connected to their neighbours by cytoplasmic bridges ( Fig 1B ) , thin membrane tubes resulting from incomplete cell division [37–39] . Those cell poles whence will emanate the flagella , however , point into the sphere at this stage , and so the embryos must turn themselves inside out through an opening at the anterior pole of the cell sheet ( the phialopore ) to enable motility and thus complete their embryonic development [31] . This process of inversion has become a model for the study of cell sheet deformations [40–42] . Inversion in Volvox [44 , 45] and in related species [46–49] results from cell shape changes only , without the complicating additional processes found in metazoan development discussed above . This simplification facilitates the study of morphogenesis . While different species of Volvox have developed different ways of turning themselves inside out [46] , here , we focus on the so-called type-B inversion arising , for example , in V . globator [44 , 46 , 50] . This shares features such as invagination and involution with developmental events in metazoans [51–53] . This inversion scenario is distinct from type-A inversion , in which four lips open at the anterior of the shell and peel back to achieve inversion [45] . This process is driven by a single wave of uniform cell shape changes moving from the anterior to the posterior pole of the embryo [45] . By contrast , type-B inversion involves different types of cell shape changes in different parts of the cell sheet [44] , the coupling of which has remained unclear . This inversion begins with the appearance of a circular bend region at the equator of the embryo ( Fig 1C and 1D , Fig 2A ) : cells there become wedge-shaped by developing narrow basal stalks [44] . At the same time , the cells move relative to the cytoplasmic bridges so as to be connected at their thin stalks , thus splaying the cells and bending the cell sheet [44] . Nishii and colleagues [54] showed that type-A inversion in V . carteri is arrested in the absence of analogous motion of cells relative to the cytoplasmic bridges . This relative motion is mediated by a motor protein , the kinesin InvA , associated to the microtubule cytoskeleton ( S1 Fig ) ; orthologues of InvA are found throughout the volvocine algae [32] . After invagination , the posterior hemisphere moves into the anterior ( Fig 1E ) , the phialopore widens , and the anterior hemisphere moves over the subjacent posterior ( Fig 1F ) while 'rolling' over a second circular bend region , the anterior fold [44] . Additional cell shape changes ( Fig 1D–1F , Fig 2B–2D ) in the anterior and posterior hemispheres are implicated in the relative contraction and expansion of either hemisphere with respect to the other [44] . This plethora of cell shape changes is possible as embryonic Volvox cells do not have a cell wall [31] . It is not yet known what triggers the initial cell shape changes , what determines their location , and what kind of signal drives the propagation of waves of cell shape changes in Volvox embryos . In the present study , we show that even without this knowledge , we are able to infer information on local changes from the variability of global dynamic embryo shapes . In a previous study [43] , we combined light sheet microscopy and theory to analyse the early stages of inversion , showing that only a combination of active bending and active stretching ( i . e . , expansion or contraction ) can account for the cell sheet deformations observed during invagination . The crucial role of active stretching was also highlighted by Nishii and Ogihara [55] , who showed that type-A inversion in V . carteri cannot complete if actomyosin-mediated contraction is inhibited chemically . We later analysed the mechanics of this competition between bending and stretching in more detail [56] . The general question of how the different parts of a morphogenetic process relate to each other , however , remained unanswered in this system , too: are the different deformations of either hemisphere during type-B inversion coupled ? What drives the 'peeling' of the anterior hemisphere ? The present analysis addresses these questions and naturally divides into three parts: we begin by deriving an average sequence of Volvox inversion and quantifying its variability . This consensus inversion sequence serves as a template for the mathematical analysis in the second part of the paper: building on our earlier , qualitative model of the early stages of inversion and combining ideas from morphoelasticity [57] and shell theories [58 , 59] , we derive a detailed , quantitative description of the entire process of inversion . In so doing , we show for the first time how detailed information on the underlying cellular changes can be deduced from deformations at the tissue level . In the third and final part of the paper , we compare the experimental distribution of variability to simulated distributions based on perturbations of the local active deformations in the model . We thus infer how the observed distribution of variability in the embryo shapes arises , and we find that inversion is driven by two separate , temporally uncoupled processes . This provides proof of principle of using developmental variability to infer developmental mechanisms and mechanics .
To define an average inversion sequence and analyse its mechanics , we compare the local geometry of the traced curves . The rather philosophical question of how to define an appropriate metric for this kind of comparison goes back at least to the work of D'Arcy Thompson [61] and has no unique answer . Thompson showed , for example , how the outlines of fish of different species could be mapped onto one another by dilations , shears , and compositions thereof . Our averaging approach must allow for the different types of variability that arise in Volvox inversion ( as discussed in the previous paragraph ) , while recognising that the posterior poles and the rims of the phialopores of the different embryos must correspond to each other . Our approach is therefore based on minimising the euclidean distance between individual embryo shapes and their averages , with alignments obtained using dynamic warping of shapes ( Materials and methods ) . Results are shown in Fig 4 . Averaging approaches that do not consider both stretching in time of individual inversions and local stretching of corresponding points of individual shapes tend to give unsatisfactory results: the simplest averaging approach is to align the inversion sequences by a single time point—for example , when the posterior-to-bend distance reaches half of its initial value ( Materials and methods and S2 Fig ) . The absence of time stretching , however , means that large variations arise at later stages of inversion . ( Given the dramatic embryonic shape changes during inversion , it is not surprising that there should be no single parameter that could be used to align inversions of different embryos . ) A better alignment is obtained if we allow stretching in time ( Materials and methods and S3 Fig ) , but this method , without local stretching of individual shapes relative to each other , produces unrepresentative kinks in the bend region of the average shapes ( S3 Fig ) . To quantify the time course of individual inversions further , we shift the time coordinate of each embryo half so that t = 0 is the time when it reaches the first fitted stage . We then define the average time ⟨t⟩ to be the average , over all embryo halves , of these shifted times . Plotting the time course of individual inversions in the resulting ( ⟨t⟩ , t ) diagram ( Fig 5A ) , we observe that different stages of inversion take different times in different embryos , with some embryos seeming to linger in certain stages . Nonetheless , despite this 'stop-and-go' behaviour , each time course is well approximated by a straight line in the ( ⟨t⟩ , t ) diagram ( Fig 5B ) , which signifies that inversion proceeds at constant speed in all embryos . To analyse the local variations of the embryo shapes , we define , at each point of the average shapes , a covariance ellipse . The curves that are parallel to the average shape and tangent to the covariance ellipse define what we shall term the standard deviation shape . These standard deviation shapes measure the variability of the average shapes and are shown in Fig 4 . The variations they represent naturally divide into two components: first , those variations that are parallel to the average shape and , second , those perpendicular to the average shape . The former represent mere local stretches of the average shapes , while the latter correspond to actual variations of the shapes; we shall therefore refer to the thickness of the standard deviation shapes as 'shape variation' in what follows . We report the mean shape variation and its standard error in Fig 5C . This plot shows that the mean shape variation reaches a maximal value around the stages in Fig 4G–4I: different embryos start from the same shape and reach the same inverted shape after inversion ( up to a scaling ) but may take different inversion paths . Plotting the mean shape variation for different averaging methods ( S4 Fig ) , we confirm that the present averaging method yields a better alignment than the alternative methods discussed earlier . It is intriguing , however , to note the spatial structure of the local shape variations . In particular , during the early stages of posterior inversion ( Fig 4D–4F ) , the shape variation is smaller in the active bend region than in the adjacent anterior fold ( Fig 1E , the second bend region of increased positive curvature ) . As the phialopore opens , and the anterior begins to peel back over the partially inverted posterior ( Fig 4H ) , the relative shape variation becomes smaller in the anterior fold . The initially small variation in the bend region is especially intriguing , since this is where cells become wedge-shaped to drive invagination , while the anterior fold bends passively [43] . In other words , the shape variation is reduced in the part of the cell sheet where the active cell shape changes that drive invagination arise . If there were no variability in the cell shape changes , then inversion could not fail . This correspondence therefore characterises what one might term , from a teleological point of view , a 'good' inversion . We shall focus on a less exalted question , the answer to which will be falsifiable , however: how is this spatial structure of the variability related to the mechanics of inversion ? Before we can address this question , we need to analyse the mechanics of inversion in some more detail . The second step of our analysis is to derive a quantitative theoretical model of inversion . We have previously described the early stages of inversion using a qualitative mathematical model [43] in which cell shape changes appear as local variations of the intrinsic ( meridional and circumferential ) curvatures κs0 , κϕ0 and stretches fs0 , fϕ0 of an elastic shell ( Fig 6A ) . Open , one-dimensional elastic filaments can simply adopt a shape in which the curvature and stretch are everywhere equal to their intrinsic values , but two-dimensional elastic shells cannot , in general , do this: the intrinsic curvatures and stretches may not be compatible with the global geometry , causing the shell to deform elastically and adopt actual ( meridional and circumferential ) curvatures κs , κϕ and stretches fs , fϕ different from the imposed intrinsic curvatures and stretches ( Fig 6A ) . A more technical discussion of these issues , couched in the language of differential geometry , is provided in [57] . Our previous model [43] revealed that active bending , active contraction , and active expansion are necessary for the early stages of inversion ( Fig 6C ) . The relation between these processes remained unclear , however , and the model could not describe the large deformations during later stages of inversion ( Fig 6D ) . Indeed , that model was derived under the assumption of small strains . While the elastic strains are small indeed ( since the metric tensor , which describes the deformed shape , is close to the intrinsic tensor defined by the cell shape changes ) , the geometric strains are large: both the metric tensor of the deformed shell and the intrinsic tensor differ considerably from the metric tensor of the undeformed sphere . We must therefore generalise our previous mathematical model by combining ideas from morphoelasticity and shell theories ( Materials and methods ) in order to obtain a quantitative description of the entire inversion process . The cell shape changes ( Fig 6B and 6C; see also Fig 1C–1F and Fig 2 ) observed previously in [44] suggest simple functional forms of the intrinsic stretches and curvatures defined in terms of 15 parameters ( Fig 6D–6F ) that vary over the course of inversion: the parameters f1 , … , f5 and κ1 , … , κ5 encode the magnitudes of the intrinsic stretches and curvatures of the different cell types that arise in different positions of the cell sheet at different times during inversion , while the parameters s1 <…< s5 encode the arclength positions of the transitions between cell types . These 15 parameters allow for a minimal representation of the cell shape changes [44] and ensuing variations of the intrinsic stretches and curvatures: We proceed to fit the generalised elastic model to the experimental average embryo shapes ( Materials and methods ) . The fitting algorithm compares experimental and numerical embryo shapes to obtain values of the 15 parameters described above for each stage of inversion . In the model , we impose a larger extent of the phialopore than in the biological system , in which the phialopore is initially very small ( Fig 4A ) . This is an important simplification to deal with the discrete nature of the few cells that meet up at the phialopore . In spite of this simplification , the model captures the various stages of inversion ( Fig 7 ) ; fitted numerical values of the 15 parameters are given in S1 Data . This supports our interpretation of the observed cell shape changes ( Fig 6B and 6C , Fig 2 ) and their functions . At this stage , we are finally set up to relate the spatial structure of the shape variations to the mechanisms and mechanics of inversion . This structure of the local shape variations results from variations of the underlying cell shape changes , via the mechanics of inversion , and from geometric effects associated with averaging the shapes . Some of the structure observed in Fig 4 is clearly geometric: since the shapes are aligned so that the positions of their centres of mass along the axis coincide ( Materials and methods ) , the shape variations accumulate and are thus expected to , e . g . , increase in the anterior hemisphere , towards the phialopore , as at the stage in Fig 4C . At the same stage , however , the shape variation is smaller in the bend region than in the adjacent anterior fold . Both of these regions are , however , close to the centre of mass , and so we do not expect this difference to arise from mere geometric accumulation of shape variations . We must therefore ask: can this global structure arise purely mechanically ( i . e . , from a uniform variability of the local parameters implementing the changes of intrinsic curvatures and stretches so that each parameter varies by the same relative amount ) , but possibly as a statistical fluke , or must there be some regulation ( i . e . , nonuniform variability of these local intrinsic parameters ) ?
Our answers to the questions of developmental regulation that we have raised here in the context of Volvox inversion have so far been either negative ( i . e . , ruling out certain mechanisms of regulation ) or of what one might term the Occam's razor variety ( i . e . , invoking the law of parsimony to find the simplest modification of the model that can explain the observations ) . This approach of testing falsifiable hypotheses [68] has the advantage of mitigating the risk of drawing teleological conclusions . However , a fuller answer to the questions above requires estimation of the variability of all the model parameters from the experimental data . Solving this full inverse problem would provide a firmer grip on the relatively large number of fitting parameters required to reproduce the experimental observations , yet that endeavour entails significant statistical , computational , and experimental difficulties: to quantify the full range of variability , one would need a very much larger number of experimental samples to estimate the experimental distribution; additionally , for each step of the optimisation algorithm used to estimate the large number of variability parameters , a large number of computational samples would have to be computed to estimate the distribution under the model . Similar difficulties arise when estimating the variability allowed mechanically . While we have previously noted [56] that the dynamic data for type-B inversion suggest that invagination proceeds without a 'snap-through' bifurcation , there is no general requirement for individual developmental paths to lie on one and the same side of a mechanical bifurcation boundary . This poses an additional challenge for modelling approaches . Coupling the deformations described by the elastic model to the signalling processes that underlie its intrinsic deformations is a further challenge for these models . Feedback loops relating the diffusion of contractility-inducing 'mechanogens' that are degraded by the resulting strain have been studied theoretically [69] and were coupled to a differential-tension model of a discrete epithelium [70] in more recent work [71] . After this discussion of general challenges for a mechanobiological analysis of morphogenesis and its regulation , we mention some of the remaining questions specific to Volvox inversion: it remains unclear what triggers the initial cell shape changes , what determines their location , and what kind of signal drives the propagation of waves of cell shape changes . It seems likely that the cytoplasmic bridges play a role in chemical or mechanical signal transduction . It is curious that inversion starts at the equator in type-B inversion but starts at the phialopore in type-A inversion . It is not known whether there are patterning mechanisms in Volvox that predetermine the spatial distribution of specific cell shape changes . It is unlikely that morphogens known from animals are conserved in Volvox , but plant hormones have been suggested to act as morphogens in photosynthetic organisms [72] . Alternatively , the position of the bend region could be determined by mechanical and/or chemical cues right at the start of inversion . Interestingly , inversion is preceded by temporary local dents in both embryo hemispheres [44] . One could speculate that this 'denting' plays a role in determining the location of the bend region . Once triggered , a wave of cell shape changes could be propagated either by mechano-sensing and/or a chemical signal . Calcium waves , for example , are known to play a role in plant development and can be triggered by mechanical stimuli [73] . In Chlamydomonas reinhardtii , a close relative of Volvox , calcium signalling plays a role , for instance , in the flagellar response [74] . Moreover , in the type-A inverter V . carteri , cells mechanically released from preinversion embryos undergo shape changes prematurely [38 , 45] , which could either indicate the absence of a chemical repressor [31] or that the act of isolating the cells serves itself as a mechanical cue . Combined molecular and physical approaches will be needed to address these questions . Moreover , our model does not resolve the details of the phialopore and hence does not describe the closure of the phialopore at the end of inversion , which remains a combined challenge for experiment and theory: as discussed above , the cytoplasmic bridges elongate drastically at the phialopore [44] , and confocal imaging has revealed the possibility of rearrangements within the cell sheet at the phialopore . Do some cytoplasmic bridges rend to make such rearrangements possible , or are some cells next to phialopore not connected to all of their neighbour , as in type-A inversion ? Understanding the details of the opening of the phialopore may also require answering more fundamental questions , the answer to which has remained elusive [38 , 54]: what subcellular structures apart from endoplasmic reticulum [75] are located within the cytoplasmic bridges ? How is it possible for them to stretch to such an extent ? At the theoretical level , rearrangements of cells near the phialopore raise more fundamental questions of morphoelasticity [57]: in particular , how does one describe the evolution of the boundary of the manifold underlying the elastic description ? Cytoplasmic bridges rending next to the phialopore would lead to the formation of lips similar to those seen in type-A inversion [45 , 46] . Is there a simple theory to describe the elasticity of this nonaxisymmetric setup ? We note in passing that the curling of the membranes of red blood cells upon malaria parasite egress [76] leads to shape changes qualitatively similar to the curling of the lips during type-A inversion ( albeit at very different scales ) . These shape changes have been described theoretically by intrinsic membrane curvature [77 , 78] . At the close of this discussion , it is meet to briefly dwell on questions of more evolutionary flavour: all genera of Volvocaceae and its sister group Goniaceae—with the exception of the single genus Astrephomene [79]—display some form of inversion [42] . There is a general trend among these genera for complexity of inversion to increase with cell number , enabling comparative studies of the evolution of this complexity [49] . The simplest inversion occurs in Gonium [48]: as cells uniformly change their shape , the initially bowl-shaped , convex embryos become concave . Increases of this complexity may appear in different guises: certain cell shape changes may arise only in part of the cell sheet , as in Pleodorina [49] , or cell shape changes may proceed in a wave , as exemplified by type-A inversion in Volvox [45] . The separate regulation of different processes and heterochrony in type-B inversion described here may be a prototype of an additional trait of the evolution of multicellularity that can be studied in the volvocine algae: the transition between cell sheet deformations driven by a single process and those resulting from two separate processes . This complements the similarly prototypical transition from organisms with one cell type to organisms with two cell types associated with germ-soma differentiation in the volvocine algae [80] . The question how the different species of the polyphyletic genus Volvox [81] evolved different ways of turning themselves inside out remains , however . Phylogenetic studies of the volvocine algae show that different inversion types evolved several times independently in different lineages [46 , 82] . Additionally , Pocock [83] reported that in V . rousseletii and V . capensis , inversion type depends on the ( sexual or asexual ) reproduction mode . This may be a manifestation of the poorly understood role of environmental and evolutionary cues in morphogenesis [84] , but such cues remain subject to the mechanical constraints on the respective tissue .
Wild-type strain V . globator Linné ( SAG 199 . 80 ) was obtained from the Culture Collection of Algae at the University of Göttingen , Germany [85] , and cultured as previously described [86] with a cycle of 16 h light at 24°C and 8 h dark at 22°C . From the traced cell sheet outlines , anterior–posterior axes of the embryos were determined as follows: for shapes for which the bend region was visible on either side of the cross-section , the embryo axis was defined to be the line through the centre of mass of the shape that is perpendicular to the common tangent to the two bend regions ( the apex line ) . Shapes were then rotated and translated manually so that their axes coincided . Since embryos do not rotate much before the flagella grow , the orientation of the axes of the earliest traces ( for which the bend regions are not apparent ) were taken to be the same as that of the earliest timepoint for which two bend regions were visible . The intersection of the embryo trace and axis defines the posterior pole . After manually recentring some embryos with more pronounced asymmetry , embryos were halved to obtain N = 22 embryo halves . We consider a spherical shell of radius R and uniform thickness h ≪ R ( Fig 16A ) , characterised by its arclength s and distance from the axis of revolution ρ ( s ) , to which correspond arclength S ( s ) and distance from the axis of revolution r ( s ) in the axisymmetric deformed configuration ( Fig 16B ) . We define the meridional and circumferential stretches fs ( s ) =dSds , fϕ ( s ) =r ( s ) ρ ( s ) . ( 6 ) The position vector of a point on the midsurface of the deformed shell is thus r ( s , ϕ ) =r ( s ) ur ( ϕ ) +z ( s ) uz , ( 7 ) in a right-handed set of axes ( ur , uϕ , uz ) , and so the tangent vectors to the deformed midsurface are es=r′ur+z′uz , eϕ=ruϕ , ( 8 ) where dashes denote differentiation with respect to s . By definition , r′2+z′2=fs2 , and so we may write r′=fscosβ , z′=fssinβ . ( 9 ) Hence , the normal to the deformed midsurface is n=r′uz−z′urfs=cosβuz−sinβur . ( 10 ) We now make the Kirchhoff 'hypothesis' [58] that the normals to the undeformed midsurface remain normal to the deformed midsurface ( Fig 16C ) . Taking a coordinate ζ across the thickness h of the undeformed shell , the position vector of a general point in the shell is r ( s , ϕ , ζ ) =rur+zuz+ζn= ( r−ζsinβ ) ur+ ( z+ζcosβ ) uz . ( 11 ) The tangent vectors to the shell are thus es=fs ( 1−κsζ ) ( cosβur+sinβuz ) , eϕ=ρfϕ ( 1−κϕζ ) uϕ , ( 12 ) where κs = β′/fs and κϕ = sin β/r are the curvatures of the deformed midsurface . The metric of the deformed shell under the Kirchhoff hypothesis accordingly takes the form dr2=fs2 ( 1−κsζ ) 2ds2+fϕ2 ( 1−κϕζ ) 2ρ2dϕ2 . ( 13 ) The geometric and intrinsic deformation gradient tensors are thus Fg= ( fs ( 1−κsζ ) 00fϕ ( 1−κϕζ ) ) , F0= ( fs0 ( 1−κs0ζ ) 00fϕ0 ( 1−κϕ0ζ ) ) , ( 14 ) where fs0 , fϕ0 and κs0 , κϕ0 are the intrinsic stretches and curvatures of the shell . Thence , invoking the standard multiplicative decomposition of morphoelasticity [57] , the elastic deformation gradient tensor is F=Fg ( F0 ) −1= ( fs ( 1−κsζ ) fs0 ( 1−κs0ζ ) 00fϕ ( 1−κϕζ ) fϕ0 ( 1−κϕ0ζ ) ) . ( 15 ) While we do not make any assumption about the geometric or intrinsic strains derived from Fg and F0 , respectively , we assume that the elastic strains derived from F remain small; we may thus approximate εss≈fs ( 1−κsζ ) fs0 ( 1−κs0ζ ) −1 , εϕϕ≈fs ( 1−κsζ ) fs0 ( 1−κs0ζ ) −1 , ( 16 ) with the off-diagonal elements vanishing , εsϕ = εϕs = 0 . For a hookean material with elastic modulus E and Poisson's ratio ν [58 , 59] , the elastic energy density ( per unit extent in the meridional direction ) is found by integrating across the thickness of the shell: E2πρ=E2 ( 1−ν2 ) ∫−h/2h/2 ( εss2+εϕϕ2+2νεssεϕϕ ) dζ=Eh2 ( 1−ν2 ) { ( 1+h24 ( κs0 ) 2 ) Es2+ ( 1+h24 ( κϕ0 ) 2 ) Eϕ2+2ν ( 1+h212 ( ( κs0 ) 2+κs0κϕ0+ ( κϕ0 ) 2 ) ) EsEϕ}+Eh324 ( 1−ν2 ) {Ks2+Kϕ2+2νKsKϕ−4κs0EsKs−4κϕ0EϕKϕ−2ν ( κs0+κϕ0 ) ( EϕKs+EsKϕ ) } , ( 17 ) where we have expanded the energy up to third order in the thickness and where we have defined the shell strains and curvature strains Es=fs−fs0fs0 , Eϕ=fϕ−fϕ0fϕ0 , Ks=fsκs−fs0κs0fs0 , Kϕ=fϕκϕ−fϕ0κϕ0fϕ0 . ( 18 ) As in our previous work [43 , 56] , the elastic modulus is an overall constant that ensures that E has units of energy but does not otherwise affect the shapes . This property of the model enables us to neglect global variations of the elastic modulus between different embryos . We make the additional assumption that the elastic modulus does not vary locally within embryos . We shall also assume that ν = 1/2 for incompressible biological material; the cell size measurements of [62] for type-A inversion in V . carteri support this assumption qualitatively . ( These considerations also explain why we do not perturb these mechanical parameters in our analysis of the shape variations . ) We finally set h/R = 0 . 15 as in our previous work . For the purpose of fitting the model to the observed average shapes , we fit values of the 15 parameters f1 , … , f5 , κ1 , … , κ5 , s1 , … , s5 defined in Fig 6D–6F . The other geometrical parameter of the shell , the angular extent P of the phialopore , is not fitted for . We arbitrarily set P = 0 . 3 . The reasons for this simplification are discussed in the main text . We do not fit either for the distance Δs over which we regularise the functional form of κϕ0 ( Fig 6F ) , since we lack information about the cell shape changes that define it . We arbitrarily set Δs = 0 . 05 . Numerical shapes were fitted to the average shapes by distributing M = 100 points uniformly along the arclength of the numerical and average shapes and minimising a euclidean distance between them using the Matlab ( The MathWorks ) routine fminsearch , modified to incorporate the variant of the Nelder–Mead algorithm of [92] as well as a modified shrinking step of the Nelder–Mead simplex . A custom-written adaptive stepper was used to move about in parameter space and select the initial guess for the Nelder–Mead simplex . For each shape , the fit for the previous stage of inversion was used as the initial guess for the optimisation . Sample code is given in S2 Code . To define perturbations for the F = 15 fitted model parameters P0 ∈ ℝF at noise level δ , we draw independent N uniform random samples X∼U[0 , 1]F on the unit interval and define the perturbed parameters P = P0 ( 1 − δ + 2δX ) by pointwise multiplication . | Biological noise is unavoidable in—and even necessary for—development . Here , we ask whether this variability can teach us something about the process that underlies it . We show how to access the information hidden in the variability in an analysis of the variability of cell sheet folding in the green alga Volvox globator . Through a combination of light sheet microscopy and mathematical modelling , we show how the inversion process , by which the spherical embryos of Volvox turn themselves inside out , results from two separate mechanisms of bending and stretching ( expansion and subsequent contraction ) . Our analysis therefore uncovers a prototypical transition of developmental complexity in Volvox and the related volvocine algae , from a morphogenetic process driven by a single mechanism to one driven by two separate mechanisms . This complements the similarly prototypical transition from one cell type to two cell types that has made the volvocine algae a model system for the evolution of multicellularity . | [
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] | 2018 | The noisy basis of morphogenesis: Mechanisms and mechanics of cell sheet folding inferred from developmental variability |
Trypanosoma brucei is a protozoan parasite that is used as a model organism to study such biological phenomena as gene expression , protein trafficking , and cytoskeletal biogenesis . In T . brucei , endocytosis and exocytosis occur exclusively through a sequestered organelle called the flagellar pocket ( FP ) , an invagination of the pellicular membrane . The pocket is the sole site for specific receptors thus maintaining them inaccessible to components of the innate immune system of the mammalian host . The FP is also responsible for the sorting of protective parasite glycoproteins targeted to , or recycling from , the pellicular membrane , and for the removal of host antibodies from the cell surface . Here , we describe the first characterisation of a flagellar pocket cytoskeletal protein , BILBO1 . BILBO1 functions to form a cytoskeleton framework upon which the FP is made and which is also required and essential for FP biogenesis and cell survival . Remarkably , RNA interference ( RNAi ) -mediated ablation of BILBO1 in insect procyclic-form parasites prevents FP biogenesis and induces vesicle accumulation , Golgi swelling , the aberrant repositioning of the new flagellum , and cell death . Cultured bloodstream-form parasites are also nonviable when subjected to BILBO1 RNAi . These results provide the first molecular evidence for cytoskeletally mediated FP biogenesis .
Endocytosis and exocytosis in trypanosomes is performed by the flagellar pocket ( FP ) , an important organelle that is sequestered within the cytoplasm of the posterior region of the cell . On the basis of its protein composition , the FP membrane is biochemically distinct from the flagellar or pellicular membranes [1–3] and is also required for the molecular trafficking and recycling of glycosylphosphatidylinositol ( GPI ) -anchored proteins such as procyclin and variable surface glycoproteins ( VSG ) . Both procyclin and VSG are surface coat proteins that are trafficked and recycled from the cytoplasm via the FP to the cell surface , where they function in the survival strategies of the cell . The molecular processes involved in these trafficking events are complex and require clathrin , actin , and a number of important GTPase Rab proteins [4 , 5] . An additional important feature of the FP is that it is physically linked to the cytoskeleton . This linkage can be observed in two areas of the pocket: ( 1 ) the axoneme of the flagellum traverses the FP prior to exiting the cytoplasm , and ( 2 ) the neck of the FP originates from a cytoskeletal structure that appears to be attached to the flagellum [6] . Similar to the sequence of events observed in kinetoplast segregation , the segregation of the new FP is precisely temporally and spatially coordinated , which implies that this process maybe mediated by the flagellum [7] . The site of FP biogenesis may also be mediated by the flagellar axoneme , as is observed for the Golgi apparatus of T . brucei [8] . The existence of a FP–cytoskeleton linkage would therefore explain the exquisite precision in positioning and segregation of the FP; namely that , the FP is always located on the proximal , cytoplasmic portion of the flagellum axoneme , and FP segregation is tightly coordinated with the flagellum biogenesis and segregation cycle . The formation of a new flagellum is tightly associated with the maturation and elongation of the probasal body that is associated with the old flagellum [6] . The new axoneme then traverses the luminal core of the FP and exits the cell at a constricted site called the flagellar pocket collar ( FPC ) [9 , 10] . Although a number of proteins have been characterised to be specific to the FP , most are not essential , and none function in FP biogenesis [11–17] . For example clathrin and the proteins needed for receptor-mediated endocytosis are sequestered to the FP but are never exposed to the cell surface [2] . To date , data on FP organisation have been extremely limited and mainly based on ultrastructural studies . No proteins of the FPC have been identified , which is surprising in that the FP has the important role of controlling the targeting of molecules to and from the cell surface to avoid the host immune system [18–22] . Furthermore , endocytosis via the FP is not only used for the trafficking of parasite-derived molecules , but is used in the clearance of host antibodies bound to the cell surface [22–26] . In this study , we describe the identification and characterisation of the first cytoskeletal flagellar pocket protein: BILBO1 . BILBO1 is a component of a cytoskeletal framework that is essential for biogenesis of the FPC . RNA interference ( RNAi ) ablation of BILBO1 in cultured procyclic insect forms ( PF ) of T . brucei prevents FP biogenesis , thus disturbing endocytotic activity and inducing vesicle accumulation , Golgi swelling , gross repositioning of the new flagellum , and cell death . Furthermore , cultured bloodstream forms ( BSF ) are not viable when subjected to BILBO1 RNAi in vitro . BILBO1 , therefore , provides an interface between the cytoskeleton and the endocytotic and exocytotic systems , and represents the first molecular component of the FPC to be identified .
T . brucei has proven to be an excellent model for the study of cytoskeletal biogenesis [10 , 27] . T . brucei has several single-copy organelles , such as the mitochondrion , the kinetoplast ( mitochondrial genome ) , a single Golgi apparatus , and a single FP . The endocytotic and exocytotic activity is limited to the posterior region of the cell via the FP . Figure 1A illustrates the morphology of a PF cell and shows the overall position of the FP within the cell . In PF cells , the FP is always closely associated with the flagellum , and both structures are always located in the posterior of the cell . To attempt to characterise minor but essential proteins in the flagellum of T . brucei , salt-extracted flagellar proteins were separated by polyacrylamide gel electrophoresis ( PAGE ) , and slices of the gel were used to immunise mice . Polyclonal serum obtained from these mice was then used to probe for novel proteins in immunofluorescence and western blotting studies . Proteins that appeared novel by immunofluorescence analysis were further investigated and eventually identified by mass spectrometry . From these studies , we identified a novel 67 . 3-kDa flagellar protein that we named BILBO1 . A single-copy gene , located on chromosome 11 of the T . brucei genome , encodes the BILBO1 protein . BLAST analysis , using parasite GeneDB databases , identified eight orthologs of BILBO1: one in T . brucei gambiense , one in T . congolense , two in T . cruzi ( the South American trypanosome ) , one in T . vivax , one in Leishmania major , one in L . infantum , and one in L . braziliensis . With the exception of T . congolense , these genes have very similar locations with regard to their respective flanking genes , indicating that BILBO1 gene synteny is preserved amongst these species . BLAST analysis of the genes of non-kinetoplastid organisms lacking a FP did not identify any other homologs to BILBO1 . The primary and secondary structures of BILBO1 do not predict any localisation or cytoskeletal functions; however , this protein does possess two putative EF-hand calcium-binding motifs ( amino acids [aa] 185–213 and 221–249 ) , suggesting the existence of calcium binding sites and possible roles in regulation . The large C-terminus coiled-coil domain ( aa 263–566 ) signifies a role in oligomerisation or protein–protein interactions . In vivo overexpression of enhanced green fluorescent protein ( eGFP ) -tagged BILBO1 ( Figure S1A ) in PF cells localised the protein to the FPC ( Figure 1B ) . In addition to the eGFP labelling experiments , we also raised antiserum to recombinant BILBO1 protein or peptides . Immunoelectron microscopy and immunofluorescence studies on PF cytoskeletons confirmed the eGFP-tagged BILBO1 localisation data ( Figure 1C–1G ) . Identical immunofluorescence FPC–FP localisation was observed on BSF cytoskeletons ( Figure S1B ) . The anti-BILBO1 immunogold labelling observed in Figure 1C forms a horseshoe structure that is oriented around the emerging axoneme . No label is observed directly on the axoneme , suggesting that BILBO1 is present on the cell body side of the FPC as opposed to the flagellum side . However , when cytoskeletons are treated with 1 M NaCl , little or no BILBO1 protein is extracted , and the BILBO1 signal remains associated with the flagella preparation ( Figure S1C ) . This indicates that , although BILBO1 is located on the cell side of the flagellum , the FPC and the BILBO1 protein both remain tightly linked to the flagellum . This observation is further supported by the presence of BILBO1 protein in the T . brucei flagella proteome [28] . In order to understand the biogenesis of the FPC , we examined cells in different cell-cycle stages labelled with the anti-BILBO1 antiserum . We observed that early in the kinetoplast S phase ( as observed by kinetoplast DAPI staining ) , the old maternal FPC elongates and grows along its long principal axis , followed by a complete constriction of the short principal axis , thus forming two FPC structures ( Figure 1E and 1F ) . During kinetoplast S phase , one of the FPC structures is moved towards the cell posterior along with new flagellum migration ( Figure 1F and 1G ) . Figure 1F illustrates that division and segregation of the FPC occurs before kinetoplast S phase is completed . The new FPC appears to be segregated simultaneously with the new flagellum . In wild-type ( WT ) cells , new flagellum segregation is accomplished by a subpellicular microtubule-mediated mechanism , which moves only the new flagellum towards the posterior end of the cell [7] . Since the FP is always physically linked to a flagellum , the simultaneous separation of the new flagellum and FP suggests that they may both be segregated by the same microtubule-mediated mechanism . Taken together , these data indicate that the mother FPC participates in daughter FPC biogenesis . We used the tetracycline-inducible RNAi system to assess the function of BILBO1 in PF cells and BSF cells [29 , 30] . Cell growth was arrested in the PF cells after 24 h of induction , followed by cell death ( as judged by a reduction in cell numbers over time ) after 48–72 h of induction . In induced BSF cells , cell death , ( as judged by a reduction in cell numbers over time ) , began after approximately 24 h of induction ( Figure S1G and S1H ) . Note that western blot studies show that BILBO1 protein was not completely depleted in PF cells at 72 h after induction ( Figure 2A ) . Densitometry data of PF cells indicate that at 24 h of induction , BILBO1 protein levels had dropped to 44 . 2% of parental levels and to 27 . 5% and 19 . 6% at 48 h and 72 h , respectively . When we observed PF cells by immunofluorescence after 36 h of BILBO1 RNAi induction , the BILBO1 signal was weak and only detectable on the mother FPC ( unpublished data ) . During BILBO1 RNAi induction , we observed that PF cells were elongated and supported new motile flagella but displayed an aberrant flagellum–cell body attachment . New flagella were attached to the cell body only through the basal body and were relocated to the distal portion of the aberrantly elongated posterior end of the cell . Antibody labelling of the basal body or paraflagellar rod ( PFR ) ( a flagellar structure required for flagellar motility ) indicated that the new flagellum was positive for the basal body and PFR proteins of these structures . The new flagellum was also closely associated with a new kinetoplast , as observed by immunofluorescence and DAPI staining ( Figure 2B ) . Thin-section transmission electron microscopy observation of induced cells illustrated the astonishing finding that the FPs of these cells were not duplicated . Thus , no new FP were formed at the site of new flagellum growth ( Figure 2D and 2E ) . However , the kinetoplast had duplicated and remained attached to , and was segregated by , the basal bodies of the new flagellum ( Figure 2B , 2D , and 2E ) . Taken together , these data indicate that ( 1 ) the formation of the FP requires BILBO1 protein and ( 2 ) that a reduction of BILBO1 protein levels by approximately 50% prevents FP formation and leads to cell death . The image shown in Figure 2C illustrates a WT PF cell longitudinally sectioned at the FP level . This image clearly illustrates that the transition zone of the mature basal body ( as shown by the arrowhead in the figure ) is positioned within the FP lumen [31 , 32] and the PFR originates at the point where the axoneme exits the pocket [6 , 29] . In BILBO1 RNAi-induced cells , however , flagellum-to-cell body attachment has been disrupted , and the new basal body and transition zone are external to the cell body ( Figure 2B and 2E ) . As with control cells , the origin of the new PFR in induced cells is also distal to the transition zone . Because the new flagellum of induced cells is attached to the cell only through the basal body region , this observation suggests that the axoneme itself contains the information necessary for determining where the PFR originates , as opposed to a signal or marker derived from attachment to the cell body . A higher magnification image of the basal body region of an induced cell ( Figure 2E ) illustrates the absence of a FP but also that the kinetoplast remains associated with , and segregated by , the basal body; it also shows the abnormal presence of microtubules in the cytoplasm at the proximal end of the basal body . The electron-dense material corresponding to the FPC at the exit site of the flagellum is clearly visible in noninduced cells but is not visible at the exit site of the new flagellum in induced cells , supporting the perception that BILBO1 RNAi cells do not form a new FP or a FPC ( Figure 2D and 2E ) . Overexpression of nontagged BILBO1 in PF cells did not produce any obvious aberrant phenotypes other than a slight delay in growth rate compared to WT cells ( unpublished data ) . Immunofluorescence studies on cells overexpressing BILBO1 showed that the protein localised to both the mother and daughter FPCs ( unpublished data ) . Intriguingly , overexpression of amino or carboxyl terminal eGFP-tagged BILBO1 for 24 h induced a large accumulation of the tagged protein at the mother FPC . Longer induction of eGFP-tagged BILBO1 ( 48 h ) induced growth arrest ( unpublished data ) . As with the RNAi cells , induction of eGFP-tagged BILBO1 produced cells with new motile flagella that were relocated to an aberrantly elongated posterior portion of the cell . These new flagella were not associated with any BILBO1 immunofluorescence signal or BILBO1-eGFP fluorescence signal , suggesting that no new FPC was formed . Induction of eGFP-tagged BILBO1 also initiated a disruption of new flagella-to-cell body attachment ( unpublished data ) . Thin-section electron microscope images of these cells showed that they had accumulated abnormally large numbers of cytoplasmic vesicles , indicating that they had considerable endo- and exocytotic defects ( unpublished data ) . These cells displayed similar phenotypes to the RNAi-induced cells described previously thus we propose that lethality is not due to overexpression of BILBO1-eGFP per se , but rather due to the inhibition of BILBO1 function via a dominant-negative effect . Cell counts using DAPI-stained PF cells ( Figure 3A ) indicated that the ratio of cells with two kinetoplasts and two nuclei ( 2K2N ) increased from 11 . 73% ( standard error [SE] ± 0 . 63% , n = 1 , 542 ) in the nontransformed parental cell line to 24 . 41% ( SE ± 5 . 73% , n = 811 ) in induced cells after 36 h of induction . We also detected a decrease in the population of cells with one kinetoplast and one nucleus ( 1K1N ) from 70 . 24% ( SE ± 2 . 22% , n = 1 , 542 ) in the parental cell line to 42 . 62% ( SE ± 4 . 33% , n = 811 ) in induced cells after 36-h induction . Interestingly , only 3 . 57 ± 1 . 43% of the population were multinucleated , as compared to 1 . 46% ± 0 . 7 in the noninduced cells , indicating that induced cells do not continue through mitosis but instead undergo a cell-cycle block at the 2K2N stage ( Figure 3A ) . Within the induced 2K2N population ( Figure 3B ) , 60 . 06% ( SE ± 2 . 76% , n = 535 ) of cells possessed an elongated posterior end . Furthermore , 91 . 04% of induced 2K2N cells had the mispositioned flagellar phenotype , 8 . 96% of noninduced cells had mispositioned flagella , whereas 3 . 36% of WT cells had this phenotype . In all BILBO1 RNAi-induced cells , mispositioned new flagella always maintained a disrupted flagellum-to-cell body attachment . Five distinctive 2K2N phenotypes were observed in induced PF cells ( Figure 3B ) : ( 1 ) 2K2N cells that appeared normal in kinetoplast and nuclear positioning ( KNKN [8 . 96% SE ± 0 . 82%] ) , ( 2 ) KNKN cells with a disrupted loss of new flagellum–cell body attachment phenotype ( 20 . 56% SE ± 1 . 76% ) , ( 3 ) cells with two kinetoplasts and two nuclei positioned kinetoplast–kinetoplast , nucleus–nucleus ( KKNN ) with a disrupted loss of new flagellum–cell body attachment phenotype ( 9 . 63% SE ± 0 . 63% ) , ( 4 ) elongated KNKN cells ( 18 . 33% SE ± 3 . 01% ) , and ( 5 ) elongated KKNN cells ( 41 . 73% SE ± 2 . 3% ) ( Figure 3B ) . The reason for production of KKNN cells is not clear , but it may be related to where the cell is positioned within its cell cycle ( e . g . , early or late in mitosis ) when new FPC biogenesis is inhibited by RNAi knockdown . Intriguingly , a KKNN organisation is observed in normal WT BSF trypanosomes , thus this organelle arrangement may reflect a modified mechanism of organelle segregation in BSF cells compared to PF cells . Noticeably , in all of the induced cells , the new flagella were shorter than the mother flagella ( Figure 2B ) , suggesting that these cells were also experiencing difficulties in delivery of cargo for construction of the new flagellum . Electron microscopy reveals that induced PF cells possess what appear to be stacks of membranes that resemble a Golgi apparatus . These cells also amass large numbers of vesicles ( Figure S1D and S1F ) . Since Golgi duplication in procyclic T . brucei cells involves Centrin-2 [33] , and Golgi separation in T . brucei is basal body mediated [8] , we wanted to test whether the observed Golgi swelling influenced Golgi duplication or segregation in induced BILBO1 RNAi-elongated cells . BILBO1-induced cells ( 36-h induction ) were therefore probed with anti-GRASP antibody ( Golgi marker ) [33] and viewed by immunofluorescence to observe Golgi duplication and segregation . Similar to previous studies on WT cells , we observed two or more major separate Golgi-positive signals in all induced 2K2N phenotypes ( Figure 4A–4F ) [8 , 33] . The extended posterior portion of induced 2K2N cells varied in length; therefore , we scored Golgi signals of induced cells that were present in the extreme posterior distal half of the extension as “basal body segregation positive” and Golgi signals in the proximal anterior half as “basal body segregation negative . ” In 2K2N WT cells , 98 . 56% ( SE ± 0 . 26% , n = 764 ) were segregation positive , whereas 19 . 98% ( SE ± 3 . 31% , n = 456 ) of induced cells ( 36-h induction ) were segregation positive . These data indicate that in BILBO1 RNAi cells , Golgi duplication is not inhibited , but the basal body-dependent Golgi segregation machinery is disrupted . This latter observation is due , most likely , to malformations observed in the duplicated Golgi that may block the formation of essential components of the segregation machinery , but also probably related to the loss of cytoskeleton organisation and function in the absence of a FPC at the relocated posterior flagellum . In trypanosomes , a cytoskeletal structure , called the flagellum attachment zone ( FAZ ) is thought to be involved in the organisation of the flagellum and cytokinesis . This structure is located in the subpellicular cytoskeleton , where it subtends the flagellum [6 , 34 , 35] . FAZ proteins are required for flagellum attachment , and loss of the FAZ induces both a flagellum-to-cell body detachment and an inhibition of cytokinesis [36 , 37] . The L3B2 monoclonal antibody recognises the cytoplasmic filament of the FAZ in immunofluorescence and in immunoelectron microscopy [34] . We have used the anti-FAZ antibody L3B2 to study the organisation of the FAZ in the context of flagellum positioning in induced cells . Immunofluorescence studies demonstrate that in BILBO1 RNAi-induced PF cells , no new L3B2-positive FAZ filaments are formed , and the FAZ signal observed remains associated only with the old maternal flagellum ( Figure 4G–4I ) . These data illustrate that flagellum and basal body formation are not sufficient for FAZ formation and could imply that the FPC or FP is required for FAZ formation . If this is the case , the absence of the FAZ could induce the absence of normal flagellum-to-cell body attachment . The lack of a FAZ has previously been observed to produce loss of flagellum-to-cell body attachment [36–39] . Alternatively , the absence of FAZ formation could be explained by the fact that new flagella exhibit a rapid flagellum-to-cell body detachment . As the new flagellum of induced cells consistently exhibits a flagellum-to-cell body detachment , we therefore wanted to determine when in the cell cycle does flagellum detachment occur , and does the new flagellum remain associated to the old flagellum while within the FP ? We probed PF cytoskeletons with AB1 , which is a monoclonal antibody that localizes to a protein component of the flagellar connector ( FC ) [40] . The FC is a flagellum–flagellum linkage that is formed during cell division in PF cells [39] . It is present on the distal tip of the new flagellum and is normally tethered at the tip to the lateral aspect of the old flagellum . It is involved in the replication of the helical cell pattern and polarity of trypanosomes . Studies using trypanosome intraflagellar transport ( IFT ) knockdown cells showed that in the absence of a new flagellum , the FC can still migrate along the old flagellum . Therefore , new flagellum–FC attachment is not essential for FC movement and suggests that the FC has a novel motor for movement along the old flagellum [41] . AB1 labelling of noninduced cells showed the normal attachment of the new flagellum to the old flagellum within the FP , a similar observation to those published previously [40] . The immunofluorescence data presented in Figure 5A show a noninduced PF cell that has been probed with AB1 and a monoclonal antibody ( L8C4 ) that targets the PFR . The merged immunofluorescence and phase contrast images of this cell indicate that a short new flagellum has formed , and the AB1 anti-FC staining shows the presence of the FC new-to-old flagellar attachment site . However , no L8C4 signal is observed on this new flagellum , indicating that it is only a few microns long and is located within the FP . As cells progress through the cell cycle ( Figure 5B , 5C , 5E , and 5F ) , the new flagellum emerges from the FP . The distal tip of the new flagellum remains attached to , and moves along , the old flagellum ( Figure 5A–5C ) . BILBO1 RNAi-induced and AB1-probed cells showed that there was a FC-positive signal present on the old flagellum , but this signal remained in the FP ( proximal to the origin of the old PFR signal ) ( Figure 5G–5L ) . Induced non-elongated cells also had formed the FC-positive signal , which , similar to elongated cells , remained in the FP . Based on its short length , and in comparison to WT cells , the new flagellum of an induced cell early in the cell cycle should normally be located within the FP . The example shown in Figure 5M and 5P illustrates that the new flagellum is PFR negative , indicating that it would normally be located within the FP and should be attached to the old flagellum . However , in this case , attachment of the new flagellum did not occur or was transient . The location of this short new flagellum indicates that the new basal body of BILBO1 RNAi-induced cells can “dock” in the proximity of the old pocket in a similar manner to that found in WT cells . Figure 5N and 5Q or Figure 5O and 5R illustrate induced cells with short flagella , but in both cases , they are PFR positive . In all induced cells , however , the new flagellum is never attached to the old flagellum even though the FC has formed . This suggests that attachment did not occur or was not stable enough to maintain the new-to-old flagellar linkage . If FC attachment did occur , it was lost at an early point in the growth stage of the new flagellum . To test whether induced cells were capable of orthodox endocytotic activity , we carried out live PF cell endocytosis analysis using the fixable fluorescent lipophylic dye FM4-64X . WT 2K2N cells showed strong endocytotic activity at the base of both flagella , suggesting endocytosis activity via the old and the new FP ( Figure 6A and 6B ) . In induced cells , we observed no endocytotic activity at the site of the new flagellum but considerable activity at the old FP ( Figure 6C and 6D ) . Additionally , induced cells at the site of the new flagellum were negative for markers of early endocytosis such as clathrin or Rab5A ( Figure S2 ) , illustrating that in the absence of the FPC , and despite the fact that the new flagellum is still formed , no endocytotic activity is associated with this new flagellum . In order to identify perturbations in trafficking and pocket targeting systems , we probed induced cells with ( 1 ) antiserum to the cysteine-rich acidic transmembrane protein ( CRAM ) ( a FP protein of unknown function but postulated to be a lipoprotein receptor ) [42]; ( 2 ) antiserum to procyclin , a major surface coat protein ( GPEET ) expressed in PF cells [43]; and ( 3 ) antiserum to p67 , a lysosomal protein [44] . In all cases , noninduced cells gave localisation signals similar to control cells in work published previously . However , induced cells gave strong vesicle and/or vacuolar labelling patterns ( Figures S3 and S4 ) , and in the case of CRAM , the whole cytoplasm was positive for this protein ( Figure S3E–S3H ) . CRAM localization was also checked by immunoelectron microscopy and confirmed that induced cells rapidly accumulate CRAM-positive vesicular structures ( Figure S1E ) . Western blotting of BILBO1 , clathrin , Rab5A , CRAM , and procyclin ( GPEET ) proteins after BILBO1 RNAi indicated that clathrin and Rab5A levels appear relatively constant , but that CRAM protein levels increased considerably ( unpublished data ) . Preliminary studies on cultured BSF cells show that there is a significant difference between the phenotype seen in PF cells versus that seen in BSF cells following BILBO1 ablation . In BSF cells , the immediate morphological effect observed was the rapid formation of spherical cells . Cells began rounding up as early as 12 h after RNAi induction . No aspects of the BSF cells were elongated after BILBO1 knockdown . This rounding up of induced cells prevented a clear analysis of kinetoplast and nucleus number or organisation ( Figure S5 ) . Furthermore , immunofluorescence labelling with the anti-PFR monoclonal antibody L8C4 showed that induced cells did not have the flagellum-to-cell body detachment phenotype observed in PF cells , and immunofluorescence labelling with the anti-FAZ monoclonal antibody L3B2 showed that in contrast to PF cells , induced cells often possessed two FAZ signals ( unpublished data ) . Together , this indicates that BILBO1 RNAi in BSF cells has very different effects on cytoskeleton function and organisation in comparison to PF cells .
We have identified a novel protein ( BILBO1 ) that is located around the axoneme of T . brucei as it exits the FP . Using a variety of techniques , we have demonstrated that BILBO1 is part of a “horseshoe” or “ring” of a detergent-insoluble cytoskeletal structure known as the flagellar pocket collar ( FPC ) . BILBO1 is the first component of the FPC to be identified and characterised . The FPC is important for the cell because it forms an “adhesion zone” of electron-dense material located between the pellicular , flagellar , and sequestered FP membranes [6 , 18] . New flagellum growth is supported in the absence of new FP construction in the case of RNAi knockdown of BILBO1 , but the deficiency of a new FPC and FP directly or indirectly results in cell death in both insect and bloodstream forms of T . brucei . In T . brucei , it has been demonstrated that FP selectivity exists to retain certain proteins since , for example , the CRAM protein and transferrin receptors are restricted to the FP , whereas procyclin and VSGs are found on the flagellum , FP , and cell surface [14 , 45] . The nature of this selectivity of distribution is unknown , but it may be developmentally regulated or associated with interactions between the FP membrane , the pellicular membrane , and the FPC . Numerous structures that physically link axonemes to the cell body or the cytoskeleton have been identified in lower and higher eukaryotes [46–49] . One interesting example is the “ciliary necklace . ” This structure has been identified in all 9+2 and 9+0 mammalian and invertebrate cilia , but it is not universally found in sperm . The necklace is located at the basal plate of cilia where the axonemal membrane “pinches in” [50] . Additionally , numerous proteins , including centrin , have been identified to be associated with basal bodies/centrioles and the cytoskeleton [51] . However , the molecular nature or function ( s ) of many of these structures remain to be identified [52] . The ciliary necklace-like structure of T . brucei is visible at the transition zone region , between the axoneme and the flagellar membrane [53] , but it does not appear to be associated with the FPC [54] . Furthermore , extensive searches to define necklace proteins in any organism and to characterise their function have been fruitless . A more comprehensive search for these proteins should now be possible since centriole , cilia , and flagella proteomes have been published [28 , 55–58] . Recent evidence illustrates that certain primary cilia can function as sensory organelles that detect changes in fluid flow and initiate gene expression accordingly [52 , 59 , 60] . Notably , some of these primary cilia have structures similar to FPs with some electron-dense material at the exit point of the cilium , similar to the organisation of the FPC . The pocket-like structure is called the axonemal “vesicle” or sheath; it is thought to be Golgi derived and extends along with the growing ciliary axoneme within the cytoplasm [61] . The function of this vesicle is in all probability to provide a distinct , isolated compartment separated from the cytoplasm to allow intraflagellar transport for axonemal elongation . However , the molecular functions of the vesicle of primary cilia are not known . Certain primary cilia can retract if subjected to physiological stress [62] . Membrane that is bound to the proximal region of the primary cilia axoneme is observed when they retract [63] . Presumably , the membrane in these structures is derived from the pellicular membrane , but exactly how this membrane is maintained as a uniform and organised “sack” or “pocket” around the primary cilia remains unresolved . Electron microscopy data suggest that the primary cilia vesicle is also able to carry out endocytotic activity via coated pits [64 , 65] . The presence of pits implies an organisation of membrane and proteins that separate the plasma membrane from the primary cilia vesicle . In this regard , we propose that a structure additional to the necklace and analogous to the FPC of trypanosomes may exist in primary cilia and may be important for positioning of cilia and , possibly , in trafficking processes . In mammals and yeasts , actin and actin-binding proteins are the major cytoskeleton components associated with endo- and exocytosis [66 , 67] . These proteins are essential for the reshaping of the plasma membrane to facilitate endocytosis . They are often found associated with coated pits in the form of transient patches tightly associated with primary endocytotic vesicles . The actin poisons Latrunculin A and Jasplakinolide partially inhibit endocytosis in mammalian cells but initiate a complete endocytotic block in Saccharomyces cerevisiae [66–69] . In trypanosomes , actin has a differential role whereby it is essential and required for the formation and trafficking of endocytotic vesicles in BSF cells of T . brucei . Loss of actin by RNAi in BSF cells prevents endocytosis and results in enlargement of the FP , followed by cell death . In contrast , actin is neither essential nor associated with the FP in procyclic cells [4] . Furthermore , in trypanosomes , actin has not been observed as polymers or bundles , rather it localises to the endocytotic pathway but does not associate with the subpellicular cytoskeleton or the FP , illustrating that it is not a component of the FPC [4] . The FAZ is thought to attach the trypanosome flagellum along the cell body and to coordinate correct cytokinesis [10]; thus , the FAZ plays an important role in the regulation of cell division . In BILBO1 RNAi-induced PF cells , the flagellum-to-cell body attachment of the new flagellum was disrupted and the expected new FAZ was absent , implicating an important relationship between the FAZ and the FPC/FP . The lack of FAZ formation is striking , but consistent with the orientation of the new flagellum being detached from the cell body . Alternatively , the FAZ is absent because the new flagellum ( 1 ) rapidly loses a flagellum–cell body attachment or ( 2 ) never initiates an attachment to the cell body . In either case , the absence of a new FAZ raises interesting questions regarding the control of FAZ formation and its relationship with other structures of the cytoskeleton . A unique feature of the trypanosome cell cycle is that defects in cytokinesis do not necessarily trigger mitosis checkpoints , so that cells become multinucleated when cytokinesis is blocked [70] . However , in BILBO1 RNAi-induced cells , only 3 . 57% were multinucleated , suggesting the stimulation of a true cell-cycle block . This apparent S phase and mitotic block is unlikely to be due completely to the loss of FAZ . Previous studies have shown that interfering with correct FAZ formation , by RNAi knockdown of a FAZ protein called FLA1 , induces flagellar detachment and cytokinesis block , but not mitosis , because induced FLA1 RNAi cells develop a multinucleated phenotype [36] . In BILBO1 RNAi-induced PF cells , the presence of a single FAZ may pose difficult cytokinesis-related problems for the cells . Our studies suggest that the FP or FPC plays a more substantial role in the cell cycle than does the FAZ; however , we are unable to define whether it is the FPC or the FP that initiates this cell cycle block . Even though BILBO1 is expressed in both PF and BSF cells , reduction of expression in PF cells induces the formation of many 2K2N cells that arrest in a phenotype in which basal bodies are located on the posterior side of the two nuclei ( KKNN ) instead of an alternated KNKN conformation . These data raise questions as to whether BILBO1 or the FPC , in interaction with the cytoskeleton , function in controlling cell-shape differentiation . It appears that the new basal body of BILBO1 RNAi-induced cells can “dock” in the proximity of the old pocket in a fashion similar to that found in WT cells , but it then moves away to the extreme posterior end of the elongated cell , possibly because of failure to assemble the FP and FPC . It does not appear to depart from its position next to the old basal body , or migrate into the cytoplasm to an incorrect position , before extending a measurable length of axoneme . The mispositioning of the new flagellum towards the cell posterior and the absence or early loss of flagellum-to-flagellum attachment supports the supposition that the new flagellum grows into the pellicular membrane and/or remnants of the old FP membrane during or early after new axoneme growth is initiated . In these induced cells , the PFR grows in parallel with the new flagellum and is independent of attachment to the cell body , thus indicating that the PFR is dependent on the axoneme for initiation and formation rather than signals from the cell body . Our data also show that new flagellar growth of procyclic cells is autonomous of the FC , or an attachment to the old flagellum . Indeed , new flagella of induced cells rapidly lose or may not establish flagellum-to-flagellum or flagellum-to-cell body attachment early in the cell cycle . The work of Davidge et al . ( 2006 ) [41] showed that new flagellum formation is not essential for FC movement . They also showed that a new FAZ was formed after intraflagellar transport ( IFT ) knockdown inhibited new flagellum growth . Why the FC is limited to the FP after BILBO1 RNAi remains to be determined , but it could be argued that absence of FC movement could be related to the absence of FAZ formation . To date , no proteins of the FC have been identified , thus the dependency relationships between the old and new flagella via the FC cannot as yet be studied in more detail . The extreme posterior localisation of the new flagellum in BILBO1 RNAi-induced cells is intriguing and is observed only in 2K2N cells . This location in the cell cycle is not coincidental; otherwise , one would expect to observe the site of the new flagellum to be distributed randomly on the cell surface and in any cell-cycle stage . One possibility is that the new flagellum may be pushed to the posterior end of the cell by the growth of new or preexisting subpellicular microtubules . This would suggest a loss of control over the polymerisation of the microtubules involved in the posterior extension of the cell during normal division . Why subpellicular microtubules of induced cells elongate to such an extent is also interesting and requires further investigation . Other workers have observed a posterior-end extension in trypanosomes after expression or RNAi knockdown of proteins related to differentiation or control of the cell cycle . Overexpression in PF cells of TbZFP2 , a zinc finger protein implicated in differentiation from BSF to PF cells , causes a “nozzle” phenotype ( a posterior extension of the cytoskeleton ) as well as the occurrence of multinucleated and multiflagellated cells [71] . The authors showed that the nozzle is a result , at the posterior end of the cell , of polarized extension of microtubules rather than interdigitating short microtubules . RNAi depletion in PF cells of the cyclin CYC2 , an essential PHO80-like cyclin , and the cyclin-related kinases CRK1+CRK2 also induced a polarized extension of posterior-end microtubules [72] . In all these studies , the extended or nozzle phenotypes were only observed in 1K1N/2K1N cells ( cells arrested in G1 ) , as opposed to the postmitotic 2K2N cell-cycle stage observed in BILBO1 knockdown PF cells . The absence of an elongated posterior end in BSF BILBO1 RNAi cells is similar to the observations of Hammarton et al . ( 2004 ) [73] in that RNAi of CYC2 induces a nozzle phenotype in PF cells , but not in BSF cells . The reasons for the production of nozzle or extended posterior-end phenotypes are unclear; however , these results clearly indicate that cytoskeleton elongation is heavily influenced by cell-cycle and/or cell-differentiation checkpoints . The FPC remains intact and attached to flagella after detergent and salt extraction . The FPC thus most likely consists of a complex of proteins in addition to BILBO1 , because BILBO1 itself does not appear to have any obvious membrane-targeting domains , but it does have a large coiled-coil domain consistent with protein–protein interactions . One function of the FPC complex is to physically link the flagellum to the neck of the FP and the cell body . More precisely , this link would produce an intimate bridge between the FP membrane , pellicular membrane , and the flagellum membrane . This bridge complex forms a barrier or an adherens junction-like plaque between the flagellum and the subpellicular cytoskeleton . It is well documented that the trypanosome axoneme exits the FP via the FPC , but little data have been published on the organisation of this structure . It follows that structural homologs are likely to be present in many organisms in order to define and localise the exit site of cilia or axonemes . A schematic diagram of the positioning of the FPC and its role in noninduced or induced cells is shown in Figure 7 . This figure also illustrates the distribution and organisation of organelles before and after BILBO1 RNAi knockdown . BILBO1 RNAi-induced cells arrest and die before all BILBO1 protein is lost from the old FPC . The new flagellum of induced cells does not remain attached to the old flagellum via the FC , even within the old mature FP . Surprisingly , the membrane at the base of the new flagellum of induced cells is not capable of carrying out endocytotic function ( as demonstrated using FM4-64FX uptake ) ; however , we should consider the possibility that activity could be below the level of detection using this fluorescence-based assay . Nevertheless , with only one functional FP , BILBO1 RNAi-induced cells appear stressed in the sense that this single FP must function for two FPs; this probably induces an endocytotic imbalance . Loss of the FPC appears to disrupt all components of the endocytotic pathway as observed by electron microscopy or via endocytotic and lysosomal markers . Exocytosis is possibly disrupted also , which raises questions regarding the ability and consequences of these cell types to carry out procyclin , VSG , or invariant surface glycoprotein ( ISG ) trafficking in insect form or BSF cells . In summary , FP biogenesis , endocytotic activity , flagellar positioning , and cell division all have strict dependency relationships with the FPC in PF trypanosomes . The discovery of BILBO1 and identification of its partners will facilitate studies on the trafficking of surface proteins involved in parasite survival strategies and on FP biogenesis . The identification of nonparasite-specific BILBO1 partner proteins may help to identify generic axoneme and cilia positioning structures .
Genomic DNA of Trypanosoma brucei TREU927/4 GUTat10 . 1 [74] was used to amplify by PCR the BILBO1 ORF . T . brucei procyclic cell line EATRO1125-T7T and BSF line 427 90–13 single marker [75 , 76] were grown and transformed as described in [77] . Transformants were screened by immunofluorescence and cell morphology after tetracycline induction ( 1 μg · ml−1 ) , and cloned . Flagellar proteins were prepared as follows: 1 × 1010 T . brucei EATRO 1125 cells were harvested by centrifugation ( 1 , 000 × g , 20 °C , 10 min ) washed in PBS ( pH 7 . 2 ) , 10 mM EDTA . Cells were lysed in PBS , 2 mM MgCl2 , 0 . 25% NP40 , and protease-inhibitors ( 539134; Calbiochem ) . Genomic DNA and total RNA were digested with 200 U Benzonase . Cytoskeletons were extracted in 1 M NaCl ( final concentration ) and incubated for 10 min on ice . Flagella were harvested ( 30 min , 4 °C , 8 , 422 × g ) , washed in PBS , 2 mM MgCl2 , 36 U Benzonase , washed in PBS and stored in PBS at −80 °C . Flagellar proteins ( 10 mg total ) were preseparated in denaturing conditions ( 5% Ampholines [pH 3–10] , 2% CHAPS , 7 M urea , 2 M thiourea , 50 mM DTT ) on a Bio-Rad Rotofor . Individual fractions were run on SDS-PAGE gels , and protein bands ( 60 kDa to 80 kDa ) were excised and subjected to liquid chromatography–tandem mass spectrometry ( LC/MS/MS ) analysis . The protein sequence of BILBO1 was identified using a signature of ten individual peptide sequences . The corresponding ORF was identified by WU-BLAST on GeneDB database has been deposited at GenBank . The plasmid p3960SL contains a “sense/antisense” cassette targeting a 600-bp fragment of the T . brucei BILBO1 ORF ( nucleotide position 1 to 600 ) in the pLew100 vector [78] and was constructed as follows . A sense fragment of 600 bp was amplified by PCR ( with Taq polymerase ) using the primers HindIII-3960 ( 5′GGTCGCaagcttATGGCGTTTCTCGTACAAGTAGCA3′ ) and 3960–600-XbaI ( 5′CTCAACtctagaCACACGGTTACCCTTTACATCGA3′ ) and cloned into pCR2 . 1-TOPO ( p39–600 ) . A 650-bp antisense fragment was amplified with the primers BamHI-3960 and 3960–650-XbaI ( 5′GTAGCTtctagaAAGTTGAGATTAAACACAGTGAA3′ ) and cloned in the pCR2 . 1-TOPO ( p39–650 ) . After digestion of p3960–600 by HindIII-XbaI , and p3960–650 by BamHI-XbaI , the excised fragments were simultaneously cloned into pLew100 between the HindIII and BamHI sites ( p3960SL ) . For RNAi in BSF , a fragment corresponding to the last 514 bp of BILBO1 ORF was amplified by PCR ( using the primers BamHI-514-3960 5′tcaggatccCAGAGACGCTGATATCGTGAAA3′ and 3960-HindIII 5′GGTCGCaagcttATGGCGTTTCTCGTACAAGTAGCA3′ ) cloned between the HindIII and BamHI sites of the p2T7–177 plasmid [79] . To overexpress in vivo the recombinant BILBO1-eGFP protein , the BILBO1 ORF was amplified by PCR with the primers HindIII-3960 and 3960-NoStop-XbaI ( 5′ATATtctagaATCTCGCGGATAGGACCTC3′ ) and cloned into the pLew79-GFP1 vector [80] to make p3960-GFP . We thank the following researchers for antibodies: K . Gull ( anti-PFR2 , L8C4 , anti-FAZ , L3B2 , anti-FC , AB1 , and anti-basal body , BBA4 ) , M . Field ( anti-Rab5A and anti-Clathrin ) , G . Warren ( anti-GRASP ) , M . Lee ( anti-CRAM ) , J . Bangs ( anti-p67 ) , and I . Roditi ( anti-GPEET procyclin ) . To produce anti-BILBO1 antiserum , two peptides ( H2N-SFPSRPSISELTRSAE-CONH2 and H2N-GSRSPVSHRSESQQAR-CONH2 ) were synthesized , conjugated to carrier protein ( Eurogentec ) , and then injected into rabbits to produce polyclonal antiserum . Additionally , recombinant 6 histidine-BILBO1 protein was overexpressed in bacteria , purified in urea on Ni-NTA resin , and then injected into a mouse which was then used to produce a monoclonal antibody . All antisera , including the monoclonal , were tested by immunofluorescence and western blotting . Whole cells ( 1 × 107 cells ) or cytoskeleton proteins were prepared as described in [77] . Membranes were blocked 1 h in TBS , 0 . 2% Tween-20 , 3% milk ( or TBS , 5% milk for K1 ) , incubated overnight at 4 °C with mouse polyclonal anti-BILBO1 antibody diluted in blocking buffer 1:200 . After washing , anti-CRAM , anti-Clathrin , anti-RAB5A , or anti-GPEET procyclin ( K1 ) , diluted 1:2 , 000 was used as in [43] . Filters were processed as in [77] or [43] . Western blots were scanned at 300 dpi , and densitometry analysis was done using NIH Image 1 . 62 . Whole cells were washed in PBS , then spread on poly- l-lysine–coated slides . For cytoskeleton preparations , cells were extracted with 0 . 25% NP40 in PIPES buffer ( 100 mM PIPES [pH 6 . 9] , 1 mM MgCl2 ) for 5 min , and then washed twice in PIPES buffer . Cells or cytoskeletons were fixed in −20 °C methanol or 3 . 7% paraformaldehyde or 3 . 7% paraformaldehyde with 0 . 025% glutaraldehyde . In the latter case , cells were neutralised for 15 min in 200 mM glycine washed in PBS , blocked in 1%–10% bovine serum albumin ( BSA ) , and probed with anti-FC ( AB1 ) 1:5 , anti-PFR2 ( L8C4 ) neat , anti-basal bodies ( BBA4 ) 1:20 , anti-CRAM 1:250 , anti-GRASP 1:300 , anti-Clathrin 1:250 , anti-RAB5A 1:250 , anti-GPEET procyclin ( K1 ) 1:200 , anti-p67 1: 400 , or anti-BILBO1 rabbit polyclonal 1:500 and anti-BILBO1 monoclonal 1:10 . Slides were processed as in [77] using Jackson , Sigma , or Molecular Probes secondary anti-IgG– or anti-IgM–specific secondary antibodies conjugated to FITC , Oregon Green , or Texas Red . Prior to K1 labelling , cells were permeabilised in 0 . 1% NP40 in PBS for 1 min , and then washed 3 × 10 min in PBS . Prior to Clathrin , RAB5A , and CRAM labelling , cells were permeabilised in 0 . 1% Triton X-100 for 1 or 10 min and blocked with BSA ( 1% or 10% , respectively for 10 min to 1 h ) . Slides were DAPI stained and mounted with Slowfade Lite ( Molecular Probes ) . Images were acquired with Metavue 4 . 4 software , on a Zeiss Axioplan 2 microscope , using a Roper CCD 1300-Y/S digital camera , and processed with Adobe Photoshop 8 . Brightness was reduced on DAPI images when used in merged presentations . Microscopic analysis of FM4-64FX uptake was carried out by a modification of the assay described by Hall et al . , 2005 [81] . A total of 1 × 107 induced ( 36 h ) and noninduced BILBO1 RNAi procyclic cultures were harvested by centrifugation , washed in PBS , and then resuspended in 1 ml of PBS , 0 . 1 mM adenosine , and 10 mM glucose . FM4-64FX was added ( to a final concentration of 2 . 5 μg/ml ) , and the cells were incubated for 15 min in the dark with mild agitation ( 25 rpm ) on a rotary shaker . After incubation , the cells were kept on ice to block endocytotic activity . All following solutions and protocols were done at 4 °C . The cells were washed in PBS , deposited on poly-l-lysine–coated slides for 5 min in the dark , and then fixed for 15 min in the dark with 4% paraformaldehyde in PBS , followed by two 5-min washes in PBS . Finally , the slide was DAPI stained , 10 μg/ml , in PBS for 4 min , washed 2 × 5 min in PBS , mounted , and then viewed at room temperature as for immunofluorescence . A total of 50 ml of mid-log phase WT or 48-h RNAi-induced cells were harvested by centrifugation at 1 , 000 × g for 15 min . Block preparation and protocol was performed exactly as in [77] . A total of 50 ml of RNAi-induced cells ( 36 h ) at 1 × 107/ml were harvested and resuspended in 25 ml of 4% paraformaldehyde , 0 . 025% glutaraldehyde in PBS for 2 h . Fixed cells were washed , dehydrated , and embedded in Lowicryl HM20 mono-step ( EMS ) . Sections were cut , neutralised in 100 mM glycine for 10 min , blocked in PBS 1% BSA for 10 min , and probed with anti-CRAM 1:250 in blocking buffer 4 °C overnight . Sections were washed 4 × 10 min in PBS 1% BSA , then probed with 10-nm gold-conjugated protein A or G ( Aurion ) 1:30 in blocking buffer for 2 h . Sections were washed 4 × 10 min in PBS 1% BSA , then 4 × 10 min PBS , fixed in 1% glutaraldehyde in water for 1 min , stained in 2% uranyl acetate for 15 min , washed 4 × 5 min in water , and then viewed as described in [77] . Cytoskeletons were prepared as above with the following exceptions: extraction was in 25 ml of 1% NP40 in Pipes buffer , washed in Pipes buffer , and resuspended in 25 ml of 4% paraformaldehyde , 0 . 25% glutaraldehyde in PBS for 30 min . Blocks were prepared as above and probed with mouse anti-BILBO1 polyclonal antiserum 1:50 at 4 °C overnight . Sections were washed , probed with 10-nm gold-conjugated anti-mouse 1:30 and processed as above . Standard error was used for cell counts and n = 3 ( 3 different experiments in all cases ) . Sample size ( n ) is indicated as total cell number counted for the three experiments .
The GenBank ( http://www . ncbi . nlm . nih . gov/Genbank ) accession number for BILBO1 is DQ054527 , and the GeneDB ( http://www . genedb . org/ ) accession number is Tb11 . 01 . 3960 . | Trypanosomes are ubiquitous unicellular parasites that infect humans , animals , insects , and plants . African , Asian , and some South American trypanosomes have evolved the amazing ability to change their surface coat proteins , an essential strategy for their survival . The surface coat proteins are recycled and targeted to the surface of the parasite via an endocytic and exocytotic organelle called the flagellar pocket , which is sequestered in the trypanosome cell's cytoplasm . The flagellar pocket is also used to remove host-derived antibodies that are bound to the surface of the parasite , making this organelle critical for the parasite's evasion of the host immune system . We describe a novel protein , “BILBO1 , ” which was identified from the insect-form parasite of the African trypanosome Trypanosoma brucei . We show that BILBO1 is part of a ring or horseshoe-like cytoskeletal structure that is located in a region of the flagellar pocket called the collar . When BILBO1 transcripts were knocked down with inducible RNA interference , trypanosome cells became arrested in a post-mitotic cell-cycle stage . Induced cells lost the normal flagellum-to-cell-body attachment , were unable to regulate endocytosis and exocytosis , and most importantly , were unable to construct a new flagellar pocket . These results provide molecular evidence for the idea that flagellar pocket biogenesis is cytoskeletally mediated . | [
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] | 2008 | Biogenesis of the Trypanosome Endo-Exocytotic Organelle Is Cytoskeleton Mediated |
The neuraminidase ( NA ) inhibitor oseltamivir offers an important immediate option for the control of influenza , and its clinical use has increased substantially during the recent H1N1 pandemic . In view of the high prevalence of oseltamivir-resistant seasonal H1N1 influenza viruses in 2007–2008 , there is an urgent need to characterize the transmissibility and fitness of oseltamivir-resistant H1N1/2009 viruses , although resistant variants have been isolated at a low rate . Here we studied the transmissibility of a closely matched pair of pandemic H1N1/2009 clinical isolates , one oseltamivir-sensitive and one resistant , in the ferret model . The resistant H275Y mutant was derived from a patient on oseltamivir prophylaxis and was the first oseltamivir-resistant isolate of the pandemic virus . Full genome sequencing revealed that the pair of viruses differed only at NA amino acid position 275 . We found that the oseltamivir-resistant H1N1/2009 virus was not transmitted efficiently in ferrets via respiratory droplets ( 0/2 ) , while it retained efficient transmission via direct contact ( 2/2 ) . The sensitive H1N1/2009 virus was efficiently transmitted via both routes ( 2/2 and 1/2 , respectively ) . The wild-type H1N1/2009 and the resistant mutant appeared to cause a similar disease course in ferrets without apparent attenuation of clinical signs . We compared viral fitness within the host by co-infecting a ferret with oseltamivir-sensitive and -resistant H1N1/2009 viruses and found that the resistant virus showed less growth capability ( fitness ) . The NA of the resistant virus showed reduced substrate-binding affinity and catalytic activity in vitro and delayed initial growth in MDCK and MDCK-SIAT1 cells . These findings may in part explain its less efficient transmission . The fact that the oseltamivir-resistant H1N1/2009 virus retained efficient transmission through direct contact underlines the necessity of continuous monitoring of drug resistance and characterization of possible evolving viral proteins during the pandemic .
A novel swine-origin H1N1 influenza virus emerged in Mexico in April 2009 and rapidly spread worldwide , causing the first influenza pandemic of the 21st century [1] , [2] . Most confirmed human cases of H1N1/2009 influenza have been uncomplicated and mild [3] , but the increasing number of cases and affected countries warrant optimal prevention and treatment measures . At present , two classes of antiviral drugs are approved for specific management of influenza: M2-ion channel blockers ( amantadine and rimantadine ) and neuraminidase ( NA ) inhibitors ( zanamivir and oseltamivir ) . However , variants resistant to both classes of drugs have emerged . During the 2007–2008 season , most circulating seasonal H3N2 influenza viruses , and H1N1 viruses in certain geographic areas , were reportedly resistant to M2-blockers [4] , [5]; today , almost all of the pandemic H1N1/2009 viruses tested are resistant to M2-blockers [6] . Therefore , only the NA inhibitors are currently recommended for treatment of influenza [7] . The NA-inhibitor resistance-associated mutations in influenza viruses are drug-specific and NA subtype-specific [8] . Until 2007 , the clinical data indicated only sporadic , rare emergence of oseltamivir resistance under drug selection pressure ( <1% in adults and 4%–8% in children ) [9]–[11] . Later reports observed increased frequency of oseltamivir-resistant variants ( 18% and 27% ) in drug-treated children [11] , [12] . The situation changed dramatically during the 2007–2008 season , when seasonal H1N1 influenza viruses with the common oseltamivir-resistance NA H275Y mutation ( 275 in N1 numbering , 274 in N2 numbering ) became widespread in first the northern [13] and then the southern [14] hemispheres . It remains uncertain where these naturally resistant H1N1 influenza viruses originated and how they acquired optimal fitness and transmissibility , but the resistant variants were clearly becoming the dominant strain at the time the swine-origin pandemic H1N1/2009 virus emerged [15]–[17] . During the H1N1/2009 influenza pandemic , to date , almost all tested viruses have remained susceptible to oseltamivir and zanamivir [6] , but oseltamivir-resistant variants with H275Y NA mutation have been isolated from individuals receiving prophylaxis [18] , [19] and from immunocompromised patients [20] under drug selection pressure . Oseltamivir-resistant variants also have been isolated from untreated patients [21] , [22] and from a few community clusters [23]–[25] , including two suspected cases of nosocomial transmission among immunocompromised patients [23] , [24] , although it is uncertain whether the mutants came from secondary transmission or arose spontaneously . The isolation of resistant H1N1/2009 viruses with no link to oseltamivir use raised serious concern that these viruses might acquire fitness and spread worldwide , as had oseltamivir-resistant seasonal H1N1 viruses during 2007–2008 . The increasing concern about oseltamivir-resistant H1N1/2009 viruses prompted us to evaluate transmissibility and growth fitness of one oseltamivir-resistant variant . The infectivity and transmissibility ( and thus the clinical relevance ) of several NA inhibitor-resistant influenza viruses have previously been studied in experimental animal models [26]–[29] . These studies differed in the influenza A subtypes studied ( H1N1 , H3N2 , or H5N1 ) , the NA mutations involved ( H275Y , R292K , E119V or I222V ) , the animal model used ( ferret or guinea pig ) , and the transmission routes studied ( direct contact and respiratory droplets ) ; in these studies , the transmissibility of most of the NA inhibitor-resistant influenza viruses was to some extent less efficient . Here we characterized in vitro and in a ferret model a pair of pandemic H1N1/2009 clinical isolates . The pandemic A/Denmark/524/09 ( A/DM/524/09 ) and A/Denmark/528/09 ( A/DM/528/09 ) viruses were isolated from a small cluster of patients with H1N1/2009 virus infection [30] . The A/DM/528/09 virus , carrying the H275Y NA mutation , was isolated from a patient on oseltamivir prophylaxis , and its ancestor is likely to have been A/DM/524/09 virus . By recapitulating two natural routes of influenza virus transmission in ferrets , we found that the oseltamivir-resistant virus was less transmissible than its sensitive counterpart through the respiratory droplet route but retained efficient transmission through direct contact .
Sequence analysis of the NA genes revealed that A/DM/524/09 virus encoded a conserved H residue at amino acid position 275 , whereas A/DM/528/09 virus had an H275Y amino acid mutation caused by a single T-to-C nucleotide substitution at codon 275 ( Table 1 ) . Pairwise sequence analysis of the full viral genomes showed that the A/DM/524/09 and A/DM/528/09 viruses had no amino acid differences other than the H275Y NA mutation and were a highly matched pair . Sequence analysis and phylogenetic analysis of the two viruses' NA and HA genes ( data not shown ) confirmed that the wild-type A/DM/524/09 and mutant A/DM/528/09 viruses belonged to the swine-origin 2009 pandemic virus lineage . The alignment of the NA and HA sequences showed that viruses with H275Y NA substitution have some amino acid differences from certain wild-type viruses ( without H275Y NA mutation ) , but these differences also were observed in other wild-type viruses . Comparison of the NA and HA amino acid sequences of A/DM/528/09 virus with sequences of other 24 H275Y mutants and around 2000 wild-type H1N1/2009 viruses available in Gene Bank did not reveal an increased frequency of any specific amino acid mutation ( s ) shared among the viruses analyzed ( data not shown ) . To assess the NA inhibitor susceptibility of the two viruses , we performed NA enzyme inhibition assays with the NA inhibitors oseltamivir carboxylate ( active metabolite of oseltamivir ) and zanamivir . The wild-type A/DM/524/09 virus was susceptible to oseltamivir carboxylate ( mean IC50: 5 . 0 nM ) , but the A/DM/528/09 carrying the H275Y NA mutation had IC50 values approximately 200 times that of the wild-type virus ( Table 1 ) . The IC50 of zanamivir was comparable for both viruses and was uniformly low ( mean IC50≤1 . 3 nM ) ( Table 1 ) . These results showed that the H275Y NA mutation conferred resistance to oseltamivir carboxylate but did not alter susceptibility to zanamivir . To understand the impact of the H275Y mutation on the NA enzymatic properties of the H1N1/2009 viruses , we determined the NA enzyme kinetics of both viruses . Km is an estimate of the dissociation equilibrium for substrate binding to enzyme and the reciprocal of Km approximates the affinity of substrate binding , while Vmax reflects the enzyme's catalytic activity . The NA of resistant A/DM/528/09 virus had a slightly higher Km and lower Vmax than the NA of the sensitive A/DM/524/09 virus ( Table 1 ) . The H275Y NA mutation reduced NA affinity for substrate and NA catalytic activity , although the function of NA was not severely impaired . This finding in the H1N1 pandemic virus is similar to that reported by another group , in which NA enzymatic function was not impaired in some naturally resistant seasonal viruses isolated during the 2007 season [31] . Our study is the first to show reduced but not severely impaired NA enzymatic function in a resistant H1N1/2009 virus with the H275Y mutation . To determine whether the H275Y NA mutation affects virus growth in vitro , we characterized virus plaque morphology and growth kinetics in both MDCK and MDCK-SIAT1 cells . The latter have increased surface expression of human-like α2 , 6-linked terminal sialic acids [32] and may better assess the growth capability of human influenza viruses . In MDCK cells , both pandemic H1N1/2009 viruses formed pinpoint-like ( 0 . 3 mm ) plaque phenotype ( Figure 1A ) , differing significantly from some seasonal H1N1 viruses , such as A/Brisbane/59/2007 ( BR/59/07 ) virus , which formed large plaques ( 1 . 3 mm ) ( P<0 . 05 ) ( data not shown ) ; however , the plaque size did not differ between the oseltamivir-sensitive and -resistant viruses ( Figure 1A ) , indicating that the H275Y NA mutation did not alter plaque morphology . In MDCK-SIAT1 cells , both the pandemic viruses and seasonal BR/59/07 ( data not shown ) formed only pinpoint-like plaques ( Figure 1B ) , consistent with a previous report [32] that this cell line did not generate clear plaques for influenza viruses . To further evaluate the impact of the H275Y NA mutation on virus growth in vitro , we performed single- and multiple-cycle growth studies of both viruses in MDCK and MDCK-SIAT1 cells . In single-cycle growth in the two cell lines , the two viruses reached comparable levels 6 hours post-infection , but the initial growth of the oseltamivir-resistant virus was significantly delayed in comparison to its sensitive counterpart ( P<0 . 05 ) ( Figure 1C ) : at 4 hours post-infection , the yield of resistant viruses was at least 1 log10TCID50/ml lower ( P<0 . 05 ) . Likewise , in multiple-cycle growth , the two viruses reached comparable yields 24 hours post-infection , but the resistant virus showed a significant growth delay during the first 12 hours post-infection ( P<0 . 05 ) ; this delay was more conspicuous in MDCK-SIAT1 cells than in MDCK cells ( Figure 1D ) , probably because overexpressed α2 , 6 receptors on cell surface could better differentiate NA's function in support of viral growth . Therefore , final virus yields of oseltamivir-resistant pandemic virus in the MDCK and MDCK-SIAT1 cells were not altered , but their growth at the initial infection stage was significantly delayed . The transmissibility of pandemic H1N1/2009 viruses was studied in a ferret model . Two naïve ferrets were housed at day 2 post-inoculation ( p . i . ) in the same cage with one inoculated ferret ( direct contact ) , and two naïve ferrets were placed in an adjacent cage separated from the donor's cage by two layers of wire mesh ( respiratory droplet exposure ) . Transmission of H1N1 virus was assessed by detection of infection in recipient ferrets ( nasal wash titers , clinical signs , and seroconversion ) . Virus samples in nasal washes at day 4 p . i . or post-contact ( p . c . ) were sequenced to detect the presence of the H275Y NA mutation . The donor ferret inoculated with oseltamivir-sensitive A/DM/524/09 virus shed virus until day 6 p . i . ( Figure 2A , Table 2 ) . Two of 2 direct-contact ferrets and 1 of 2 respiratory droplet-exposed ferrets were infected through virus transmission , as indicated by the virus titers and inflammatory cell counts in their nasal washes ( Figure 2 ) and by seroconversion ( Table 3 ) . Virus shedding and nasal inflammation began earlier in the direct-contact ferrets , suggesting that transmission through respiratory-droplets may have a greater lag time . One respiratory droplet-exposed ferret showed no detectable virus shedding or inflammation , but its post-contact serum had a positive HI titer ( 320 ) . Although seroconversion indicated infection in this ferret , the time of infection could not be determined and therefore we could not attribute the infection to direct contact with the co-caged ferret versus respiratory droplet transmission from the adjacent cage . The donor ferret inoculated with oseltamivir-resistant A/DM/528/09 virus shed virus until day 8 p . i . ( Figure 2 ) , with a peak virus titer comparable to that of A/DM/524/09 virus ( Table 2 ) . Two of 2 direct-contact ferrets were infected through transmission ( Figure 2 ) , but neither respiratory droplet-exposed ferret was infected , as confirmed by the absence of seroconversion ( Table 3 ) . These results showed that the oseltamivir-resistant H275Y mutant A/DM/528/09 virus was transmitted efficiently only by direct contact . Virus shedding in two direct-contact ferrets was lower and peaked after a longer interval in this group than in the oseltamivir-sensitive A/DM/524/09 group ( Figure 2A ) , although the severity and course of disease were similar ( Figure 2B , Table 3 ) . We verified the sequence stability of the NA at position 275 in each virus after replication and transmission in ferrets . Direct sequencing of the NA genes from nasal wash samples revealed no sequence change at this position in either virus ( data not shown ) . Therefore , no spontaneous H275Y NA mutation emerged in the wild-type virus and the H275Y mutation remained stable in the mutant after transmission to a new host . Because both the oseltamivir-sensitive and the oseltamivir-resistant H1N1/2009 viruses were efficiently transmitted by direct contact , hosts could potentially be exposed to both types of virus . To compare the relative growth capability and transmissibility of the sensitive and resistant H1N1/2009 viruses within the host , we co-inoculated a ferret with a 1∶1 ratio of the sensitive A/DM/524/09 and resistant A/DM/528/09 viruses . The pattern of virus shedding and the clinical signs were similar to those in ferrets inoculated with either A/DM/524/09 or A/DM/528/09 virus ( Figure 3A ) . By using a relative quantification of single nucleotide polymorphism ( SNP ) method to detect the NA genotype at codon 275 ( CAC or TAC ) , we found that the virus population in the co-inoculated ferret's nasal washes remained mixed but was predominantly a wild-type ( oseltamivir-sensitive ) population ( Figure 3B ) . The proportion of wild-type virus in the nasal wash increased progressively , from 75% on day 1 p . i . , to almost 100% on day 6 p . i . ( Figure 3B ) . Two of 2 ferrets placed in direct contact with the co-inoculated ferret were infected through transmission ( Figure 3A ) . SNP analysis of their nasal wash samples showed only wild-type virus ( Figure 3B ) . In summary , the oseltamivir-sensitive A/DM/524/09 virus possessed greater growth capability in the upper respiratory tract than did resistant A/DM/528/09 virus and thus had an advantage in direct-contact transmission .
This study is the first , to our knowledge , to demonstrate the inefficient respiratory droplet transmission of an oseltamivir-resistant H275Y mutant of H1N1/2009 in ferrets , which are an established animal model of the pathogenesis and transmission of human influenza viruses . The oseltamivir-resistant mutant virus retained efficient transmission only by direct contact , whereas the oseltamivir-sensitive pandemic virus was efficiently transmitted by both routes . These results show that the transmissibility of the oseltamivir-resistant H1N1/2009 influenza virus had been altered . We suggest that the lower fitness of oseltamivir-resistant variant within the host along with its reduced NA enzyme efficiency and delayed growth of the H275Y mutant virus in vitro may at least in part explain its impaired transmission among ferrets . There are limited experimental data about the routes of transmission of oseltamivir-resistant influenza viruses . The two natural routes of influenza virus transmission , direct contact with fomites and respiratory droplets ( aerosol and larger droplets [33] ) , are not mutually exclusive . Therefore , the transmissibility of influenza virus via both routes must be investigated if the results are to be clinically relevant . In the earliest studies , oseltamivir-resistant H3N2 ( R292K NA mutant ) and H1N1 ( H275Y NA mutant ) variants exhibited severely compromised replication and virulence both in vitro and in vivo [34] , [35] and were therefore thought unlikely to be of clinical consequence . In a subsequent study , an R292K mutant of H3N2 virus was not transmitted by direct contact among ferrets [28] . Under similar conditions , the transmission of an E119V mutant of H3N2 virus and an H275Y mutant of A/New Caledonia/20/99-like ( H1N1 ) virus by direct contact required a higher dose of inoculum than transmission of the wild-type viruses , and it occurred more slowly [27] . However , none of these studies assessed both routes of transmission . The only study to date that has evaluated both routes of transmission of oseltamivir-resistant virus showed that recombinant resistant H3N2 viruses with either the E119V or the E119V+I222V NA mutation were transmitted efficiently by direct contact but not by respiratory droplets among guinea pigs [26] . Our study is a latest addition to the previous data by comparing a highly matched pair of H1N1/2009 viruses and by assessing the transmissibility of resistant viruses via two routes in ferrets . The reduced transmissibility of the oseltamivir-resistant H1N1 viruses could be explained by a number of factors [33] , [36] , [37] . First , host physical exposure to virus is directly affected by the quantity of virus shed into the environment . In our study , inoculated donor ferrets shed comparable quantities of both viruses , which indicated potential comparable environmental contamination in the restricted space of cages; therefore , it is unlikely that transmission was affected by the level of donor viral shedding . Other host variables such as the extent of inflammation could affect the amount and size of upper respiratory secretions thus the release of infectious respiratory droplets . For example , sneezing , a common host symptom believed to mediate viral transmission , was observed only at later stages in the ferret inoculated with resistant virus , when inflammation was more severe but virus shedding had declined greatly . Second , efficient transmission to a naive host requires not only viral exposure but also successful viral invasion , effective replication and simultaneous evasion of the first line of host innate immunity [38] . Our results showed a significant initial growth delay in two cell lines of the oseltamivir-resistant virus . This growth delay could be caused by delayed release of progeny virions from the host cell surface due to reduced NA enzyme efficiency observed in the resistant virus . Such a delay would not affect the final virus yield in cell lines , but in the respiratory tract of ferrets it could allow the host's first-line innate immune defense ( e . g . , macrophages or neutrophils ) sufficient time to clear the virus . The NA enzyme also facilitates virus binding , entry , and spread within the host by removing terminal sialic-acid residues from mucus and preventing virion self-aggregation [39] , and therefore the NA mutation could have affected viral penetration into the host respiratory tract . The slightly reduced ( not severely impaired ) NA enzyme function and delayed viral growth of the H275Y mutant may have been more crucial in recipient ferrets that acquired virus from environment via natural routes than in donor ferrets inoculated with a high dose of virus , as we observed delayed viral shedding or inefficient transmission in the recipient ferrets , but not in the inoculated donor ferret . Although the transmissibility of the oseltamivir-resistant H1N1/2009 virus was reduced by the H275Y NA mutation , the severity and course of disease was similar to that caused by oseltamivir-sensitive H1N1/2009 virus in both inoculated and direct-contact ferrets , with no apparent attenuation of clinical signs . In inoculated ferrets , the viruses showed comparable replication in the upper respiratory tract and caused comparable clinical signs , including weight loss and inflammation . However , one caveat to ferret model has been noticed that high inoculation dose may mask the differential viral replication and clinical signs for different viruses [40] . In the direct-contact ferrets , which acquired virus though natural routes , the shedding of resistant virus peaked later than the shedding of susceptible virus , but the duration of shedding and the severity of disease was not compromised when compared with sensitive virus . Therefore , the H275Y mutant of pandemic H1N1/2009 virus is likely to be of clinical consequence in humans . The fitness of a virus describes its relative ability to produce infectious progeny in a host [41] . Competitive growth assay by co-infection is a method of evaluating the growth fitness of two viruses [41] , [42] . In the present study , we inoculated a ferret with equal doses of oseltamivir-sensitive and -resistant H1N1/2009 viruses to compare their relative growth fitness within the host . The mixed virus population in the nasal wash was analyzed at different days p . i . to determine which viral genotype predominated . To bypass the time- and labor-intensive process of cloning the desired genes from the mixed populations and choosing an arbitrary number of clones for genotypic analysis , we used a new method , relative quantification of SNP , to determine the ratio of wild-type to mutant populations . This method showed high reproducibility in genotyping HIV protease gene [42] . Our study is the first to use this method to genotypically analyze influenza viruses . For the H1N1/2009 influenza viruses , we designed a specific probe to detect the first nucleotide of codon 275 of the NA gene , where a single C-to-T substitution causes an H-to-Y amino acid substitution . Our results showed that the oseltamivir-resistant mutant H1N1/2009 virus possessed less growth fitness than the sensitive H1N1/2009 virus in the ferret upper respiratory tract . At least partly for that reason , only wild-type H1N1/2009 virus was transmitted to the direct-contact ferrets . The competitive transmission advantage of wild-type H1N1/2009 virus should be confirmed by other types of experiments . In summary , our study determined the comparative transmissibility of a pair of naturally circulating oseltamivir-sensitive and oseltamivir-resistant H1N1/2009 viruses . This information from this study could be useful in assessing the clinical relevance of contemporary pandemic viruses , considering the extensive use of oseltamivir during this pandemic . The H275Y mutant of H1N1/2009 used in this study was the first oseltamivir-resistant H1N1/2009 isolate from a patient on oseltamivir prophylaxis . As this study was undertaken , additional H275Y mutants of H1N1/2009 viruses have emerged in the absence of oseltamivir use [21]–[25] . The emergence of these viruses should raise concerns as to whether resistant H1N1/2009 viruses will acquire significantly greater fitness and spread worldwide as did the naturally resistant H1N1 viruses during the 2007–2008 season . Further studies of these newly isolated H275Y mutants of H1N1/2009 viruses are warranted to determine whether they have acquired additional changes .
All animal experiments with H1N1 influenza viruses were performed in biosafety level 3+ facilities at St . Jude Children's Research Hospital ( St . Jude; Memphis , TN , USA ) , were approved by the St . Jude Animal Care and Use Committee , and complied with the policies of the National Institutes of Health and the Animal Welfare Act . A/Denmark/524/09 ( H1N1 ) influenza virus ( A/DM/524/09 ) and an oseltamivir-resistant A/Denmark/528/09 ( H1N1 ) virus ( A/DM/528/09 ) were provided by Statens Serum Institute , Copenhagen , Denmark . The resistant virus was isolated from the tthroat swab of a patient who had influenza-like symptoms and received post-exposure oseltamivir prophylaxis ( 75 mg once daily ) [30] . A/Brisbane/59/07 ( H1N1 ) influenza virus ( A/BR/59/07 ) was provided by U . S . Centers for Disease Control and Prevention . Stocks of H1N1 viruses were prepared in Madin-Darby canine kidney ( MDCK ) cells ( ATCC , Manassas , VA ) and grown in minimal essential medium ( MEM ) supplemented with 5% fetal bovine serum , 5 mM L-glutamine , 0 . 2% sodium bicarbonate , 100 U/ml penicillin , 100 µg/ml streptomycin sulfate , and 100 µg/ml kanamycin sulfate in a humidified atmosphere of 5% CO2 . All strains of virus underwent a limited number of passages in MDCK cells to maintain their original properties . MDCK cells transfected with cDNA encoding human 2 , 6-sialyltransferase ( MDCK-SIAT1 cells ) were maintained as described previously [32] . The NA inhibitors oseltamivir carboxylate ( [3R , 4R , 5S]-4-acetamido-5-amino-3-[1-ethylpropoxy]-1-cyclohexene-1-carboxylic acid ) and zanamivir ( 4-guanidino-Neu5Ac2en ) were provided by Hoffmann-La Roche , Ltd . ( Basel , Switzerland ) . The compounds were dissolved in distilled water and aliquots were stored at −20°C until the time of use . The 50% tissue culture infectious dose ( TCID50 ) was determined in MDCK cells . The cells were infected with serial log dilutions of the stock viruses , incubated for 1 h at 37°C , washed , and overlaid with infection medium ( MEM with 0 . 3% BSA and 1 µg/ml TPCK-trypsin ) . Infection of cells was determined by hemagglutination assay ( HI ) after incubation for 3 d at 37°C , and TCID50 was calculated by the Reed-Muench method [43] . Single-step growth curves were generated for influenza viruses in MDCK cells or MDCK-SIAT1 cells . Confluent cell monolayers were infected with viruses at a multiplicity of infection ( MOI ) of ∼2 . 0 PFU/cell . After incubation , the cells were washed with 0 . 9% aqueous NaCl solution ( pH 2 . 2 ) to remove free infectious virus particles and then were washed twice with phosphate-buffered saline ( PBS ) to adjust the pH . Supernatants were collected 2 , 4 , 6 , 8 , 10 and 12 h p . i . and stored at −70°C for titration . To generate multi-step growth curves , MDCK cells or MDCK-SIAT1 cells were infected with viruses at a MOI of 0 . 001 PFU/cell . Supernatants were collected 12 , 24 , 36 , 48 , 60 and 72 h p . i . and stored at −70°C for titration in the same cell line . Confluent MDCK or MDCK-SIAT cells were incubated for 1 h at 37°C with 10-fold serial dilutions of virus in 1 ml of infection medium . The cells were then washed and overlaid with freshly prepared MEM containing 0 . 3% BSA , 0 . 9% bacto-agar , and 1 µg/ml TPCK trypsin . The plaques were visualized after incubation at 37°C for 3 d by staining with 0 . 1% crystal violet solution containing 10% formaldehyde . A modified fluorometric assay using the fluorogenic substrate 2′- ( 4-methylumbelliferyl ) -α-D-N-acetylneuraminic acid ( MUNANA ) ( Sigma-Aldrich ) was used to determine viral NA activity [44] . The fluorescence of the released 4-methylumbelliferone was measured in a Synergy 2 multi-mode microplate reader ( BioTek ) using excitation and emission wavelengths of 360 and 460 nm , respectively . The drug concentration required to inhibit 50% of the NA enzymatic activity ( IC50 ) was determined by plotting the percent inhibition of NA activity as a function of compound concentration calculated in the GraphPad Prism 4 software from the inhibitor-response curve . The NA inhibitor–sensitive A/Fukui/20/04 ( H3N2 ) influenza virus was included in every plate for comparison . All H1N1 viruses were standardized to an equivalent dose of 106 . 0 PFU/ml . We measured NA enzyme kinetics at pH 6 . 5 with 33 mM 2- ( N-Morpholino ) ethanesulfonic acid hydrate ( MES; Sigma-Aldrich ) , 4 mM CaCl2 , and MUNANA with a final substrate concentration of 0 to 400 µM . The reaction was conducted at 37°C in a total volume of 50 µl , and the fluorescence of released 4-methylumbelliferone was measured every 60 sec for 60 min in a Synergy 2 multi-mode microplate reader ( BioTek ) using excitation and emission wavelengths of 360 and 460 nm , respectively . The Km and Vmax were calculated by fitting the data to the appropriate Michaelis-Menten equations by using nonlinear regression in the GraphPad Prism 4 software . The A/PR/8/34 ( H1N1 ) influenza virus was included for comparison in all assays . Young adult ferrets ( 4–5 months of age ) were obtained from the ferret breeding program at St . Jude Children's Research Hospital . All ferrets were seronegative for influenza A H1N1 and H3N2 viruses and for influenza B viruses . Ferrets were housed in the isolators in ABSL3+ facilities and monitored for 3–5 days to establish baseline body temperature and overall health . Donor ferrets were initially housed separately from contact ferrets . The donor ferrets were lightly anesthetized with isoflurane and inoculated with 106 TCID50 of A/DM/524/09 , A/DM/528/09 virus in 1 . 0 ml sterile PBS . One donor ferret was inoculated with 106 TCID50 of a mixture of A/DM/524/09 and A/DM/528/09 viruses ( 1∶1 infectivity ratio ) . After the donor ferrets were confirmed to shed virus on day 2 p . i . by the Directigen Flu A+B quick test ( BD , Franklin Lakes , NJ ) , each was then housed in the same cage with 2 naïve direct-contact ferrets . Two additional recipient ferrets were placed in an adjacent cage isolated from the donor's cage by a two layers of wire mesh ( ∼5 cm apart ) that prevented physical contact but allowed the passage of respiratory droplets . A Borazine gun ( Zero Toys , Concord , MA ) was used to ensure non-directional air flow inside the isolator . The donor and recipient ferrets were housed together since day 2 p . i until day 21 p . i . Ferret weight and temperature were recorded daily for 21 days . Body temperature was measured by subcutaneous implantable temperature transponders ( Bio Medic Data Systems Inc , Seaford , DE ) . Nasal washes were collected from donors and recipients on days 1 , 2 , 4 , 6 , 8 , 10 , 12 , and 14 p . i . by flushing both nostrils with 1 . 0 ml PBS , and TCID50 titers were determined in MDCK cells . Inflammatory cell counts were determined as described previously [45] . Briefly , the nasal washes were centrifuged at 2000 rpm for 5 min . The pellet was resuspended in PBS , and the total cell number was counted in a hemacytometer under light microscopy . Inflammation was defined as a cell count ≥10 times the baseline count determined before the inoculation or exposure . Serum samples were collected from ferrets 3 weeks after virus inoculation , treated with receptor-destroying enzyme , heat-inactivated at 56°C for 30 min , and tested by HI assay with 0 . 5% packed chicken red blood cells ( CRBC ) as described previously [46] . Viral RNA was isolated from ferret nasal washes by using the RNeasy Mini kit ( Qiagen , Valencia , CA ) . Samples were reverse-transcribed and analyzed by PCR using primers specific for the NA gene segment , as described previously [47] . Sequencing was performed by the Hartwell Center for Bioinformatics and Biotechnology at St . Jude Children's Research Hospital . The DNA template was sequenced by using rhodamine or dRhodamine dye terminator cycle-sequencing Ready Reaction kits with AmpliTaq DNA polymerase FS ( Perkin-Elmer , Applied Biosystems , Inc . , Foster City , CA ) and synthetic oligonucleotides . Samples were analyzed in a Perkin-Elmer Applied Biosystems DNA sequencer ( model 373 or 377 ) . DNA sequences were completed and edited by using the Lasergene sequence analysis software package ( DNASTAR , Madison , WI ) . The alignment of NA and HA for multiple sequences was conducted by BioEdit software ( Tom Hall Ibis Therapeutics , Carlsbad , CA ) . The relative quantification of SNP assay was performed as described previously [42] , with slight modification . Briefly , an NA fragment ( nucleotide 673 to 1034 ) containing the codon at NA 275 position was amplified by RT-PCR . Primers were 5′-AGAACACAAGAGTCTGAATGTG-3′ and 5′-CCATTTGCTCCATTAGACGATACT-3′ . Single nucleotide primer extension was performed using a SNaPshot kit ( ABI ) per the manufacturer's protocol . The reaction consisted of 2 . 5 µl SNaPshot Reaction Mix , 3 µl RT-PCR product , and 0 . 2 µmol/L of the extension primer in a 5 µl final reaction volume . The extension primer , 5′-CAGTCGAAATGAATGCCCCTAATTAT-3′ , was synthesized by IDTDNA and used to detect the first nucleotide of NA 275 codon . After the SNaPshot reaction , a unit of shrimp alkaline phosphatase ( USB ) was added to remove 5′ phosphoryl groups of unincorporated dideoxynucleotide substrates as directed by manufacturer's protocol . One µl of the SNaPshot products was mixed with deionized formamide and LIZ120 ( ABI ) size standard and was injected into the ABI 3730xl capillary electrophoresis instrument ( ABI ) per the manufacturer's protocol . Data were analyzed by using ABI GeneMapper software . Serially diluted DNA template ( 35 ng/µl to 0 . 02 ng/µl ) from each genotype was used for signal standardization . Spike-in samples were generated by using 11 different ratios of wild-type and mutant DNA fragments , e . g . 100% wild-type , 90% wild-type , etc . A good correlation was achieved between the spike-in ratios and ratios of fluorescence intensity values ( R2 = 0 . 9877 ) ( data not shown ) . The unpaired t-test or analysis of variance ( ANOVA ) was used for all comparisons . | Most of the currently circulating pandemic H1N1/2009 ( “swine” ) influenza viruses are susceptible to the anti-influenza drug oseltamivir . Many countries have stockpiled oseltamivir for pandemic preparedness , and to date only a small proportion of the H1N1/2009 viruses isolated have been oseltamivir-resistant . However , if these viruses can be readily transmitted , oseltamivir resistance may spread . We evaluated the transmissibility of a pair of pandemic H1N1/2009 influenza viruses in ferrets . One virus was oseltamivir-sensitive and the other carried the oseltamivir resistance-associated H275Y NA mutation . We also investigated the viruses' susceptibility to NA inhibitors ( the drug class to which oseltamivir belongs ) , their NA enzyme kinetics , and their replication efficiency in cultured cells . Under identical conditions , the resistant H1N1/2009 virus was not transmitted by respiratory droplets but was efficiently transmitted by direct contact , while the sensitive H1N1/2009 virus was efficiently transmitted by both routes . | [
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] | 2010 | Oseltamivir–Resistant Pandemic H1N1/2009 Influenza Virus Possesses Lower Transmissibility and Fitness in Ferrets |
The ability to learn sequential behaviors is a fundamental property of our brains . Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results . In particular , when subjects have to learn multiple action sequences , learning is sometimes impaired by proactive and retroactive interference effects . In other situations , however , learning is accelerated as reflected in facilitation and transfer effects . At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings . Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings . The self-organizing recurrent neural network ( SORN ) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity ( STDP ) with homeostatic plasticity mechanisms ensuring network stability , namely intrinsic plasticity ( IP ) and synaptic normalization ( SN ) . When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference , facilitation , and transfer effects . We show how these effects are rooted in the network’s changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity . Furthermore , since learning in the model is based on fundamental neuronal plasticity mechanisms , the model reveals how these plasticity mechanisms are ultimately responsible for the network’s sequence learning abilities . In particular , we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences . This ability to form effective internal models is also the basis for the observed interference and facilitation effects . This suggests that STDP , IP , and SN may be the driving forces behind our ability to learn complex action sequences .
Humans can improve their performance in sequential movement tasks through practice , but such motor learning has shown puzzling and seemingly contradictory results . On the one hand , a wide variety of proactive and retroactive interference effects have been observed when multiple tasks have to be learned [1] . On the other hand , some studies have reported facilitation and transfer of learning between different tasks , sometimes based on abstract structure similarities [2] . At present it is unclear what learning mechanisms give rise to these various findings , how these effects depend on the training , what their biophysical substrate is , and in what brain structures they are implemented . Progress towards answering questions about the neural underpinnings of sequence learning in humans and other mammals has revealed that it depends on a number of brain structures including the recurrent loops between neocortex , cerebellum , and basal ganglia [3] . At this system level , computational modeling work rooted in reinforcement learning has tried to explain the contributions of different brain areas [4] while matching the behavioral performance of humans and monkeys . At the cellular level , there has been a strong interest in how the learning of sequential patterns may be supported by the temporally asymetric learning window of spike-timing-dependent plasticity ( STDP ) [5–9] and related learning rules , e . g . , [10–17] , review in [18] . Furthermore , it has been investigated how the relatively short time windows associated with STDP might be extended to behaviorally relevant time scales [19] . However , such models have not been related to human performance in actual sequence learning experiments and no mechanistic explanation of the above-mentioned interference and facilitation effects has been given . Here we show how these effects can be understood based on the interaction of different learning mechanisms in a recurrent neural network model . Specifically , we consider the self-organizing recurrent neural network ( SORN ) , a sparsely connected recurrent network model whose activity and connectivity are shaped by three plasticity mechanisms: spike timing-dependent plasticity ( STDP ) , intrinsic plasticity of neuron excitability , and a form of synaptic normalization [20] . Despite its simplicity , the original SORN model and a recent extension have been shown to exhibit powerful sequence learning abilities [20 , 21] . Moreover , a variation of the SORN has been shown to match findings on the probability distribution and the pattern of fluctuations of synaptic efficacies in neocortex and hippocampus [22] . Most recently , it has been shown that the SORN can reproduce a range of findings on neural spiking variability and the relationship between spontaneous and evoked activity patterns [23] . Therefore , it is an interesting candidate model for trying to bridge the gap between behavioral performance of human subjects on the one hand and cellular and synaptic mechanisms of plasticity on the other hand . In the present work , we consider a SORN model which receives stimulus-specific input and is connected to a layer of motor neurons mediating movement sequences through a winner-take-all mechanism . We use this network to model a series of experiments on movement sequence learning [1 , 24–26] using a single set of parameters in all simulations . We furthermore show the robustness of these results across variations of network parameters . The network learns to carry out the correct movement sequences over trials and reproduces differences in behavior between training schedules such as blocked vs . randomly interleaved training . The network also reproduces human performance in tasks with similar training sequences but different training times . In addition , it shows how psychophysical performance measures are reflective of the learned neuronal representations in the recurrent network . Mutual information calculations and PCA of network activity reveal how input representations and trajectories of neural activity change with training . Importantly , by parametrically varying tasks when learning multiple sequences we find an interaction between training schedule and task similarity , which provides testable predictions for further experiments . In sum , we show how fundamental mechanisms of neural plasticity may be responsible for the rich set of interference and facilitation effects induced by task similarity and training schedule in human sequence learning .
In this section , we present a specific recurrent neural network with threshold units combining three different forms of plasticity . The network architecture here belongs to the SORN family: a self-organizing recurrent neural network [5 , 20 , 22 , 23] and a schematic is provided in Fig 1 . In contrast to traditional reservoir computing architectures [27–29] the “reservoir” is not static in SORNs but adapts to inputs via multiple plasticity mechanisms giving rise to powerful sequence learning abilities [20 , 21] . The network is composed of NE excitatory ( E ) and NI ( = 0 . 2 × NE ) inhibitory ( I ) threshold units . Neurons are connected through weighted synaptic connections , where Wij is the connection strength from unit j to unit i , with self-connections being prohibited . All possible connections between the excitatory and inhibitory neuron populations are present ( WIE and WEI ) , while the excitatory to excitatory connections WEE are sparse and random . On average each neuron has λW incoming and outgoing connections . Direct connections between inhibitory units are absent . The initial weight strengths are drawn from the interval [0 , 1] and subsequently normalized such that the incoming connections to a neuron sum up to one: ∑ j W i j I E = 1 , ∑ j W i j E I = 1 , ∑ j W i j E E = 1 . The network state , at a discrete time t , is given by the binary vectors x ( t ) with length NE and y ( t ) with length NI corresponding to the activity of the excitatory and inhibitory units , respectively . The TE and TI are threshold values for the excitatory and inhibitory units . They are initially drawn from a uniform distribution in the interval [ 0 , T m a x E ] and [ 0 , T m a x I ] , respectively . The Heaviside step-function θ ( . ) constrains the activation of the network at time t to a binary representation: The neuron i fires if the total drive it receives is greater then its threshold ( xi ( t ) = 1 ) otherwise it stays silent ( xi ( t ) = 0 ) . The evolution of the network state is described by: x i ( t + 1 ) = Θ ( ∑ j = 1 N E W i j E E ( t ) x j ( t ) - ∑ k = 1 N I W i k E I y k ( t ) + v i U ( t ) - T i E ( t ) ) ( 1 ) y i ( t + 1 ) = Θ ( ∑ j = i N E W i j I E x j ( t + 1 ) - T i I ) . ( 2 ) Each input symbol ( letter or digit ) is associated with a predefined subset of NU input units , and all neurons i in the corresponding group will receive a positive input drive ( v i U ( t ) = 1 ) . There is no overlap between input units of different symbols . We are using the same plasticity mechanisms as the original SORN . The network relies on three forms of plasticity: STDP , Synaptic Normalization ( SN ) of the excitatory-excitatory connections , and Intrinsic Plasticity ( IP ) regulating the thresholds of excitatory units . All plasticity rules here only apply to excitatory units and connections between excitatory units . To understand the mechanisms underlying changes in the neuronal activities across learning and mediating the differences in generated motor sequence behavior we carried out a number of different analyses of network activities , which are detailed in the following section .
Overall , we carried out five different experiments to address sequence learning tasks in SORN and elucidating the connection between facilitation and interference effects on the one hand and task similarities and training schedule on the other hand . First , we made sure that SORN is able to reproduce some of the key aspects of previously published behavioral work with a single set of network parameters across all simulations . To this end we modeled human sequence learning tasks published in Panzer et al . [24] , the tasks with altered learning times published in Panzer et al . [25] , and a sequence learning task involving finger tapping published by Koedijker et al . [26] . Based on these results , we devised two sets of additional experiments addressing sequence element representations and investigating joint effects of task similarity and training schedule . From the considered experiments we can conclude that learning a sequence S1 can lead to proactive facilitation when subsequently learning a similar sequence S2 . But arguably , this may not be the case for arbitrary sequences S1 and S2 . In the considered cases , the sequences had a high similarity in terms of the overlap of positions and directions within the movement sequences . However , the measure in performance considers the whole task sequence . To study how task similarity can influence learning performance , for example , whether changes on specific positions will influence the learning locally , we carried out a position specific analysis of input sequences . Based on these results , we trained SORN on the discretized button press experiments by Koedijker at . el [26] , in which subjects learned two tasks consisting of consecutively pressing eight target buttons in sequence . The two sequences , which had to be learned , differed on positions 4 and 5 within the sequences , which were exchanged . In this section , we trained SORN with the same network parameters in all previous experiments on a large number of different sequence learning tasks to investigate the effects of task similarities and training schedules . We jointly varied task similarities between sequences , as quantified by the fraction of overlapping sequence elements , and training schedules , as measured by the number of blocks of training in which the training sequence is not altered . We show how task similarity and training schedule interact to produce a rich set of interference and facilitation effects thereby unifying procedural memory consolidation and structure learning in a recurrent network model with multiple plasticity mechanisms . This provides an implementational explanation of a rich set of behavioral phenomena as well as testable predictions for further experiments .
In this work we have shown how different phenomena in human sequence learning can all be understood based on generic learning principles in a recurrent neural network model . Specifically , we have considered a sparsely connected recurrent network whose activity and connectivity is shaped by three plasticity mechanisms: spike-timing dependent plasticity ( STDP ) , an intrinsic plasticity regulating neuronal excitability , and a synaptic normalization controlling the amount of afferent input to each neuron . The network receives stimulus-specific input and is connected to a layer of “motor” neurons mediating the movement sequences through a winner-take-all mechanism . We have used this network to model a series of experiments on movement sequence learning using a single set of parameters in all simulations . The network learns to carry out the correct movement sequences over trials and reproduces differences in behavior between training schedules such as blocked vs . randomly interleaved training . The network also shows close similarity to human performance in tasks with similar training sequences but different training times . Like various pervious models [5–9] , our model of sequence learning is formulated as a spiking network learning through STDP and we have used it to model behavioral data from human subjects . We view this approach as complementary to recent modeling efforts using firing rate networks to reproduce neural firing patterns in motor cortex , e . g . , [41 , 42] . Whether such firing patterns can also be learned with spiking networks through ( reward-modulated ) STDP is an interesting topic for future research , as is the question wether such rate models , often trained with very different learning mechanisms , can reproduce the kinds of behavioral data on interference and facilitation effects that have been the focus of the present study . The current work presents a detailed analysis of the underlying changes in the neuronal representations of the motor sequences across learning . Mutual information , PCA of network activity , and measures of neuronal selectivity reveal how neural activity changes with training and how these changes crucially depend on the three plasticity mechanisms in the SORN . Finally , we have provided testable predictions for future experiments jointly varying task similarity and training schedule . Overall , we have shown how task similarity and training schedule can interact to produce a rich set of interference and facilitation effects thereby unifying procedural memory consolidation and structure learning in a recurrent network model with multiple plasticity mechanisms . The SORN model we have used in this study is admittedly a gross simplification of learning processes in real cortical networks . It uses binary threshold units operating in discrete time steps and highly abstracted forms of plasticity . It is intriguing , however , that networks from the SORN family have already managed to account for both various structural features of cortical networks [22 , 43] , as well as a large range of physiological findings on neural variability and the relationship between spontaneous and evoked activity [44] . This suggests that despite their simplicity they capture some essential aspects of cortical information processing and learning . Therefore , it is maybe not that surprising that they also manage to account for a range of psychophysical findings on human sequence learning as we have demonstrated here . Studying the restructuring of neural circuits and their changes in representation during sequence learning in human subjects is currently not feasible . However , extended recordings from the same neural circuit during acquisition of a complex behavior are now possible in animal experiments . Impressively , [45] have even optogenetically reversed synaptic changes occurring during learning of a motor task thereby erasing a recently acquired engram . This makes studying the neural mechanisms underlying sequence learning behaviors both experimentally and theoretically a promising direction for future research . | From dialing a phone number to driving home after work , much of human behavior is inherently sequential . But how do we learn such sequential behaviors and what neural plasticity mechanisms support this learning ? Recent experiments on sequence learning in human adults have produced a range of confusing findings , especially when subjects have to learn multiple sequences at the same time . For example , the succes of training can strongly depend on subjects’ training schedules , i . e . , whether they practice one task until they are proficient before switching to the next or whether they interleave training of the different tasks . Here we show that a model self-organizing neural network readily explains many findings on human sequence learning . The model is formulated as a recurrent network of simplified spiking neurons and incorporates multiple biologically plausible plasticity mechanisms of neurons and synapses . Therefore , it offers a theoretical bridge between basic mechanisms of synaptic and neuronal plasticity and the behavior of human subjects in sequence learning tasks . | [
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] | 2017 | A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity |
In an accident later known as the Lübeck disaster , 251 neonates were orally given three doses of the new Bacille Calmette–Guérin ( BCG ) antituberculosis ( TB ) vaccine contaminated with Mycobacterium tuberculosis . A total of 173 infants developed clinical or radiological signs of TB but survived the infection , while 72 died from TB . While some blamed the accident on BCG itself by postulating reversion to full virulence , such a possibility was conclusively disproven . Rather , by combining clinical , microbiological , and epidemiological data , the chief public health investigator Dr . A . Moegling concluded that the BCG vaccine had been contaminated with variable amounts of fully virulent M . tuberculosis . Here , we summarize the conclusions drawn by Moegling and point out three lessons that can be learned . First , while mortality was high ( approximately 29% ) , the majority of neonates inoculated with M . tuberculosis eventually overcame TB disease . This shows the high constitutional resistance of humans to the bacillus . Second , four semiquantitative levels of contamination were deduced by Moegling from the available data . While at low levels of M . tuberculosis there was a large spread of clinical phenotypes reflecting a good degree of innate resistance to TB , at the highest dose , the majority of neonates were highly susceptible to TB . This shows the dominating role of dose for innate resistance to TB . Third , two infants inoculated with the lowest dose nevertheless died of TB , and their median time from inoculation to death was substantially shorter than for those who died after inoculation with higher doses . This suggests that infants who developed disease after low dose inoculation are those who are most susceptible to the disease . We discuss some implications of these lessons for current study of genetic susceptibility to TB .
Tuberculosis ( TB ) is an enduring global public health challenge , with 9 million new cases of disease each year [1] . Approximately one-third of the world’s population has been infected with Mycobacterium tuberculosis , yet less than 10% of infected individuals develop clinical disease during their lifetimes [2] . Understanding why only some individuals exposed to the bacilli develop disease is of paramount public health importance and has been a major focus of global TB research efforts [3] . A range of intrinsic biological factors contribute to an individual’s risk of developing disease . Among these , human genetics studies have identified some common variants that confer a modest risk of disease and some rare variants that can determine severe forms of TB [4] . However , for most infected people who develop disease , a predisposing or determining factor is still lacking . Most importantly , the impact of exposure length and intensity , i . e . , the infectious dose , on the risk of developing clinical TB is unknown . M . tuberculosis is typically transmitted via the respiratory route . The bacilli are deposited by aerosol in the lung alveoli , where they are taken up by alveolar macrophages , interstitial lung macrophages , dendritic cells , and neutrophils . Dendritic cells provide a major conduit for the transport of M . tuberculosis to the lymphatic system . If innate immunity effector cells fail to control the infection in the lung , dendritic cells will migrate to the regional lymph node and present M . tuberculosis antigens to T cells [5] . This will trigger an acquired immune response by which antigen-specific T cells migrate to the lung and secrete cytokines , such as IFN-γ , that enhance macrophage microbicidal activity . This can result in effective killing of all tubercle bacilli , the formation of infectious granulomas that wall off the bacilli and prevent further spread of the infection , or can prove ineffective , resulting in the development of primary TB disease that may spread from the lung to other organs ( extrapulmonary , disseminated TB ) . The latter outcome is more typically found in pediatric TB , and very young children are generally considered to be extremely susceptible to TB disease [6 , 7] . A TB-like disease can also be caused by infection with M . bovis , which is typically transmitted by consumption of contaminated milk [8] . Because of the oral route of infection , the number of extrapulmonary granulomas is higher than in M . tuberculosis TB [9] . The cellular mechanisms of pathogenesis are thought to be independent of the route of infection , while tissue-based pathogenesis depends on the route of infection . Accounts of the natural history of TB from the pre-antibiotic era can provide valuable insights into variation in disease susceptibility . The “Lübeck disaster , ” unfolding in the spring of 1930 , offers such insights . At that time , 251 newborn babies were accidentally inoculated with M . tuberculosis-contaminated Bacille Calmette–Guérin ( BCG ) , an attenuated form of M . bovis , instead of a pure substrain of the live BCG vaccine . In the follow-up to the accident , the establishment of a uniform dataset and the systematic evaluation of the clinical and epidemiological data were done by Dr . Albert Moegling of the German Tuberculosis Research Institute and the Federal Public Health Office . Accurate details of the outbreak have only been available in German [10] . Here , we recapitulate the events and outcomes of the incident , based on Moegling’s meticulously recorded clinical and pathological data , and examine this outbreak from a fresh perspective , attempting to draw some lessons for our understanding of this challenging and devastating disease .
Beginning in December 1929 , the Lübeck General Hospital embarked on a campaign to prevent TB . All newborns were offered the newly available live BCG vaccine ( strain 374 ) within the first ten days of life , although not all parents accepted vaccination . Unfortunately , vials of vaccine were inadvertently contaminated with live M . tuberculosis bacteria at unknown proportions . The source of contamination was an easily identifiable , uncommon strain of M . tuberculosis ( Kiel strain ) that was cultivated at the vaccine preparation laboratory . Over the next four months , 251 of the 412 newborn babies were given a contaminated vaccine . For vaccine batch preparation , BCG was grown for 14 days on egg-nutrient agar , taken up in a glycerol–glucose solution , and adjusted to desired bacterial content by density determination . A total of three aliquots , each corresponding to 2 ml , were packaged for each infant . Most commonly , the vaccine stock for a given vaccination day was obtained from pools of two to four harvested agar cultures . For vaccine administration , vaccine vials were vigorously shaken , and re-suspended bacterial emulsions were added to a spoon of warm breast milk . Each dose contained the equivalent of 10 micrograms of a solid bacterial culture . The recommended administration of the vaccine involved three separate oral doses of the bacterial emulsion , to be given during the first ten days of life . Each dose was separated by two days from the next inoculation , implying that the first dose was received no later than day six of life . The vaccine was given by mothers , midwives , or hospital nursing staff using a medicine spoon . Occasionally , if infants did not swallow the vaccine , their noses were held until it was swallowed . Dr Moegling’s report indicated that all but 14 children received the prescribed three doses . Some children vomited or experienced diarrhea soon after ingestion and/or up to one week following vaccination . Following extensive interviews with caregivers , Moegling concluded that while there were deviations from the recommended protocol ( e . g . , too much liquid , heating of the preparation , the use of water or diluted milk instead of breast milk ) , such deviations were the exception and did not impact the main conclusions reached in his report . Within the next three months , a substantial number of infants began to die , and a public health investigation was launched . Autoradiographs were conducted on a total of 228 children , while tuberculin skin tests ( TST ) were performed on 212 infants as part of their clinical assessments . Surviving children were followed closely until 1933—three years after the outbreak began—since no antibiotic therapy to treat the disease was yet available . Abdominal radiography was performed on surviving children in 1932 and 1933 to record the presence of mesenteric lymph node calcification , a sign of mesenteric TB . For deceased children , an autopsy was performed . Deaths were classified by pathologists as either being related to TB or due to another cause [11] . The socioeconomic status of each infant was also determined , on the basis of a questionnaire administered to their parents .
Among the 251 inoculated children , 228 ( 90 . 8% ) developed some clinical or radiographic evidence of TB disease ( Table 1 ) , and 77 infants ( 30 . 7% of those inoculated ) died within a year of inoculation . Of those who died , five were thought to have died of TB-unrelated causes , while the remaining 72 demonstrated clinical evidence of extensive TB . While the parents of four of the children refused permission for autopsy , pathology on autopsies of all remaining 68 children confirmed the clinical diagnosis of TB disease [11] . In contrast , of the 164 non-inoculated children born during the same period , 19 ( 11 . 6% ) died before the age of three , including 16 before the age of one , from TB-unrelated causes . We calculated that mortality in the first year of life was 3 . 1 ( 95% CI 1 . 9–5 . 1 ) times higher in inoculated than non-inoculated children . Male children were more likely to die during follow-up than females ( relative risk [RR] 1 . 6 , 95% CI 1 . 0–2 . 3 ) ( S1 Table ) . Of note , the proportion at year one of non-TB–related deaths in the inoculated children was significantly reduced compared to non-inoculated neonates ( p = 0 . 002 ) . Since the prognosis for M . tuberculosis-exposed children was the dominating concern at the time of the accident , TB disease was classified by the investigating team in one of seven categories in order of improving prognosis: ( 1 ) death; ( 2 ) severe illness with worst prognosis; ( 3 ) severe illness with questionable , eventually unfavourable , prognosis; ( 4 ) moderately ill with questionable , eventually favourable , prognosis; ( 5 ) mild illness with clear clinical symptoms of TB , yet a good prognosis; ( 6 ) no clearly detectable signs of TB , but with positive TST; ( 7 ) no signs of disease and a negative TST . A total of 156 infants developed TB yet recovered spontaneously from the disease in the absence of effective treatment . The clinical history of TB in inoculated children is shown in Fig 1 . Only six surviving children demonstrated ongoing signs or symptoms of active disease after three years . Follow-up investigations in subsequent years detected autoradiographic evidence of disease in 17 apparently healthy children . The relationship between the time of inoculation and outcomes was carefully evaluated . Moegling observed that the proportion of children with severe disease varied markedly according to the day on which they had been inoculated . For example , among 23 newborns inoculated between March 25 and 27 , 1930 , 17 ( 74% ) died and four ( 17% ) developed severe TB . In contrast , of 20 children inoculated between April 1 and 5 , 1930 , none ( 0% ) died of TB-related causes and 11 infants ( 55% ) developed mild disease . Understanding the pronounced differences in outcome for different vaccination days was a major question in the investigation of the outbreak . As a first step , Moegling considered different operational and demographic causes of the observed heterogeneity in outcome , including a possible role of specific caregivers , sex of children , family history of TB , socioeconomic status of parents , vaccine preparation , adverse effects , and deviations from feeding protocol such as administration of less than the three required doses of the BCG preparation . While some of these factors had an impact on individual outcome , none could explain group outcomes for specific vaccination days . Specifically , breast milk had no discernible impact on either mortality or severity of TB . Likewise , secondary childhood infections were not substantial confounders of severity of TB . Hence , Moegling postulated that group outcome heterogeneity reflected contamination of the BCG vaccine preparations with differing amounts of M . tuberculosis for each vaccination day . He allocated infants to one of four strata ( called “virulence strata” 1–4 ) , defined according to the proportion of children who died or developed severe disease with poor prognosis on the day that the first dose was administered . The method by which cutoffs for these strata were chosen was not reported in detail . A quantitative determination of contamination levels in individual vaccine batches given to infants was not possible . One of the treated children was assumed to receive a pure BCG vaccine called “virulence stratum 1” ( n = 1 ) . Stratum 2 ( n = 93 ) , stratum 3 ( n = 83 ) , and stratum 4 ( n = 74 ) were classified in order of increasing mortality based on the day of administration , and stratum was therefore associated with group mortality . Increased group mortality was attributed to an increased dose of M . tuberculosis in the vaccine batch . The clinical outcomes of infants in each of Moegling’s strata are shown in Table 2 . In the absence of convincing alternative hypotheses , Moegling’s explanation of different levels of contamination in the four virulence strata is supported by four observations: ( i ) Follow-up testing of two sources of BCG from the Lübeck laboratory revealed variable degrees of contamination with M . tuberculosis . First , analysis of BCG stock cultures , including those potentially used for vaccine preparation , revealed variable contamination with M . tuberculosis ( Kiel strain ) in five of 12 tested cultures . One of those cultures was revealed as pure M . tuberculosis [12] . In addition , leftovers from ten batches of used vaccine vials were put in culture , and , in three instances , mycobacteria could be grown . Follow-up studies in animal experiments showed that two cultures were made up of pure BCG , while from one batch , only M . tuberculosis could be cultivated [12] . ( ii ) The number and extent of infection foci detected either by autopsy or during roentgenographic follow-up of surviving children correlated strongly with the virulence strata . ( iii ) Among the 74 children given the vaccine with the highest level of M . tuberculosis contaminant—virulence stratum 4—three of the four children who missed at least one dose survived . The risk of death was 2 . 9 ( 95% CI 1 . 5–5 . 8 ) times higher among children who took all doses compared to those who missed one or more doses . ( iv ) Vomiting and diarrhea within a week of inoculation were associated with a substantial reduction in mortality . The relative risk of death with vomiting and diarrhea was 0 . 32 ( 95% CI 0 . 15–0 . 68 ) compared to those who did not vomit or develop diarrhea ( S2 Table ) . Presence of primary foci in the lung of some of the children with vomiting strongly suggested that these children aspirated part of their vomitus . When taking into account 221 children without evidence for vomitus aspiration , the protective effect of diarrhea and vomiting was substantially stronger , with a relative risk of death of 0 . 06 ( 95% CI 0 . 01–0 . 46 ) . For the 72 infants who died of TB , the median time from inoculation to disease onset was 42 days ( IQR 28–56 days ) , as shown in Table 2 . The time from first inoculation to onset of disease was independent of the inferred level of contamination . The median time from inoculation to death was 86 days ( IQR 67–114 days ) , with a range of 34 to 363 days . Death occurred more rapidly in the two individuals who died after being given a “low dose” of M . tuberculosis ( virulence stratum 2 ) , although this did not reach statistical significance ( p = 0 . 12 ) . Children inoculated with preparations of the same vaccine batch displayed a spectrum of clinical symptoms ranging from death to very mild signs of TB . This observation is often quoted as evidence for variable innate , possibly genetically controlled , resistance to TB disease . However , mild and moderate disease followed the mortality classification scheme very well . For example , in the lowest virulence stratum , 16 . 1% of children showed no overt clinical symptoms of disease , while 67 . 7% displayed moderate TB . In contrast , none ( 0% ) of the children in the highest virulence stratum were without any clinical symptoms , and only 4 . 1% displayed moderate TB disease ( Table 2 ) . Therefore , the proportions of neonates with mild or no symptoms were strongly correlated with deduced dose , suggesting that innate resistance can be overcome by increasing exposure intensity to M . tuberculosis .
In spite of the tragic deaths of 72 children from TB , a striking feature of the outbreak was the remarkable resilience of the remaining children who had been exposed to the mycobacterium . Careful clinical observation of the 174 surviving children found that only six children remained unwell by September 1933 . Overall , 68% of those who had developed clinical disease achieved a spontaneous resolution of their symptoms . This is similar to findings in the limited number of other studies in children from the pre-antibiotic era [7] . It is worthwhile to keep in mind that the mode of infection in the accident more closely resembles M . bovis , which is commonly transmitted via the oral route . The second lesson from the Lübeck disaster is the important relationship between dose and outcome . Clear differences were observed in the mortality rate according to the day of vaccine administration . Although the concentrations of virulent bacteria were not measured at the time of inoculation , temporal trends in prognosis suggested that an environmental factor ( s ) explained much of the variability in outcome between individuals . The chief investigator of the outbreak , Moegling , concluded that variation in dose of M . tuberculosis was the most likely explanation for the temporal variability . This explanation was directly supported by the bacteriologic workup that detected pure cultures of BCG , variable levels of M . tuberculosis in BCG cultures , and pure M . tuberculosis cultures . Moegling concluded that , as between two and four stock vials were used to prepare each vaccine batch throughout the outbreak , it was quite possible that the different batches used for inoculation of neonates contained M . tuberculosis at different concentrations . Likewise , as mentioned above , any deviation from the protocol that resulted in reduced uptake of inoculum had substantial beneficial effects for neonates . While we cannot be certain that Moegling’s explanation is correct , as the concentration of the virulent bacteria was not directly measured , his explanation is consistent with all known facts of the disaster . As pointed out by Moegling , we must then conclude that with an increasing dose of M . tuberculosis , the role of any individual resistance ( e . g . , genetic resistance to disease ) would be less important than the effect of the dose—i . e . , the effect of a high level of exposure outweighed the natural resistance to the bacillus . In this context , the general shift of disease severity with increasing dose further supports the dominating role of the quantity of ingested M . tuberculosis for the clinical picture . The median time intervals from exposure to the occurrence of clinical symptoms were very similar irrespective of the infectious dose ( Table 2 ) . On the other hand , the time to death was particularly short among the two children exposed to a “low dose” ( a median of 59 days for virulence stratum 2 compared to 86 days for the whole cohort ) ( Table 2 ) . Although the numbers here are small , and , hence , do not reach statistical significance , this observation does raise some interesting possibilities . It is possible that these two individuals were particularly susceptible to the disease , given the rapid progression in the setting of a low “virulence dose . ” In light of the observation that the quantum of exposure is likely to have a major effect on prognosis , it may be that the effects of individual susceptibility are best seen at the lower end of the dosing spectrum . Adapted to present-day conditions , this might imply that “sporadic” cases without known index case are more likely to reveal strong genetic susceptibility . This observation had been previously made in the context of interleukin-12 receptor β1 deficiency but also for pediatric TB and the impact of the NRAMP1 gene on TB susceptibility [13 , 14] .
The Lübeck disaster offers some valuable insights into the relationship between environmental exposure and innate susceptibility to TB . In particular , the importance of environmental exposure in disease progression could explain why human genetic studies of TB , to date , have failed to identify as many replicated susceptibility genes as in other complex diseases . For example , in contrast to leprosy ( caused by M . leprae ) , for which a number of potential susceptibility candidate genes have been identified [15 , 16] , results of genetic association studies in TB disease have identified relatively few loci , each with a very modest effect , that could be replicated consistently in different populations [17–19] . In leprosy , the mode of transmission is still unknown , but given the unusually long exposure times to cause disease , it seems likely that effective infectious doses are generally lower than in TB , which might account for the selection of strong genetic effects , especially among young , early-onset leprosy cases [20] . Despite its historical nature , important implications of the accident for the present-day study of TB susceptibility can be seen . In the present antibiotic era , TB patients are treated , and we do not know which of the patients would have self-healed . Hence , the pronounced shift away from no or moderate symptoms of TB ( which might go unnoticed ) toward more severe TB disease with increasing dose poses a huge challenge for phenotype definition of TB susceptibility . The data clearly suggest that TB susceptibility for molecular studies needs to be defined in the context of a given dose—something that has been accepted by researchers working on the mouse model for a long time . It appears the best approach to this challenge is to work with “sporadic” cases in low-transmission settings that , on average , are expected to have been exposed to a lower dose . In contrast , drawing inferences about dose in high transmission settings is more difficult . Low-dose sporadic patients likely also have stronger genetic susceptibilities , as pointed out above . The tragic incident provides a unique window into the natural history of TB in the neonatal period . The vaccine administration dates were carefully documented by Moegling for those children who died , permitting the duration between exposure and onset of clinical symptoms to be determined . A median time of 42 days to onset of clinical disease and 86 days to death is consistent with the time span for TB onset in early childhood recorded in the pre-antibiotic era [7] . The regression of disease for the majority of neonates is consistent with early descriptions of so-called “self-cure” [7] . Furthermore , children with moderate disease had persistent signs and symptoms for up to a year before either being cured or succumbing to the infection .
Despite careful efforts , there were some limitations preventing investigators from getting definite answers about the accident . The major limitation was the absence of experimental quantification of the dose of M . tuberculosis that was administered to children . Furthermore , the precise details of the way in which the vaccines were drawn from the contaminated flasks were not documented . Despite these limitations , the available evidence supports the principal conclusion reached by the investigative team: children were inoculated with a BCG preparation that contained M . tuberculosis , and the quantum of M . tuberculosis contaminant was not constant but varied between different vaccine batches . A further limitation in evaluation of temporal trends of clinical disease is the bias in the timing of diagnosis of children—diagnostic delay was more likely at the start of the vaccination campaign disease , before the outbreak was recognized . By the end , clinicians were more vigilant for early symptoms of disease and would have diagnosed the disease earlier , and clinical care might have been more focused in the later stages of the accident . Finally , observations about the clinical course of TB in the inoculated children cannot necessarily be generalized to modern childhood TB , which is acquired by usual airborne transmission rather than by oral administration of a concentrated bolus of bacteria . In Lübeck , contaminated BCG was administered orally , resulting in a high proportion of oropharyngeal and gastrointestinal disease . It is likely that infection via the respiratory route would have resulted in more severe disease as is suggested by the increase of mortality in children with pulmonary foci as compared to those who did not display lung involvement . Nonetheless , lessons about timing from exposure to disease and death are likely to be broadly similar . In conclusion , the Lübeck accident caused a major global scandal at the time , on account of the tragic circumstances . However , it has an enduring legacy in demonstrating the importance of exposure intensity to M . tuberculosis in determining outcomes of TB and highlighting the importance of accounting for environmental factors when examining host susceptibility to disease . | The Lübeck disaster is a unique event in the history of tuberculosis when 251 newborns were accidentally infected with a virulent strain of Mycobacterium tuberculosis . The disaster happened while BCG was introduced as an anti-TB vaccine . In an exemplary multidisciplinary investigation , the disaster was shown to be due to the accidental contamination of BCG vaccine preparations with virulent M . tuberculosis and not BCG itself . A meticulous description of the accident , including epidemiological , histopathological , and bacteriological observations , was published; however , this report is only available in German , thus limiting access to the data . Here , we present a summary of the accident and its clinical consequences . Based on the results of the initial report , we draw three important conclusions: ( i ) overall , the newborns demonstrated remarkable resistance to virulent M . tuberculosis; ( ii ) the extent of contamination had a strong impact on the observed TB clinical spectrum , demonstrating the importance of the infectious dose for innate resistance to TB; and ( iii ) the possible effect of host genetics on TB susceptibility appeared most prominent in the context of a low dose of M . tuberculosis . The latter observation suggests that low exposure settings may be best suited for host genetics studies of susceptibility to TB . | [
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] | [] | 2016 | Tuberculosis in Newborns: The Lessons of the “Lübeck Disaster” (1929–1933) |
mRNA levels are determined by the balance between transcription and mRNA degradation , and while transcription has been extensively studied , very little is known regarding the regulation of mRNA degradation and its coordination with transcription . Here we examine the evolution of mRNA degradation rates between two closely related yeast species . Surprisingly , we find that around half of the evolutionary changes in mRNA degradation were coupled to transcriptional changes that exert opposite effects on mRNA levels . Analysis of mRNA degradation rates in an interspecific hybrid further suggests that opposite evolutionary changes in transcription and in mRNA degradation are mechanistically coupled and were generated by the same individual mutations . Coupled changes are associated with divergence of two complexes that were previously implicated both in transcription and in mRNA degradation ( Rpb4/7 and Ccr4-Not ) , as well as with sequence divergence of transcription factor binding motifs . These results suggest that an opposite coupling between the regulation of transcription and that of mRNA degradation has shaped the evolution of gene regulation in yeast .
Work on the regulation of mRNA levels has traditionally focused on transcription , although mRNA levels reflect the balance between transcription and mRNA degradation . Recent studies have shown that regulation of mRNA degradation also has a central role in control of gene expression , and in certain systems might be as important as transcription regulation [1]–[10] , underscoring the importance of systematically studying the patterns of mRNA degradation and their regulation . While the basic machinery of mRNA degradation is well established [2] , [11] , very little is known regarding gene-specific and condition-specific regulation by RNA-binding proteins ( RBPs ) , which bind to subsets of mRNAs and coordinate their post-transcriptional regulation [12] , [13] . Notably , hundreds of RBPs are predicted in each eukaryotic genome , yet the subsets of bound mRNAs and the impact on mRNA degradation are known only for a selected few [14]–[18] . Although both transcription and mRNA degradation individually contribute to the regulation of mRNA levels , they are ultimately integrated to form a coherent regulatory system , and several studies provided evidence for crosstalk between the regulation of transcription and mRNA degradation . First , two conserved and general regulatory complexes , the Rpb4/7 dimmer , which is composed of two subunits of RNA polymerase II [19] , and the Ccr4-Not complex [20] , [21] , have been shown to control both transcription and mRNA degradation and thus may serve to coordinate their regulation . Second , recent work in the fission yeast has described a feed-forward loop whereby a transcription factor activates a regulator of mRNA degradation and both factors jointly control the expression of a common subset of genes [22] . Such interplay between factors that control transcription and mRNA degradation might in fact be a common property of regulatory networks [23] . Third , several studies examined the response of S . cerevisiae to environmental perturbations and found coordinated changes in mRNA degradation and transcription [5] , [6] , [8] , [9] , [24] . For example , Shalem et al . [24] found that transcriptional regulation is coordinated with changes in mRNA stability and that the mode of this coordination is condition-dependent , such that induced genes are stabilized in one condition ( during DNA damage ) and destabilized in another ( during oxidative stress ) . Taken together , these observations suggest that transcription and mRNA degradation are often coordinated . However , this coordination remains poorly understood , raising several important questions . What is the scope of this coordination ? What mechanisms underlie this coordination and are they directly or indirectly influencing both processes ? What is the mode of coordination—is transcriptional induction mostly coordinated with decreased degradation , increased degradation , or both ? What is the functional significance of such coordination ? To address these questions , we set out to examine the coordination between transcription and mRNA degradation from an evolutionary perspective , by comparing two closely related yeast species , S . cerevisiae and S . paradoxus . These species diverged from a common ancestor ∼5–10 million years ago and maintained similar physiology and genomic sequences ( ∼90% identity ) , yet as we have shown previously [25] , most of their orthologous genes have diverged in mRNA levels . Comparing the mRNA degradation rates of these species , we find significant differences at ∼11% of the orthologs . Remarkably , around half of these evolutionary differences in mRNA degradation are coupled to evolutionary differences in transcription , indicating a widespread coordination . This coordination involves almost exclusively opposite effects of transcription and degradation such that transcriptional induction is coupled to increased mRNA degradation . Furthermore , classification of transcription and degradation changes into cis and trans , by allele-specific analysis of the interspecific hybrid , suggests a direct mechanistic coupling whereby individual mutations influence both transcription and mRNA degradation . These mutations seem to involve Rpb4/7 , Ccr4-Not , as well as additional unknown factors .
To compare the mRNA degradation rates of the two species , we monitored mRNA levels following transcriptional arrest using 1 , 10-Phenantroline [7] , [26] . mRNA levels were measured at 0 , 20 , 40 , and 60 min after addition of the drug using a two-species microarray [25] . As expected , the profiles of most genes were well approximated by an exponential decay , which is reflected by a linear decrease of the log2 mRNA levels with time ( Figure 1a ) . Degradation rates were estimated as the slope of the linear fit for 78% of the genes that had an R2 value ( goodness-of-fit ) above 0 . 94 , while genes with lower R2 were excluded from further analysis . The calculated mRNA degradation rates of S . cerevisiae genes were highly reproducible among two biological repeats and between probes that were designed for different positions of the same genes , and were consistent with previous measurements of mRNA degradation that utilized a PolII mutant strain to block transcription ( Figure 1b ) [24] . Degradation rates were largely conserved among the two yeast species , with a genome-wide correlation of 0 . 78 ( Figure 1c ) , yet we identified considerable differences at ∼11% of the orthologs , in which the difference was both statistically significant ( p<0 . 05 ) and above 1 . 4-fold ( i . e . , the higher degradation rate exceeded the lower degradation rate by at least 40% , see Figure S1 for results with other thresholds ) . Differential mRNA degradation rates of six genes were validated by real-time PCR ( Figure S2 ) . These results indicate that , even among such closely related species , considerable differences in mRNA degradation rates are common , although much less common than differences in mRNA levels , which were observed for approximately half of the genes in this and in previous work ( Figure S1 ) [25] . Differential degradation was observed for genes with various functions but was particularly enriched among respiration-related genes . Notably , degradation rates of these genes were consistently higher in S . paradoxus than in S . cerevisiae , as shown in Figure 1d for the 12 oxidative phosphorylation genes included in our analysis . We next turned to systematically compare the changes in mRNA degradation rates to the changes in mRNA levels , as measured here in the zero time-point ( before transcription arrest ) , or in a previous work [25] . Sorting the genes by the degree of inter-species differential degradation rate , we observed that differential degradation is associated with inter-species differential mRNA level ( Figure 2a ) . This might seem expected , as mRNA levels are partially determined by mRNA degradation . Surprisingly , however , the direction of differences in mRNA levels is opposite to that expected purely from the difference in mRNA degradation: genes with higher mRNA degradation rate in one of the species tend to have higher mRNA levels in that species , although the increased degradation would be expected to decrease their mRNA levels ( Figure 2b ) . This indicates that apart from the differences in degradation rates , there are also differences in the transcription rates of these genes that exert opposite effects on mRNA levels . For example , oxidative phosphorylation genes have significantly faster mRNA degradation in S . paradoxus than in S . cerevisiae , yet 11 out of 12 of these genes in fact have significantly higher mRNA level in S . paradoxus than in S . cerevisiae ( Figure 2b , blue dots ) . Strikingly , in close to 80% of the genes with differential mRNA level and differential degradation , the difference in mRNA level is opposite to that expected from the difference in mRNA degradation , thus implying opposing effects of transcription and degradation ( red section in Figure 2c ) . Technical biases do not seem to have a significant effect on the observed coupling . First , the coupling is observed for large differences in mRNA degradation ( red section ) , but not for genes with very small changes in degradation , which are more dependent on technical variations ( green section in Figure 2c ) . Second , we used different datasets to compute mRNA levels and mRNA degradation , thus avoiding potential artifacts that might generate the observed coupling . Third , our microarray contains different probes for the same genes with widely different hybridization intensities ( which serve to calculate mRNA levels ) , but these differences do not affect the estimation of mRNA degradation rates ( see Materials and Methods ) . Fourth , the observed coupling cannot be accounted by microarray artifacts or residual transcription ( see Methods and Figure S3 ) . Notably , the above analysis in fact underestimates the scope of the coupling between transcription and mRNA degradation , since mRNA levels are used instead of transcription rates . For example , some genes displayed a difference in mRNA degradation rates but no significant difference in mRNA levels ( e . g . , PRP9 , see Figure 2b ) . This again implies an opposite difference in transcription that compensates for the difference in mRNA degradation ( thus resulting in similar mRNA levels in the two species ) , yet these genes were not considered in our previous analysis . To account for this effect we can estimate the transcription rates of the two species by integrating the measures of mRNA levels and degradation ( see Materials and Methods ) . This analysis indeed increases the proportion of coupled genes ( gray curves in Figure 2a , c ) , although calculated transcription rates should be taken with caution and may artificially overestimate the coupling ( see Materials and Methods ) . We thus predict the true scope of opposite coupling to be within the range indicated by analysis of mRNA levels and that of estimated transcription rates ( e . g . , among genes that differ both in transcription and in mRNA degradation ∼80%–90% have opposite effects; see Figure 2c ) . Nevertheless , in subsequent analyses we took a conservative approach and considered coupling only among those genes identified by both mRNA levels and estimated transcription rates . Taken together , a large fraction of the evolutionary changes in mRNA degradation were coupled to opposite evolutionary changes in transcription ( 44%–80% , as derived from our conservative and relaxed analyses , respectively; see Figure 2d ) . Note , however , that this coupling constitutes only 10%–20% of the evolutionary changes in transcription ( Figure 2d ) , as transcriptional changes were much more frequent and typically independent of those in mRNA degradation; this might explain why previous studies failed to notice such coupling . Transcription and mRNA degradation are controlled by different mechanisms and are thus expected to diverge through a separate set of mutations . However , the strong coupling that we observe suggests the intriguing possibility that individual mutations may influence both transcription and mRNA degradation , generating opposing effects on mRNA levels . Although we cannot identify the effect of individual mutations , this possibility can be examined by differentiating between the contributions of cis- and trans-mutations to evolutionary changes in mRNA degradation and transcription . Cis-mutations occur within the affected gene or in its flanking regulatory sequences ( e . g . , promoter or 3′-UTR motifs ) , while trans-mutations occur in other loci and indirectly influence the affected gene through the activity of another protein ( e . g . , RNA-binding protein ) . Importantly , the genome-wide contributions of cis- and trans-mutations can be uncovered by analysis of inter-species hybrids: cis-mutations discriminate between two hybrid alleles that reflect orthologs from the two species , while trans-mutations do not discriminate between the two hybrid alleles , as the alleles are in the same nucleus and thus exposed to the same set of trans-regulators . This approach has previously been used to assess the contribution of cis- and trans-mutations to total mRNA levels [25] , [27]–[29] and recently also to nucleosome positioning [30] , while here we extend it to study mRNA degradation rates . We measured allele-specific mRNA degradation rates for the hybrid of S . cerevisiae and S . paradoxus , with two biological repeats and using the same method as described above for the two species . For each gene whose mRNA degradation rate differs between the species , we examined whether this difference is maintained ( cis ) or abolished ( trans ) between the corresponding two hybrid alleles . This analysis indicated that ∼60% of the differences in mRNA degradation reflect primarily cis-mutations , while ∼40% reflect trans-mutations ( Figure 3 ) . Six cis-differences were further validated by real-time PCR of the hybrid alleles ( Figure S2 ) . If coupled changes in transcription and degradation are due to independent mutations , then each change can be either in cis or in trans , and thus the coupling should be observed for all combination of cis- and trans-effects; for example , cis-effects in mRNA degradation should be coupled both to cis-effects in transcription ( cis-cis combination ) and to trans-effects in transcription ( cis-trans combination ) . However , if transcription and degradation changes are mechanistically coupled and the observed opposite effects are generated by the same individual mutations , then these coupled changes would be generated by a single effect , either in cis ( cis-cis combination ) or in trans ( trans-trans combination ) , but not by cis-trans or trans-cis combinations . Consistent with this , a strong coupling is observed only for cis-cis and trans-trans combinations but not for cis-trans or trans-cis combinations ( Figure 4 ) . Coupling between trans-changes in mRNA degradation and trans-changes in transcription ( trans-coupling ) suggests that divergence of upstream regulator ( s ) has influenced both processes . We thus searched for enrichment of 85 high-confidence trans-coupled genes with targets of 116 transcription factors ( TFs ) [31] , 46 RNA-binding proteins ( RBPs ) [14] , [16] , Rpb4/7 [32] , and Ccr4-Not [33] . Fifteen of the 173 target gene-sets were enriched ( p<0 . 05 ) among the trans-coupled genes compared to uncoupled genes ( Figure 5a ) . Notably , these include an Rpb4/7 dataset ( Rpb4 [32] ) and three datasets of Ccr4-Not ( Ccr4 , Not5 , Caf1 [33] ) , which were among the five most enriched datasets . Furthermore , while the combined target gene-sets of Rpb4/7 and Ccr4-Not include only 12% of all genes examined here and 18% of the genes with uncoupled transcriptional changes , they include 41% of the trans-coupled genes ( p = 2×10−7 ) . Thus , our results are consistent with previous studies showing that these two complexes influence both transcription and mRNA degradation . Target gene-sets of nine TFs and two RBPs were also enriched with trans-coupled genes ( Figure 5a ) . However , excluding the targets of Rpb4/7 and Ccr4-Not completely abolished the enrichment of four of these TFs ( Figure 5b ) , suggesting that their enrichment was due to high overlap with targets of Rpb4/7 and Ccr4-Not and may not reflect the function of these TFs . The remaining enriched gene-sets included targets of three TFs involved in respiration ( Hap1 , Hap4 , and Hap5 ) , two TFs involved in amino-acid biosynthesis ( Gln3 , Met31 ) , the poly ( A ) binding protein ( Pab1 ) , and the SR-like protein Npl3 . Interestingly , both Pab1 [34] and Npl3 [35] are known to shuttle between the nucleus and the cytoplasm , Npl3 was previously implicated in regulation of transcription [36] and translation [37] , and Pab1 was previously implicated in regulation of mRNA degradation [38] . These results suggest that , in addition to Rbp4/7 and Ccr4-Not , coordination between transcription and mRNA degradation may also involve Pab1 and Npl3 . The enrichment of trans-coupled genes among targets of specific regulators suggests not only that these regulators control both transcription and mRNA degradation , but also that the activity of these regulators diverged among the two species . Consistent with this possibility , the expression level of Rpb4 is ∼3-fold higher in S . paradoxus than in S . cerevisiae , while the expression of other RNA Pol II subunits is much more conserved ( Figure S4 ) . Increased activity of Rpb4/7 in S . paradoxus would be expected to increase both transcription and mRNA degradation in S . paradoxus ( compared to S . cerevisiae ) , and indeed we find that targets of Rpb4/7 are highly enriched among coupled trans-effects with higher S . paradoxus transcription and degradation but not among those with higher S . cerevisiae transcription and degradation ( Figure S4 ) . Coupling between cis-changes in mRNA degradation and cis-changes in transcription ( cis-coupling ) suggests that mutations in a gene's promoter , coding-region , terminator or untranslated regions influenced both processes . This may reflect mutations that affect the recruitment of specific proteins to the loci of that gene , which then influence both transcription in the nucleus and degradation of the resulting mRNA following its export to the cytoplasm . To examine this possibility , we first searched for enrichment of 92 high-confidence cis-coupled genes with targets of the various regulators , as described above for the trans-coupled genes . Only one of the 170 datasets was enriched among the cis-coupled genes with a p value below 0 . 01 ( Figure 5c ) . This dataset included genes upregulated upon deletion of Rpb4 and was significantly enriched with cis-coupling ( p = 7×10−5 ) , suggesting that cis-mutations may have influenced the recruitment of Rpb4/7 to many genes . At a p value of 0 . 05 , only one additional target gene-set was enriched ( Hap3 ) , while ∼9 sets would be expected by pure chance ( 0 . 05×173 ) . Despite the significant enrichment of Rpb4/7 targets , these include only 13% of the cis-coupled genes , suggesting the existence of other mechanisms for cis-coupling . We next examined the sequence divergence between S . cerevisiae and S . paradoxus at various predicted and known cis-regulatory elements . Analysis of diverged 3′-UTR sequences that were predicted to influence mRNA stability [15] or to be bound by RNA-binding proteins ( RBPs ) [14] , [16] did not identify a significant association with cis-coupled genes ( Figure 5d ) . In contrast , diverged transcription factor ( TF ) binding sites [39] were significantly enriched at cis-coupled genes , compared to uncoupled genes that diverged only in transcription ( Figure 5d , p<10−3 ) . This enrichment was found both for known S . cerevisiae TF binding sites [31] that are not conserved in S . paradoxus and for predicted S . paradoxus TF binding sites that are not conserved in S . cerevisiae ( Figure 5d ) . Notably , diverged TF binding sites were enriched at cis-coupled target genes of Rpb4/7 , suggesting that these mutations may have influenced the recruitment of Rpb4/7 , but also at cis-coupled genes not targeted by Rpb4/7 , implying that the effect of these mutations on transcription and mRNA degradation is also mediated by additional mechanisms . This analysis of diverged binding sites included various TFs and we could not detect any TF with specific overrepresentation . As expected , diverged TF binding sites were not enriched among trans-coupled genes ( Figure 5e ) , further supporting their direct association with cis-coupling .
The association of trans-coupled genes with Rpb4/7 and Ccr4-Not suggests that altered activity of these complexes influenced , in parallel , both transcription and mRNA degradation of target genes . This possibility of parallel coupling ( see Figure 6 ) , whereby an upstream regulator controls multiple regulatory steps and may coordinate them , is consistent with known functions of Rpb4/7 and Ccr4-Not and , more generally , with the notion that RBPs often coordinate multiple steps in the regulation of their target genes [13] . Trans-coupling is also associated with two other RBPs known to shuttle between the nucleus and the cytoplasm ( Pab1 and Npl3 ) , suggesting that these may also serve as coordinators of transcription and mRNA degradation , and possibly of additional steps . Notably , divergence of individual trans-regulators can cause similar evolutionary changes across many co-regulated target genes . Indeed , trans-coupling includes a set of respiration-related genes , all with higher transcription and mRNA degradation rates in S . paradoxus than in S . cerevisiae , likely reflecting a module that coherently diverged through one or few trans-mutations . While this module is known to be transcriptionally co-regulated , these results suggest that it is also post-transcriptionally co-regulated , thus representing an “RNA regulon” [13] . Divergence of this module may have been part of the domestication of S . cerevisiae and an associated optimization of anaerobic fermentation [40] . Notably , although high-confidence trans-coupled genes are highly enriched with the respiration module ( p = 10−10 ) , this enrichment accounts only for a quarter ( 21/85 ) of these genes , suggesting that additional RNA regulons might have evolved by parallel ( and opposite ) changes in their transcription and mRNA degradation . While trans-regulators may affect transcription and mRNA degradation in parallel , cis-acting sequences are likely to be more specific to one of these processes , for example , by mediating the binding of TFs to promoters or that of RBPs to mRNAs . We thus propose that cis-coupling may work by sequential coupling ( Figure 6 ) , whereby mutated cis-acting elements affect one process ( transcription or degradation ) and this in turn signals to the other process , thereby causing an additional effect . The enrichment of cis-coupling with diverged TF motifs , but not RBP ( i . e . , stability ) motifs , suggests a mode of sequential coupling that is directed from transcription to mRNA degradation . This possibility is consistent with a shuttling mechanism , as previously proposed for Rpb4/7 [19] , whereby transcription-related molecules bind to the transcribed mRNA and are exported with it to the cytoplasm where they influence its degradation . Rpb4/7 targets are indeed enriched among cis-coupled genes , but this accounts only for a small proportion of cis-coupling , suggesting the existence of additional factors for sequential coupling by a similar shuttling mechanism or by other mechanisms . Alternatively , the enrichment of TF motifs , but not stability motifs , may reflect the bias in current knowledge , as fewer motifs are known for RNA-binding proteins and these may rely more heavily on structural properties . Sequential coupling may thus initiate by binding of RBPs to yet unknown motifs and regulate mRNA degradation , followed by signaling back to the nucleus that influences transcription of that gene or perhaps of a set of genes . This possibility is consistent with the notion that RBPs are highly inter-connected and coordinate multiple regulatory events [13] . However , the observation that coupling typically involved larger changes in transcription than in mRNA degradation appears to support a transcription-to-degradation directionality . Interestingly , both of these models make the intriguing and testable prediction that experimental manipulation of individual cis-regulatory elements would affect both transcription and mRNA degradation of the associated genes . The results presented here reflect the specific evolutionary divergence of two yeast species and hence might not be sufficient to infer general conclusions regarding the scope and mode of coupling . For example , few trans-mutations may have driven the evolution of many target genes ( e . g . , respiration module ) and by that bias our results . Importantly , however , cis-coupled genes are each affected by distinct sets of mutations; the only exception is of neighboring genes which may diverge through the same mutations in cis , but these encompass only up to 5% of the observed cis-coupled genes . Therefore , our results imply ∼140 independent cases in which cis-acting mutations affected both transcription and mRNA degradation , generating opposite effects on mRNA levels ( Figure 2d ) . At the same time , ∼1 , 700 genes diverged by cis-acting mutations only in transcription , and ∼160 genes diverged by cis-acting mutations only in mRNA degradation ( Figure 2d ) . These results demonstrate that coupling is not a global phenomenon , as it does not affect the majority of genes , nor is it a rare event . It is tempting to further speculate that cis-divergence is not strongly biased towards certain mechanisms and thus that observed patterns of cis-divergence may provide a rough estimate for the frequencies of possible mutational outcomes and regulatory mechanisms . Accordingly , we would predict that ( i ) transcriptional regulation is much more prevalent than regulation of mRNA degradation , although the exact proportion is difficult to quantify as differential mRNA degradation is more difficult to identify than differential transcription; ( ii ) coupling constitutes approximately 10% of the regulation of transcription but almost half of the regulation of mRNA degradation . ( iii ) Coupling occurs almost exclusively between opposite effects on mRNA levels ( increased transcription is associated with increased mRNA degradation and vice versa ) . This last prediction is especially surprising given that previous studies have highlighted a coherent mode of coupling whereby changes in mRNA levels may be driven by both transcription and mRNA degradation acting in the same direction [5] , [7] , [8] , [22] , [41] . These views may be reconciled if one mode ( coherent changes ) reflects coordination of distinct pathways for transcription and mRNA degradation that have co-evolved to support certain responses to environmental perturbations , while the other mode ( opposite changes ) reflects a mechanistic coordination whereby the same pathway affects both processes . Since these closely related species differ in the regulation of approximately half of the genes , and these differences are small in magnitude ( ∼1 . 5-fold ) , we suspect that they primarily reflect neutral drift and as such they expose the mechanistic ( opposite ) coupling that is presumably “built in” to regulatory mechanisms , but does not reveal coherent coupling as these primarily evolved prior to the divergence of these species and may not be continuously evolving . This proposed mode of opposite coupling appears counterintuitive and inefficient , as transcription and degradation effects would compensate one another . What then may be the benefits of such coupling ? One possibility is that an opposite coupling may enable transient responses to environmental changes: upon stress conditions , cells cease to grow and mount an transcriptional response , but at the same time increase the degradation rates of upregulated genes , thereby facilitating their return to basal expression levels and normal growth [4] , [24] . Such transient responses may have been particularly important for thriving in fluctuating environments , and coupling mechanisms may have thus become “built-in” components of gene regulation that are active also in the absence of stress and are thus exposed by genetic mutations . Another plausible advantage of such coupling is that it may decrease the effect of genetic or environmental perturbations on mRNA abundance , as changes in one level of regulation would be compensated by another level . Such intrinsic “negative feedback” could increase the robustness of gene regulation and thus reduce cell-to-cell variability . Surprisingly , however , we observe the exact opposite: genes that display coupled evolution in our data or that are targets of coupling mechanisms ( i . e . , Rpb4/7 and Ccr4-Not ) have a considerably higher cell-to-cell variability in protein abundance ( expression noise [42] ) than other genes ( Figure S5 ) . Notably , this effect is comparable in magnitude to other factors that were previously implicated in increasing noise ( i . e . , TATA-box [43] and promoter nucleosome occupancy [44] ) and remains significant after controlling for these factors . This may indicate that coupling between transcription and mRNA degradation is further associated with additional regulatory effects . Given the recent demonstration that Rpb4/7 also influences translational regulation [45] , and the interplay between mRNA degradation and translation [46]–[48] , it is tempting to speculate that the coupling that we observed is further linked to translation bursts that give rise to high cell-to-cell variability [49] .
To facilitate comparison to the diploid hybrid , we generated diploid homozygote yeast strains of the two species , thus avoiding both potential differences between haploids and diploids , and potential heterozygosity within normal diploid strains , which could confound inter-species comparisons . Diploid homozygote strains were generated from the haploid S . cerevisiae ( BY4741 ) and S . paradoxus ( CBS432 ) strains , by transient HO activation and selection for diploid strains . The hybrid strain was generated by mating the same parental haploids . These three diploid strains ( S . cerevisiae , S . paradoxus , and hybrid ) were grown to log-phase at rich media ( YPD medium at 30°C ) . Two to five different 60-mer probes were designed for most genes in each of the two species , and each probe was placed at two different positions ( duplicates ) on an Agilent custom ( two-species ) microarray . Probes were selected both by general criteria for probe selection ( intermediate %GC , no self-hybridization or low complexity regions , distance from the gene 3′-end ) and by preference for low sequence similarity between the two species in order to avoid cross-hybridization ( all probes reflect genomic positions with lower than 90% sequence identity between the two species ) . S . cerevisiae , S . paradoxus , and their hybrid were subjected to 150 µg/ml of 1 , 10-phenanthroline at log-phase and sampled after 0 , 20 , 40 , and 60 min . Total RNA was extracted using MasterPure Yeast RNA purification Kit ( EPICENTRE ) , amplified with Agilent's Low RNA Input Amplification Kit and hybridized with Agilent's standard protocols and kits to the two-species microarrays . S . cerevisiae and S . paradoxus samples were pooled and hybridized together and the hybrid was hybridized separately , both with biological repeats . Arrays were scanned using Agilent microarray scanner and feature extraction software . Raw and processed microarray data are available at the GEO database ( GSE28849 ) . During the time-course , transcription is arrested and total mRNA levels are decreasing , but this decrease is masked by the experimental protocol , as equivalent amounts of total RNA are extracted from each sample . Previous studies that used a PolII mutant strain could circumvent this problem since mRNAs constitute only a minor fraction of the total RNAs in a yeast cell , and the transcription of other RNAs ( by PolI and PolIII ) was not inhibited [3] , [24] . However , Phenanthroline appears to inhibit all three RNA polymerases to approximately the same extent and we did not detect a decrease in the relative levels of mRNA ( unpublished data ) . We therefore decided to scale the entire data at each time point according to an overall exponential decay with half-life of 25 min , consistent with previous studies [3] , [24] . Accordingly , log2 of the total ( or average ) abundance of all mRNAs should decrease linearly by 1 unit every 25 min , and thus decrease by 0 . 8 every 20 min ( the interval between consecutive time-points ) . We thus scaled the data by centering the four consecutive time points ( 0 , 20 min , 40 min , and 60 min ) on 0 , −0 . 8 , −1 . 6 , and −2 . 4 , respectively . For each probe , we averaged the hybridization intensities from the duplicate microarray spots , and fitted a linear slope to the log2-intensities . All probes with an R2 value smaller than 0 . 94 were excluded from further analysis . For each gene , the absolute value of the median slope of all remaining probes was defined as its degradation rate . Since the four time-points are evenly spaced ( 0 , 20 , 40 , and 60 min ) the difference between mRNA levels at consecutive time-points should be approximately constant and reflect the mRNA degradation rates . To identify differential degradation rates among orthologous probes , we thus performed a two-sample t test , comparing the three estimates of each probe ( M20–M0 , M40–M20 , and M60–M40 , where Mi is the mRNA level at time i ) between the two species . Genes for which the median p value from the t tests of the different probes was below 0 . 05 were further examined . p values reflect both the degree of differential degradation and the consistency among the three estimates ( even a negligible difference can be identified as significant if the three measures are highly similar within each species ) . We thus further examined the extent of differential degradation and retained only those genes in which the ratio between the faster and lower degradation rates ( from the two species ) is higher than 1 . 4 . The first time-point reflects mRNA levels during exponential growth and before transcriptional arrest . It therefore reflects an approximate steady-state mRNA level . A potential caveat is that if the first time-point is used to measure both mRNA levels and mRNA degradation , then measurement errors could generate artificial coupling between mRNA levels and degradation . For example , if the first time point is increased due to technical noise , then estimates of both mRNA level and mRNA degradation would increase and result in apparent coupling . To avoid this problem , we used only one time-course to derive estimates of mRNA degradation rates and the first time-point of the second time-course to derive an estimate of mRNA level . As additional control , we used mRNA levels as measured in a previous work and obtained similar results ( unpublished data ) [25] . Differential expression was defined as above 1 . 5-fold difference between the species ( or hybrid alleles ) . For each gene , we assume that the production rate of mRNAs ( transcription rate ) is approximately equal to the overall degradation of mRNAs , and therefore given by the steady-state level of mRNAs multiplied by their constant degradation rate . Hence , TR = D×L , where TR , D , and L are the transcription rate , degradation rate , and mRNA level , respectively . The difference in transcription rates between S . cerevisiae and S . paradoxus can thus be estimated from the respective differences of degradation rates and mRNA levels: log ( TRcer/TRpar ) = log ( Dcer/Dpar ) +log ( Lcer/Lpar ) . We note that this estimation may not be accurate as a result of possible violation of the steady-state assumption , spurious correlations with mRNA degradation due to the method of calculation , and the integration of nuclear and cytoplasmic mRNAs in our measurements . Our main conclusions do not require these estimates of transcription rates and can be inferred from direct comparison of inter-species differences in mRNA degradation to those in mRNA levels . However , since mRNA levels are inherently affected by mRNA degradation in a manner that is opposite to the observed coupling , such analysis would underestimate the scope of the coupling ( as illustrated in Figure 2c by PRP9 ) . We thus argue that analysis of mRNA levels underestimates the scope of the coupling , while analysis of estimated transcription rates may overestimate it and that the two analyses are complementary . Nevertheless , we defined coupled genes for further analysis based on consensus of mRNA levels and transcription rates analyses in order to avoid cases of spurious coupling . Our experimental design may be susceptible to two confounding effects . First , the use of two-species microarrays , whereby the two species are co-hybridized to a single array that contains species-specific probes , may result in cross-hybridization such that mRNA from one species hybridizes to probes of the other species . Second , inhibition of transcription with 1 , 10-phenanthroline may not be enough to completely block transcription and residual transcription activity may hinder our calculation of mRNA degradation rates . However , as described below , both of these effects are likely to have only a minor influence on our results and , in particular , are not expected to cause the observed coupling between transcription and mRNA degradation . Classification into cis and trans is based on whether the inter-species difference in mRNA degradation rates ( Δspecies ) is retained ( cis ) or abolished ( trans ) within the hybrid ( Δhybrid ) , while intermediate cases are excluded from the analysis . Cis changes were defined as significant inter-species differences for which Δhybrid has the same sign as Δspecies and is larger than 1 . 2-fold for each of the two repeats , and the residuals ( Δhybrid–Δspecies ) are smaller than 1 . 3-fold . Trans changes were defined as significant inter-species differences for which Δhybrid has either a different sign than Δspecies or is smaller than 1 . 2-fold for each of the two repeats , and the residuals ( Δhybrid–Δspecies ) are larger than 1 . 3-fold . This definition is clearly threshold dependent , and other thresholds or criteria that we used led to similar proportions of cis and trans changes , typically with the percentage of cis differences between 50% and 75% ( unpublished data ) . High-confidence sets of cis/trans-coupled genes were defined as those with a significant cis/trans mRNA degradation difference above 1 . 5-fold and a cis/trans mRNA level difference above 1 . 5-fold ( in the opposite direction to that expected by the degradation difference ) . Targets of 116 TFs were defined based on Chromatin Immuno-precipitation and sequence analysis , taken from MacIsaac et al . [31] ( p<0 . 005 and no conservation criteria ) . Targets of RNA-binding proteins were defined based on RNA Immuno-precipitation , taken from Hogan et al . [14] . Targets of seven subunits of Ccr4-Not were defined as genes whose expression decreased by at least 2-fold upon deletion of the respective subunits in rich media [33] . Targets of Rpb4/7 were defined as genes whose expression decreased by at least 2-fold upon deletion of Rpb4 in rich media [32] . TF binding motifs were taken from MacIsaac et al . [31] ( p<0 . 005 and no conservation criteria ) . Diverged binding sites were defined as follows: Total RNA was extracted with the MasterPure Yeast RNA purification Kit ( EPICENTRE ) . One microgram of each RNA sample was reverse transcribed with Moloney murine leukemia virus reverse transcriptase ( Promega , Madison , WI ) and random hexamer primers ( Applied Biosystems ) . Real-time PCR was performed with StepOne real-time PCR machine ( Applied Biosystems , Foster City , CA ) with Syber Green PCR supermix ( Invitrogen ) . The primers used are described in Table S1 . | The regulation of mRNA levels in the cell is important to ensure , for instance , timely cellular responses to changes in the environment . mRNA transcription and mRNA degradation directly affect mRNA levels and it would make sense to have a system in place that would coordinate these opposing processes . Previous studies suggested that regulation of transcription in the nucleus may be linked to regulation of mRNA degradation in the cytoplasm , yet the details of this connection are poorly understood . In this study , we took an evolutionary approach to address this question by comparing both transcription and mRNA degradation between two yeast species . We found that evolution of these distinct processes is coordinated , as genes that diverged in mRNA degradation tend to also diverge in transcription . Interestingly , the coordination is counterproductive , as increased transcription is linked to increased mRNA degradation . We analyzed a hybrid between the two yeast species to classify evolutionary differences according to the type of underlying mutation ( cis or trans ) . This analysis indicated that coordinated changes in transcription and mRNA degradation are likely to be driven by the same individual mutations , and thus directly coupled . Finally , we suggest several mechanisms that may mediate this coupling , including complexes which are involved in both processes ( Rpb4/7 and Ccr4-Not ) and promoter regulatory regions . These results suggest that a direct coupling between the regulation of transcription and mRNA degradation is a common phenomenon employed by approximately 10% of the genes in yeast . | [
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] | 2011 | Coupled Evolution of Transcription and mRNA Degradation |
The nucleolus has shown to be integral for many processes related to cell growth and proliferation . Stem cells in particular are likely to depend upon nucleolus-based processes to remain in a proliferative state . A highly conserved nucleolar factor named nucleostemin is proposed to be a critical link between nucleolar function and stem-cell–specific processes . Currently , it is unclear whether nucleostemin modulates proliferation by affecting ribosome biogenesis or by another nucleolus-based activity that is specific to stem cells and/or highly proliferating cells . Here , we investigate nucleostemin ( nst-1 ) in the nematode C . elegans , which enables us to examine nst-1 function during both proliferation and differentiation in vivo . Like mammalian nucleostemin , the NST-1 protein is localized to the nucleolus and the nucleoplasm; however , its expression is found in both differentiated and proliferating cells . Global loss of C . elegans nucleostemin ( nst-1 ) leads to a larval arrest phenotype due to a growth defect in the soma , while loss of nst-1 specifically in the germ line causes germline stem cells to undergo a cell cycle arrest . nst-1 mutants exhibit reduced levels of rRNAs , suggesting defects in ribosome biogenesis . However , NST-1 is generally not present in regions of the nucleolus where rRNA transcription and processing occurs , so this reduction is likely secondary to a different defect in ribosome biogenesis . Transgenic studies indicate that NST-1 requires its N-terminal domain for stable expression and both its G1 GTPase and intermediate domains for proper germ line function . Our data support a role for C . elegans nucleostemin in cell growth and proliferation by promoting ribosome biogenesis .
The nucleolus is a dynamic structure to which an increasing diversity of functions is ascribed . Previously known primarily as the central site of ribosome subunit biosynthesis , the nucleolus has recently been recognized as a coordination center for many processes related to cell growth and proliferation , in addition to ribosome biogenesis [1] . The nucleolus changes size and appearance in response to metabolic and growth cues received by the cell when in interphase , suggesting that active signaling between external stimuli and the nucleolus exists . It can also serve as a repository for proteins with a wide variety of functions related to cell proliferation and genome integrity . For instance , key cell cycle proteins such as CDC14 are regulated by their coordinated release from the nucleolus at certain stages of the cell cycle [2] . Stem cells in particular are likely to rely on nucleolar regulation of cellular growth and proliferation . Regulation of telomerase activity is critical for the ability of stem cells to undergo self-renewing divisions and human telomerase reverse transcriptase ( hTERT ) can be found in the nucleolus at certain stages of the cell cycle [3] . Additionally , nucleolar mechanisms that permit rapid and robust responses to genotoxic stress are also likely to be especially prominent in stem cells , where maintaining integrity of the genome is of paramount importance [4] . For example , in response to DNA damage , RNA polymerase I activity in the nucleolus is down-regulated [5] , and proteins that inhibit p53 are sequestered in the nucleolus [6] . Stem cells are delicately balanced between division and differentiation , and the nucleolus is a convenient place to hold transcription factors that affect differentiation . For instance , the transcription factor Hand1 is sequestered in the nucleolus of trophoblast stem cells , and its release directs those cells to differentiate into giant cells [7] . Finally , Polycomb factors are sequestered in the nucleolus during Drosophila spermatogenesis to permit differentiation of primary spermatocytes into mature spermatids [8] . A recently identified , highly conserved factor named nucleostemin is a potential link between nucleolar function and stem cell-specific processes [9] . It is preferentially expressed in stem cells and other proliferating cells , and shuttles between the nucleolus and nucleoplasm via its GTPase activity in a cell cycle-dependent manner [10] . Depletion or overexpression of nucleostemin in cell culture impairs normal cell proliferation [9] . Whether nucleostemin modulates some aspect of ribosome biogenesis or whether it has a different function that is more specific to stem cells and other rapidly proliferating cell types , remains unresolved . Mammalian nucleostemin is not present in the portion of the nucleolus where ribosomal RNA synthesis and processing occur [11] , suggesting that it does not have a role in initial aspects of ribosome biogenesis . However , the class of nucleolar GTPases to which nucleostemin belongs includes yeast Nug1 , which acts to export pre-60S ribosomal subunits out of the nucleolus [12] . Additionally , it is not known in which compartment of the cell nucleostemin function is actually required , or whether it is the act of shuttling between the nucleolus and nucleoplasm itself that is the critical activity . If the latter , cargo or associated proteins have remained unidentified . Thus , how nucleostemin might function in the nucleolus in proliferating cells is unknown . We have investigated nucleostemin function in somatic and germline stem cell division in the nematode C . elegans , a multicellular organism that permits the study of nucleostemin during both proliferation and differentiation in vivo . Previous gene expression profiling experiments in our lab indicated that C . elegans nucleostemin , here named nst-1 , is preferentially expressed in proliferating germ cells ( unpublished data ) . We found that global loss of nucleostemin ( nst-1 ) resulted in a larval arrest phenotype and failure of growth . Loss of nst-1 specifically in the germ line , but not the soma , caused germline stem cells to fail to proliferate . Ribosomal RNA production is decreased in nst-1 mutants prior to any obvious defects in the animal , suggesting that nst-1 is required for ribosome biogenesis . NST-1 protein is localized to the nucleolus and the nucleoplasm , but in contrast to mammalian nucleostemin , NST-1 is found in both terminally differentiated , non-cycling cells as well as in proliferating cells . Transgenic studies indicated that NST-1 requires its N-terminal domain for stable expression , and requires both the G1 GTPase and intermediate domains for function in the germ line . Our results suggest that nucleostemin regulates ribosome biogenesis to mediate its effects on cell growth and proliferation .
To functionally characterize nst-1 , we isolated an nst-1 deletion mutant , vr6 , by PCR screening of a mutagenized worm library . The vr6 deletion allele is predicted to produce a truncated protein containing 108 N-terminal amino acids , followed by five novel amino acids before introduction of a stop codon , thus removing both GTPase domains ( Figure 1A , B ) . RT-PCR analysis of nst-1 ( vr6 ) mutants detected only the truncated transcript , at levels that were four-fold reduced compared to wild type ( Figure S1 ) . nst-1 ( vr6 ) homozygous mutants born from heterozygous mothers arrest as young larvae in the L1 or L2 stage of development . The arrested larvae can live over 20 days , which is comparable to wild-type lifespans . The larvae are active and appear to feed normally . Because all nst-1 ( vr6 ) homozygotes are born from heterozygous mothers , any requirement for nst-1 activity in the embryo could be masked by maternally-deposited nst-1 product . In order to determine if nst-1 acts in embryos , we injected adult wild type and nst-1 heterozygous animals with nst-1 dsRNA to deplete both maternal and zygotic nst-1 gene product , and assessed the progeny for possible phenotypes . No embryonic lethality was noted . Instead , the progeny uniformly arrested as L1 or L2 larvae , phenocopying nst-1 ( vr6 ) homozygotes ( Table 1 ) . These results suggest that wild type levels of maternal ( or zygotic ) nst-1 are not essential for embryonic development , but are necessary for larval development . Moreover , this observation confirms that the deletion mutant phenotype is most likely due to the nst-1 lesion and not an independent background mutation . Based on the RT-PCR data and the fact that nst-1 ( vr6 ) , nst-1 ( RNAi ) , and nst-1 ( RNAi ) ; nst-1 ( vr6 ) /mIn1 animals all display similar phenotypes , we suggest that the nst-1 ( vr6 ) deletion represents a strong loss-of-function or null mutation . To determine a potential cause for the larval arrest , we asked if the nst-1 ( vr6 ) mutants exhibited a growth defect . We measured the length of nst-1 ( vr6 ) and wild type larvae from head to tail at multiple timepoints during larval development , beginning immediately after hatching as previously described [13] . The genotypes were indistinguishable in length for the first four hours of post-embryonic development , after which point nst-1 ( vr6 ) larvae failed to grow significantly ( Figure 1C; n = 6 ) . This growth defect precedes essentially all postembryonic cell divisions , suggesting that it is not solely due to defective larval cell divisions , but also a failure of existing cells to support growth . To ascertain whether nst-1 ( vr6 ) mutants undergo any normal larval blast cell divisions , we looked at the division of intestinal cells . At hatching , wild-type animals have 20 intestinal cells , 14 of which divide at the L1 molt resulting in 34 intestinal nuclei . nst-1 ( vr6 ) mutants also have 20 intestinal cells at hatching , but these cells do not divide at the L1 molt as seen in controls ( 20 versus 31 cells at 16 hours post hatching; n≥9 ) . We next examined the division of germ cells in nst-1 ( vr6 ) mutants during L1 development . In order to distinguish germ cells from somatic cells , we used a strain where P granules are marked by RFP . We found that the precursor germ cells Z2 and Z3 are present in PGL-1::RFP; nst-1 ( vr6 ) mutants similar to controls ( n≥10 ) . Eight hours post hatching , PGL-1::RFP; nst-1 ( vr6 ) animals had significantly less germ cells than control PGL-1::RFP animals ( 3 . 7 versus 7 . 1; p<6 . 34×10−7; n≥7 ) . The reduction in germ cell divisions in PGL-1::RFP; nst-1 ( vr6 ) mutants was also evident at the L1 molt ( 5 . 5 versus 12 in PGL-1::RFP controls; p<1 . 5×10−9; n = 10 ) . Therefore , the nst-1 lesion causes larval arrest due to a defect that occurs very early during post-embryonic development , and which subsequently may impair later larval cell divisions in both the germ line and the soma . Because nucleostemin has a conserved function in modulating cell proliferation [14] , we wanted to examine the effects of loss of nst-1 in the germ line , which contains proliferating germ cells in both larvae and adults . We found that brood size was significantly decreased in nst-1 ( vr6 ) /mIn1 heterozygotes compared to +/mIn1 ( 188 versus 260 , p<1 . 93×10−5 ) or +/+ animals ( 188 versus 291 , p<2 . 11×10−7; n≥11 ) , which might be indicative of impaired proliferation . However , the larval arrest phenotype of nst-1 ( vr6 ) homozygous mutants prior to extensive germ cell proliferation prevents direct analysis of this issue . To overcome this limitation , we took two complementary approaches to rescue the larval arrest in the soma and selectively investigate the nst-1 mutant phenotype in the germ line . In the first approach , we took advantage of the fact that high-copy , repetitive extrachromosomal transgenes are silenced in the germ line , but are still expressed in the soma [15] . We created an extrachromosomal transgenic line , vrEx5 , that expresses wild-type nst-1 under the control of its endogenous regulatory elements . We generated nst-1 ( vr6 ) ; vrEx5 animals , and found that these animals did not arrest as larvae , but instead grew up to become sterile adults ( Table 1 ) . The germ lines of these soma-rescued mutants were severely underproliferated , with only a small percentage containing sperm ( 10 . 5% ) and none containing oocytes at 20°C ( n = 38; Figure 2 ) . In the second approach , we selectively removed nst-1 function from the germ line in otherwise wild-type animals by performing nst-1 ( RNAi ) in rrf-1 ( pk1417 ) mutants , which are RNAi-defective in the soma , but RNAi-competent in the germ line [16] . rrf-1 ( pk1417 ) ; nst-1 ( RNAi ) animals also overcame the larval arrest and reached adulthood , but were sterile with severe defects in proliferation and gamete differentiation in the germ line , similar to the phenotype of nst-1 ( vr6 ) ; vrEx5 animals ( Figure 2 , Table 1 ) . These experiments demonstrate that nst-1 acts in the germ line to modulate germ cell development , but do not rule out the possibility that nst-1 could still have an additional role in somatic tissue ( s ) that control germline stem cell proliferation . We used both soma-rescued backgrounds , nst-1 ( vr6 ) ; vrEx5 and rrf-1 ( pk1417 ) ; nst-1 ( RNAi ) , for subsequent analyses of germ cell defects . First , we determined whether germ cells lacking nst-1 maintained germ granules , a key characteristic of germ cells . Using an antibody to the core germ granule component PGL-1 , we found that germ cells from both the soma-rescued transgenic line , nst-1 ( vr6 ) ; vrEx5 , and from rrf-1 ( pk1417 ) ; nst-1 ( RNAi ) progeny maintained perinuclear punctate staining of PGL-1 similar to nst-1/mIn1; vrEx5 and rrf-1 ( pk1417 ) controls , respectively , suggesting that germ granules are likely to be intact ( data not shown and Figure 3A ) . We next wanted to determine why nst-1 mutants have so few germ cells . Germ cells lacking nst-1 either cannot proliferate , or they have a normal rate of proliferation coupled with excessive cell death by apoptosis or necrosis , possibly induced as a response to stresses inflicted by loss of nst-1 function . We therefore placed the soma-rescued transgenic line , nst-1 ( vr6 ) ; vrEx5 , in a ced-4 ( n1162 ) mutant background , which cannot undergo apoptosis [17] . The presence or absence of ced-4 activity had no apparent effect on the germline phenotype of the nst-1 ( vr6 ) ; vrEx5 animals ( Figure 3B; n≥10 ) . Consistent with our findings , mouse NS−/− embryos do not exhibit abnormal caspase-3 immunostaining [14] . Because prohibiting apoptosis did not increase germ cell number , the germline defects of the soma-rescued nst-1 ( vr6 ) ; vrEx5 animals are likely due to decreased proliferation or some form of non-apoptotic cell death such as necrosis . However , the fairly normal morphology of nst-1 ( vr6 ) germ cells is not consistent with necrosis . Therefore , to examine whether loss of nst-1 results in decreased proliferation , we utilized an antibody against phosphorylated-histone H3 ( pH3 ) , which marks germ cells in late prophase and early mitotic M-phase [18] . We found a significant reduction in the number of α-pH3-positive cells in the rrf-1 ( pk1417 ) ; nst-1 ( RNAi ) animals compared to rrf-1 ( pk1417 ) controls ( Figure 3C , D; n = 6 ) . In agreement with our results , mouse NS −/− embryos also have a reduced number of α-pH3-positive cells compared to controls [14] . We conclude that germ cells lacking nst-1 do not have normal proliferation rates , and because they do not initiate apoptosis or appear to lose cell identity based on normal PGL-1 staining , we suggest that they undergo a cell cycle arrest . Germ cells in both the soma-rescued transgenic line and in the rrf-1 ( pk1417 ) ; nst-1 ( RNAi ) progeny undergo very little differentiation into gametes , with only 10% of the soma-rescued nst-1 ( vr6 ) mutants containing sperm . The failure to differentiate into gametes could be an intrinsic failure of the germ cells to be able to progress through meiosis and differentiation . Another possibility is that the nst-1 ( vr6 ) mutant germ cells remain too close to the distal tip cell , which expresses the LAG-2 ligand that activates GLP-1 signaling in germ cells to prevent differentiation ( Figure S2; n>9 ) [19] . To determine whether nst-1 mutant germ cells were capable of differentiating into sperm if the GLP-1-mediated block to differentiation was removed , we utilized the temperature sensitive glp-1 mutant , glp-1 ( e2141 ) . This mutant is fertile at 15°C , but when shifted to the restrictive temperature of 25°C after hatching , all germ cells prematurely enter meiosis and differentiate into sperm [20] . We placed the glp-1 ( e2141 ) mutation in the background of nst-1 ( vr6 ) ; vrEx5 and analyzed germ cell differentiation . Germ cells lacking nst-1 effectively differentiate into sperm when the block to meiosis is removed , compared to controls retaining one copy of nst-1 in the glp-1 ( e2141 ) mutant background ( 4 . 9±3 . 9 versus 4 . 5±1 . 4 sperm per animal; p = 0 . 578; n>45 ) . Thus , nst-1 ( vr6 ) mutant germs cells are capable of differentiation , but their failure to migrate a sufficient distance from the distal tip cell prevents them from doing so . Mammalian nucleostemin ( NS ) interacts with p53 in pull-down assays [9] and has been shown to modulate the G1/S transition of the cell cycle via the p53 pathway in culture [21] . However , loss of p53 was not sufficient to rescue the embryonic lethality of NS−/− mice [14] , making the relevance of an interaction between NS and p53 in vivo less clear . To determine whether NST-1 might interact with p53 , known as cep-1 in C . elegans , we first tested whether loss of cep-1 could rescue the larval arrest phenotype of nst-1 ( vr6 ) mutants . We found that cep-1 ( gk138 ) ; nst-1 ( vr6 ) mutants still exhibited a larval arrest phenotype ( data not shown ) . We also tested whether cep-1 played a role in the nst-1 germline phenotype , and found that cep-1 ( gk138 ) ; nst-1 ( vr6 ) ; vrEx5 mutants exhibited an under-proliferation phenotype in the germ line very similar to that of nst-1 ( vr6 ) ; vrEx5 mutants alone ( n≥38; data not shown ) . Thus , loss of cep-1/p53 does not have an obvious effect on the nst-1 ( vr6 ) mutant phenotype in either the soma or germ line . This lack of rescue is consistent with the inability of p53 loss to rescue the embryonic lethality of mouse NS [14] . Loss of NST-1 in the soma or germ line causes cell growth and proliferation defects . These phenotypes may be the result of a role for NST-1 in modulating ribosome biogenesis , based on studies of NUG1 , the nucleostemin ortholog in yeast . NUG1 exports pre-60S ribosomal subunits out of the nucleolus and when mutated , cell growth is impaired [12] . To ask whether C . elegans nst-1 has a role in ribosome biogenesis , we examined rRNA abundance in wild type , nst-1 ( vr6 ) , and ncl-1 ( e1865 ) L1 animals . ncl-1 ( e1865 ) mutants exhibit elevated levels of rRNAs [22] and served as a control . We examined young L1 animals prior to the onset of the larval arrest phenotype to avoid effects on ribosome biogenesis that might be downstream consequences of the growth defect . Using gel electrophoresis of equal amounts of total RNA , we consistently saw decreased 18S and 26S rRNA levels , with correspondingly higher levels of tRNA , in nst-1 ( vr6 ) mutants compared to wild type ( Figure 4A ) . rRNA levels in ncl-1 ( e1865 ) mutants appeared higher than in wild type , consistent with published reports [22] . To independently confirm the decreased rRNA levels seen in the nst-1 ( vr6 ) mutant , we performed RT-PCR amplification of rrn-3 . 1 , which encodes a 26S rRNA . Consistent with the total RNA analysis , rrn-3 . 1 levels are significantly lower in nst-1 ( vr6 ) mutants compared to wild-type animals ( 2 . 7-fold , ±0 . 4 , from two independent RT-PCR experiments ) ( Figure 4B , C ) . The decreased rRNA levels seen in the nst-1 ( vr6 ) mutant suggests that ribosome biogenesis is not occurring at normal levels , a defect that possibly underlies the larval arrest phenotype . The decreased rRNA levels in nst-1 ( vr6 ) mutants led us to examine whether nucleoli were aberrant . We found no apparent difference in size ( p<0 . 22; n≥18 ) or morphology of intestinal nucleoli between mutant and wild-type newly-hatched larvae . Additionally , to determine if germ cells lacking nst-1 have aberrant nucleoli , we stained the dissected gonads of rrf-1 ( pk1417 ) ; nst-1 ( RNAi ) progeny with NOP-1/fibrillarin , a specific nucleolar marker . We did not detect any gross differences between the rrf-1 ( pk1417 ) ; nst-1 ( RNAi ) and control rrf-1 ( pk1417 ) progeny ( Figure 4D; n≥7 ) , or in the soma-rescued nst-1 ( vr6 ) ; vrEx5 transgenic line ( data not shown ) . Our result is consistent with the finding that mouse NS−/− mutant embryos have normal nucleolar morphology , based on fibrillarin staining [14] . To determine whether increasing the levels of pre-rRNA might rescue the larval arrest phenotype of nst-1 ( vr6 ) mutants , we generated an nst-1 ( vr6 ) ; ncl-1 ( e1865 ) double mutant . ncl-1 acts as a repressor of rRNA transcription and the ncl-1 ( e1865 ) mutant contains 1 . 6-fold more rRNA than wild type , resulting in larger nucleoli [22] . The nst-1 ( vr6 ) ; ncl-1 ( e1865 ) double mutant still exhibited larval arrest and growth defects comparable to nst-1 ( vr6 ) mutants ( Figure S3; n≥5 ) . We also found that the nucleoli of nst-1 ( vr6 ) ; ncl-1 ( e1865 ) mutants were not statistically different in size from nst-1 ( vr6 ) mutants alone ( p<0 . 114; n≥12 ) . Thus , increasing endogenous pre-rRNA levels does not rescue the defects of loss of nst-1 . These results suggest that nst-1 acts in ribosome biogenesis downstream or independently of ncl-1 . To assess the subcellular localization of NST-1 in vivo , we generated two independent transgenic lines expressing the NST-1 protein fused to GFP ( NST-1::GFP ) under the control of endogenous nst-1 regulatory elements . The use of microparticle bombardment to generate low-copy transgenic lines permitted expression of this transgene in the germ line . Both transgenes can rescue nst-1 ( vr6 ) mutants to fertile adulthood with brood sizes similar to the nst-1 heterozygote , indicating that both somatic and germ line defects were rescued . Both strains showed similar NST-1 expression and localization . During post-embryonic development , NST-1::GFP was ubiquitously expressed in the soma and germ line from L1 larvae to the adult stage ( Figure 5A ) . It is concentrated in the nucleolus and diffusely present in the nucleoplasm , similar to reports of mammalian nucleostemin localization ( Figure 5B ) [9] . It was uniformly expressed in all cells of the adult oogenic germ line until the proximal oocyte , where expression decreased ( Figure 5C ) . Expression was not detected in the early embryo until the ≃18-cell stage , when it again became detectable in the very small nucleolus and diffusely present in the nucleoplasm ( Figure 5D ) . The lack of NST-1::GFP expression in the early embryo is consistent with the fact that rRNA processing and assembly of newly produced ribosomes are not occurring at this time [23] , although rRNA transcription is detectable [24] . In order to determine more precisely the localization of NST-1 within the nucleolus , we examined the co-localization of NST-1::GFP with NOP-1/fibrillarin in the germ line . NOP-1 is a specific marker for the dense fibrillar component and is directly involved in rRNA processing [25] . We observed obvious regions within the nucleolus where NOP-1 was highly expressed and NST-1::GFP was completely absent ( Figure 6 ) . Overlap between NOP-1 and NST-1::GFP occurred primarily in areas where NOP-1 was expressed at relatively low levels , suggesting that NST-1 does not reside in regions of robust rRNA processing . This minimal co-localization is consistent with a failure of mammalian nucleostemin to significantly co-localize with fibrillarin [11] . Although we found that loss of nst-1 results in lower rRNA levels , the absence of NST-1 in regions of the nucleolus where rRNA processing occurs suggests that NST-1 is not likely to play a direct role in rRNA processing and that the effect on rRNA levels may be secondary . NST-1 and mammalian nucleostemin are highly homologous in the predicted functional domains , especially the basic domain and the two GTPase domains , G1 GXXXXGK[S/T] and G4 KXDL ( Figure 1B ) . In order to determine the importance of the functional domains of NST-1 , we made point mutations and/or deletions within the rescuing NST-1::GFP construct and assessed the effects on subcellular localization and the ability to rescue the nst-1 ( vr6 ) mutant . The GTP-binding capacity of mammalian nucleostemin is dependent upon the G1 motif [10] , which is identical between mammals and C . elegans . A single amino acid substitution , G256V , in mammalian nucleostemin decreases its GTP-binding activity in vitro , and causes aberrant localization of nucleostemin and formation of nucleolar aggregates [10] . We inserted the equivalent amino acid substitution in the wild-type rescuing NST-1::GFP construct ( called ΔGTP ) and obtained three independent lines ( Figure 7A ) . We did not observe any changes in subcellular localization: NST-1 ( ΔGTP ) ::GFP still localized normally to the nucleolus and nucleoplasm , and did not form obvious aggregates . However , the spatial distribution of NST-1 ( ΔGTP ) ::GFP was no longer ubiquitous in the germ line but exhibited higher expression in the distal end compared to the proximal end , with an abrupt downregulation at the point of entry into meiosis ( Figure 7B , Table 2 ) . Additionally , expression in the soma became limited to a subset of tissues such as seam cells and body wall muscle ( Table 2 ) . We speculate that the difference in spatial expression of NST-1 may be due to protein de-stabilization , with turnover occurring more rapidly in the proximal region of the germ line . When we crossed the ΔGTP transgene into nst-1 ( vr6 ) mutants , we found that all three lines rescued the larval arrest phenotype of nst-1 ( vr6 ) mutants; however the animals were sterile with severely underproliferated germ lines ( Table 2 ) . The ability of the ΔGTP construct to rescue the somatic growth defects but not the germ cell proliferation defects , despite expression in the distal region of the germ line , argues that this domain is not necessary for NST-1 function in the soma but is necessary in the germ line . In mammals , the N-terminal basic region is important for nucleolar localization of NS; when the region was removed , the protein became more diffuse [9] . We made a similar deletion in NST-1::GFP ( called ΔB ) ( Figure 7A ) . We obtained eleven independent lines and never observed NST-1 ( ΔB ) ::GFP expression ( Table 2 ) . We also generated a construct with both the ΔGTP and ΔB deletions , and could not detect any NST-1 expression in five lines ( Figure 7A , Table 2 ) . These data suggest that lack of the basic region renders the protein unstable . When the intermediate domain was removed from NST-1:GFP ( called ΔI ) , we again observed that subcellular localization was unaffected , and the spatial expression of NST-1 was higher in the distal end compared to the proximal end , as in NST-1 ( ΔGTP ) ::GFP ( Figure 7A , B ) . The number of cells that expressed NST-1 ( ΔI ) ::GFP in the soma was less than that seen in NST-1 ( ΔGTP ) ::GFP ( Table 2 ) . Two independent lines of NST-1 ( ΔI ) ::GFP were able to rescue the larval arrest phenotype in 22% of nst-1 ( vr6 ) mutants , although this rescue was not nearly as robust compared to the ΔGTP lines ( 61%; Table 2 ) . The few soma-rescued animals were sterile with very few germ cells , indicating that the intermediate domain is required for germ cell proliferation . Its ability to rescue only a fraction of the nst-1 ( vr6 ) mutants may be because NST-1 ( ΔI ) ::GFP had limited expression in somatic cells in larvae , and thus was not expressed in the correct cells to permit rescue . For mammalian NS , simultaneously removing the intermediate domain and inserting the G256V point mutation caused the protein to reside exclusively within the nucleolus [10] . When similar dual mutations were made in NST-1::GFP , the subcellular localization of NST-1 ( ΔGTPΔI ) ::GFP remained normal , with primarily nucleolar and faint nucleoplasmic localization ( Figure 7A , Table 2 ) . Again , the spatial distribution of NST-1 ( ΔGTPΔI ) ::GFP in the germ line was higher in the distal end compared to the proximal end ( Figure 7B ) , and showed moderately restricted expression in the soma , similar to the ΔI lines . However , NST-1 ( ΔGTPΔI ) ::GFP never rescued the larval arrest phenotype of nst-1 ( vr6 ) mutants ( Table 2 ) . We suggest that the combination of the ΔGTP and the ΔI mutations renders NST-1 non-functional in both the soma and the germ line . In order to determine if mammalian NS could rescue nst-1 ( vr6 ) mutants , we generated two independent transgenic lines expressing mNS::GFP under the control of C . elegans endogenous regulatory elements using microparticle bombardment . Both transgenes are extrachromosomal and express detectable protein that is localized to the nucleolus , similar to NST-1::GFP , and the transgenic animals are healthy . However , neither mNS:GFP transgenic line was able to rescue the larval arrest of nst-1 ( vr6 ) mutants . This observation suggests that functional differences exist between mouse and C . elegans nucleostemin .
We have shown by two independent methods that nst-1 ( vr6 ) mutants have significantly reduced rRNA levels . The decreased rRNA levels seen in the nst-1 ( vr6 ) mutant are likely not due to a role for NST-1 processing , because NST-1 is absent from regions of robust rRNA processing within the nucleolus . In yeast , the related protein NUG1 has been shown to export RPL25 . 2 , a pre-60S subunit , out of the nucleolus [12] . We hypothesize that NST-1 also shuttles partially assembled ribosomes in and out of the nucleolus . In the absence of functional NST-1 , the ribosomal subunits would remain within the nucleolus , and ultimately affect global rRNA levels due to a negative feedback mechanism that results in decreased ribosome biogenesis . We attempted to test this possibility by determining whether a GFP-tagged ribosome subunit , RPL-25 . 2 , was restricted to the nucleolus in nst-1 ( vr6 ) mutants . However , even low expression levels of RPL-25 . 2::GFP made the animals too sick to analyze . Mutations in the highly conserved functional domains of NST-1 did not result in changes in subcellular localization seen in mammalian nucleostemin studies . We did however observe alterations in the spatial expression in the germ line for the ΔGTP , ΔI , and ΔGTPΔI mutations in NST-1::GFP . We suggest that the alteration in spatial expression of NST-1 may be due to moderate de-stabilization of the mutant proteins . The NST-1 ( ΔGTP ) ::GFP and NST-1 ( ΔI ) ::GFP transgenic lines are at least partially able to rescue the somatic defects of nst-1 ( vr6 ) mutants , arguing that neither domain is absolutely essential in the soma . The inability of the NST-1 ( ΔGTPΔI ) ::GFP transgenic line to rescue the somatic phenotype of nst-1 ( vr6 ) mutants may be due to incomplete expression , although we believe this is unlikely because it is expressed as broadly as the ΔGTP lines that do rescue this phenotype . Rather , we think it more likely that losing both the G1 GTPase domain and the intermediate domain in combination is more deleterious than loss of either alone . Interestingly , the NST-1 ( ΔGTP ) ::GFP and NST-1 ( ΔI ) ::GFP transgenic lines are not able to rescue the germline defects of nst-1 ( vr6 ) mutants , despite being expressed in the germ line . This observation argues that both domains are necessary for NST-1 function in the germ line , and suggests that the functional domains of NST-1 may have different roles in the soma and germ line . Another possibility is that NST-1 needs to be ubiquitously expressed in the germ line to maintain proper germ cell development . To date , it has been shown that S . cerevisae , S . pombe , and , in this work , C . elegans nucleostemin modulates cell growth and proliferation by regulating ribosome biogenesis . The S . pombe NUG1 homologue Grn1 is involved in 60S biogenesis , and Grn1 mutants have a severe growth defect with a significant reduction in mature rRNA species similar to the C . elegans nst-1 ( vr6 ) mutant phenotype [26] . It has not been elucidated whether mammalian nucleostemin regulates some aspect of ribosome biogenesis , or whether it has diverged to have an additional function that is specific to stem cells or other rapidly proliferating cells . The expression of mammalian nucleostemin is apparently restricted to proliferating cells , but our studies show that in C . elegans , NST-1 is expressed in terminally differentiated cells as well as in proliferating cells . Moreover , the effect of mutation of individual domains on localization and function is different between C . elegans and mammals . This difference in regulation suggests that perhaps the role of nucleostemin is more limited or specialized in mammals . Consistent with this possibility , we were unable to rescue the nst-1 ( vr6 ) mutant phenotype using mouse nucleostemin cDNA , despite having a similar expression pattern and levels to NST-1::GFP , which suggests that there may indeed be functional differences between mouse and C . elegans nucleostemin . Due to the fundamental requirement for translation in living cells , it has been difficult to determine exactly how ribosome biogenesis is specifically connected to cell growth and proliferation . Nucleostemin is potentially a key link between these two processes , as it could regulate the rate of translation to cell growth by modulating the rate of 60S subunit formation in response to cell-extrinsic cues . Further studies directed toward identifying the cargo of nucleostemin and the cell cycle-specific mechanisms controlling its ability to shuttle in and out of the nucleolus will likely shed light on this key question .
Nematode strain maintenance was as described [27] . C . elegans strain N2 was used as the wild type strain in addition to the following variants: LGI , cep-1 ( gk138 ) , gld-1 ( q485 ) ; LGII , nst-1 ( vr6 ) ; LG III , glp-1 ( e2141 ) , ced-4 ( n1162 ) , unc-119 ( ed3 ) , ncl-1 ( e1865 ) , unc-36 ( e251 ) , rrf-1 ( pk1417 ) ; LGV , qIs19 ( lag-2::GFP ) . The unc-119 ( ed3 ) ; nym-2::PGL-1::mRFP strain was a gift of James R . Priess . All experiments were conducted at 20°C unless otherwise indicated . To isolate mutations in K01C8 . 9 ( nst-1 ) , a library of mutagenized worms was screened for deletion alleles by PCR . The deletion library was constructed and screened as described [28] . Deletion breakpoints in nst-1 ( vr6 ) are GTCGCAAAAGCATCGAAACA / TTTTGAACAACACTGAGACC . After deletion mutations were identified , frozen worms from corresponding wells were recovered and homozygous mutants were isolated . Prior to phenotypic and genetic analysis , vr6 was backcrossed to wild type six times to remove background mutations . Due to the larval arrest phenotype , vr6 was balanced with the mIn1[mIs14 dpy-10 ( e128 ) ] chromosome , which marks the pharynx with GFP . RNAi was performed by injection as described [29] . nst-1 dsRNA was prepared by in vitro transcription of PCR products amplified by primers with T7 sites . Primer sequences to amplify a 998 base pair region of nst-1 were: 5′-taatacgactcactatagggCAATTCCCGACAATTGCTTT-3′ and 5′- taatacgactcactatagggGGCCCTTTCACTTTTCTTCC-3′ . The concentration of injected dsRNA was 600–1000 ng/µl . After injection , hermaphrodites were allowed to lay eggs for 24 hours . Only progeny produced after this period were analyzed for larval arrest or sterility by comparing to controls and assessed by DAPI staining . Total RNA from wild type , nst-1 ( vr6 ) , and ncl-1 ( e1865 ) L1-staged animals was extracted using Trizol ( Invitrogen , Carlsbad , CA ) . Equal amounts of total RNA ( 1 µg ) were electrophoresed on a 1% agarose gel and then stained with ethidium bromide . For RT-PCR , total RNA from wild type , nst-1 ( vr6 ) , and ncl-1 ( e1865 ) L1-staged animals was extracted using Trizol ( Invitrogen , Carlsbad , CA ) and samples were DNase treated with DNA-free ( Ambion , Austin , TX ) . 100–150 ng of total RNA from each genotype was reverse transcribed using the Omniscript RT kit ( Qiagen , Valencia , CA ) and gene-specific PCR was performed using primers for rrn-3 . 1 . his-42 was used as a loading control . Samples were electrophoresed on a 2% agarose gel and then stained with ethidium bromide . Band intensities were measured using the spotdenso tool on AlphaEaseFC software ( Alpha Innotech , San Leonardo , CA ) . A genomic fragment containing the entire nst-1 gene and its regulatory sequences ( −497 to +1169 relative to the translational start site ) , a transformation marker Pmyo-3::GFP , which marks body wall muscle , and an empty vector pGEM5Z were co-injected into wild-type animals . Extrachromosomal lines were generated and crossed to the balanced strain nst-1 ( vr6 ) /mIn1 . Animals that are GFP negative in the pharynx ( nst-1 homozygotes ) and GFP positive in the body wall muscles ( transgene positive ) were assessed for larval arrest rescue and germline effects . Gonads were dissected from animals 36–48 hours post L4 and fixed as described with the following antibodies and dilutions: affinity purified rabbit anti-PGL-1 ( 1∶30 , 000 ) ( gift from S . Strome ) [30] , rabbit polyclonal anti-GFP ( 1∶200 ) ( BD Biosciences , San Jose , CA ) , mouse anti-NOP-1/fibrillarin ( 1∶400 ) ( EnCor Biotechnology Inc . , Alachua , FL ) [24] , rabbit polyclonal anti-histone H3 phospho-S10 ( 1∶200 ) ( Upstate , Billerica , MA ) [31] . Samples were incubated at room temperature for 2–3 hours with a fluorescent secondary antibody ( 1∶500 , Molecular Probes , Carlsbad , CA ) . Slides were mounted with anti-fade solution and viewed using a Zeiss Axioplan 2 imaging epifluorescence microscope . The same nst-1 5′ regulatory region and coding region as the somatic rescue transgenic construct ( −497 to +1169 relative to the translational start site ) , the GFP coding region , and a larger nst-1 3′ regulatory region ( +404 from the termination codon ) were PCR amplified and stitched together as previously described [32] . The resulting PCR fragment was cloned into pCR2 . 1 TOPO ( Invitrogen , Carlsbad , CA ) and the unc-119 rescuing genomic fragment with engineered NotI sites was ligated into the construct after NotI digestion . The resulting plasmid was transformed into unc-119 ( ed3 ) animals by microparticle bombardment as described [33] and extrachromosomal lines bearing a large percentage of non-Unc animals were examined for GFP expression . Deletions within the NST-1::GFP construct were made using PCR , and point mutations were inserted using the Gene Tailor Site-Directed mutagenesis system ( Invitrogen , Carlsbad , CA ) . All constructs were sequenced prior to bombardment . Full-length mouse NS cDNA was amplified from RNA extracted from mouse liver . The C . elegans nst-1 regulatory elements and GFP were PCR amplified and stitched together with NS mouse cDNA as previously described [32] . The resulting PCR fragment was cloned into a plasmid containing the unc-119 rescuing genomic fragment . The resulting plasmid was transformed into unc-119 ( ed3 ) animals by microparticle bombardment as described [33] and lines bearing a large percentage of non-Unc animals were examined for GFP expression . | Stem cells are carefully poised between the alternate fates of proliferation and differentiation . The regulation of this choice is a complex one that occurs on many different levels . One major influence controlling this choice derives signals emanating from the nucleolus , which serves dual roles as the site of ribosome biogenesis and as a repository for sequestered key regulatory factors . The nucleolar GTPase nucleostemin has recently been identified as a potential link between stem cell proliferation and nucleolar function , but its exact role in the nucleolus has not been directly addressed in a metazoan . Here , we use the model organism C . elegans to investigate the function of nucleostemin in both differentiated cells and proliferating stem cells . We show that nucleostemin probably acts to regulate ribosome biogenesis , and through this process controls cell proliferation . We also suggest that , at least in C . elegans , the function of nucleostemin is not restricted to proliferating stem cells , but that it also functions in differentiated cells to control cell growth . Our study highlights the complexity of the role of the nucleolus in regulation of cell growth and division . | [
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] | 2008 | C. elegans Nucleostemin Is Required for Larval Growth and Germline Stem Cell Division |
There has been limited knowledge on spatio-temporal epidemiology of zoonotic arctic fox rabies among countries bordering the Arctic , in particular Greenland . Previous molecular epidemiological studies have suggested the occurrence of one particular arctic rabies virus ( RABV ) lineage ( arctic-3 ) , but have been limited by a low number of available samples preventing in-depth high resolution phylogenetic analysis of RABVs at that time . However , an improved knowledge of the evolution , at a molecular level , of the circulating RABVs and a better understanding of the historical perspective of the disease in Greenland is necessary for better direct control measures on the island . These issues have been addressed by investigating the spatio-temporal genetic diversity of arctic RABVs and their reservoir host , the arctic fox , in Greenland using both full and partial genome sequences . Using a unique set of 79 arctic RABV full genome sequences from Greenland , Canada , USA ( Alaska ) and Russia obtained between 1977 and 2014 , a description of the historic context in relation to the genetic diversity of currently circulating RABV in Greenland and neighboring Canadian Northern territories has been provided . The phylogenetic analysis confirmed delineation into four major arctic RABV lineages ( arctic 1–4 ) with viruses from Greenland exclusively grouping into the circumpolar arctic-3 lineage . High resolution analysis enabled distinction of seven geographically distinct subclades ( 3 . I – 3 . VII ) with two subclades containing viruses from both Greenland and Canada . By combining analysis of full length RABV genome sequences and host derived sequences encoding mitochondrial proteins obtained simultaneously from brain tissues of 49 arctic foxes , the interaction of viruses and their hosts was explored in detail . Such an approach can serve as a blueprint for analysis of infectious disease dynamics and virus-host interdependencies . The results showed a fine-scale spatial population structure in Greenland arctic foxes based on mitochondrial sequences , but provided no evidence for independent isolated evolutionary development of RABV in different arctic fox lineages . These data are invaluable to support future initiatives for arctic fox rabies control and elimination in Greenland .
Rabies , an ancient disease known for millennia , is caused by lyssaviruses of the Rhabdoviridae family [1] . The prototypical rabies virus ( RABV ) has a global distribution and the domestic dog is the host reservoir responsible for the vast majority of the estimated 60 , 000 human rabies cases annually [2] . Other RABV host reservoirs in terrestrial wildlife are primarily meso-carnivores . In Arctic regions , RABV is believed to be maintained by the arctic fox ( Vulpes lagopus ) [3] , which has a circumpolar distribution and has uniquely adapted to the extreme climatic and ecologic conditions of this northern environment [4] . The distribution and group size of arctic foxes are strongly influenced by the distribution and density of prey [5] . Notably , arctic foxes have considerably variable home ranges ( 5–120 km² ) than any other meso-carnivore RABV reservoir host [6–8] and can roam over large areas and migrate over extremely long distances [4 , 8] . From an epidemiological point of view , this may be an important factor for the spread of RABV in northern Polar regions where rabies-like diseases have been described for about 150 years [9] , specifically among sledge dogs in Greenland as early as 1859 . However , confirmation of the existence of rabies in Greenland was only provided 100 years later , when Jenkins and Wamberg [10] demonstrated the presence of RABV in dogs and arctic foxes . The disease is considered endemic among the arctic fox population of Greenland [11 , 12] . A recent epidemiological study of arctic fox rabies in Greenland between 1969 and 2011 revealed that the disease flared up every 5–10 years on average , whereby most rabid foxes were reported from southern Greenland [13] . Historically , some properties of arctic RABVs were regarded as “atypical” [14] . However , early genetic virus characterizations based on the nucleoprotein ( N ) gene clearly identified it as RABV but as a separate virus lineage designated as “arctic” [15] . This lineage circulates throughout the circumpolar region including northern regions of North America , Europe , and Asia . More detailed phylogenetic analyses revealed that the arctic RABV variant can be further delineated into at least four distinct groups [16–18] . The arctic-1 lineage , recovered from southern Ontario , Canada , in the late 20th and early 21st centuries , represented the remnants of an epidemic that spread from northern Canada in the mid-1900s; reports of this strain are now rare due to the rabies control program carried out by provincial authorities . The arctic-4 lineage has only ever been recovered from regions of Alaska and viruses of the arctic-2 lineage appear to be restricted to Siberia , the Russian Far East , and Alaska . In contrast , lineage arctic-3 has a circumpolar distribution [17] . Arctic rabies was also detected on the European Svalbard Islands with the prevailing RABV lineage having a closer phylogenetic relationship to those occurring in the polar regions of Russia [19] . Viruses closely related to those of the arctic clade have been designated as ‘arctic-like’ or ‘arctic-related’ but have a broad distribution in central , east , and southeast Asia [18 , 20] . Published phylogenetic analysis demonstrated that RABV isolates from Greenland belong to the arctic-3 lineage [16 , 17] . These studies did not allow for a more comprehensive evolutionary analysis as the datasets were restricted in terms of the number of samples , time , geographic origin , and sequence length . In this present study , a comprehensive panel of 58 RABVs from Greenland between 1990 and 2014 was analyzed . Additionally , 24 arctic RABVs from Canada/Alaska and Russia were also sequenced and added to this dataset to provide some context to the situation in Greenland . The principal objectives were ( i ) to infer the viral phylogenetic relationships in space and time based on complete genome sequences and ( ii ) to gain more insights into the contribution of the host population to the spatial spread of individual RABVs in Greenland . In particular , evidence based on sequence analysis for any links between phylogenetic clusters of arctic RABV and the arctic fox population in Greenland was sought .
All samples in this study were either taken from officially implemented passive rabies surveillance programs or already existing collections at the ( i ) DTU National Veterinary Institute , Technical University of Denmark , Denmark , ( ii ) Canadian Food Inspection Agency ( CFIA ) , Canada , ( iii ) Friedrich-Loeffler-Institut ( FLI ) , Germany , and ( iv ) Animal and Plant Health Agency ( APHA ) , UK . Samples ( Table 1 ) comprised original clinical brain samples submitted for passive surveillance from arctic foxes ( Vulpes lagopus ) , red foxes ( Vulpes vulpes ) , dogs ( Canis lupus familiaris ) , cats ( Felis silvestris catus ) , sheep ( Ovis aries ) , and a long-tailed ground squirrel ( Citellus undulates ) collected between 1977 and 2014 in Greenland , Northern Canada , Alaska and Russia that tested positive in the direct fluorescent antibody test ( FAT , [21] ) . Because this is a multi-center study , RNA extraction , library preparation and sequencing were done using slightly different protocols . The 3 protocols are briefly outlined in the following paragraphs and the respective protocol is denoted for each sample in Table 1 . In no case was 5’- or 3’-RACE performed to confirm the genome termini . The accessions for all RABV genome sequences generated in this study are given in Table 1 . For the determination of the genetic diversity of the reservoir host , mitochondrial ( mtDNA ) reference genes of arctic foxes , i . e . ATP6 , ATP8 , COX1 , COX2 , COX3 , CYTB , ND1 , ND2 , ND3 , ND4 , ND4L and NDS [25] were selected from the International Nucleotide Sequence Database ( INSDC ) databases . Additionally , the mitochondrial D-Loop sequence as suggested before [26] was also used for mapping . Briefly , raw reads from sequencing were mapped along the reference genes and all reads identified as fox mitochondrial sequences were assembled de novo . Subsequently , the resulting consensus sequence was used as reference to map all reads of the dataset in order to identify potential sequencing errors . The resulting sequences were inspected visually in Geneious ( v6 . 1 . 7; Biomatters ) . All obtained complete RABV genome sequences were aligned using ClustalW [27] ( http://www . clustal . org ) as implemented in Geneious ( Biomatters ) , the sequences were trimmed to equal length and labelled with the collection year . Subsequently , phylogenetic analyses were performed using the Bayesian Markov Chain Monte Carlo ( MCMC ) simulation in the BEAST ( Bayesian Evolutionary Analysis Sampling Trees ) package v1 . 8 . 2 [28] . Selection of the evolutionary model using IQ-Tree ( v1 . 1 . 0 , [29] proposed use of the General Time Reversible model with rate heterogeneity ( GTR + G ) . This model was used for MCMC simulation together with a relaxed molecular clock model and Bayesian Skyline population for 100 , 000 , 000 iterations , sampling every 10 , 000 states to give effective sample sizes . Maximum clade credibility trees ( MCC ) were annotated using TreeAnnotator ( v1 . 8 . 2 ) , 10% of the trees were removed as burn-in . The resulting final trees were visualized using FigTree ( v1 . 4 . 2; http://tree . bio . ed . ac . uk/software/figtree/ ) . For phylogenetic analysis of the RABV Nucleoprotein ( N ) gene sequences , the dataset was extended with additional sequences from the INSDC databases ( Table 2 ) . All calculations were performed as described above for the full genome sequences except that for MCMC simulation the transitional model with rate heterogeneity ( TIM + G ) model was used . From 55 of 58 Greenland samples , the substitutions per site and year were determined for the whole genome and all five protein coding nucleotide-sequences ( N- , P- , M- , G- and L- Gene ) , respectively . Sequences were aligned and best fitting evolutionary models selected as described above . The best model for the N and P genes was Kimura 3-parameter ( K81 , [30] , for the G and L genes Kimura 3-parameter with unequal frequencies ( K81uf ) , for the M gene Kimura 2-parameter ( K2P , [31] , and for the whole genome Kimura 3-parameter with unequal frequencies and proportion of invariable sites ( K81uf + I ) . Beast analysis was performed as described above except for the N gene for which 2 , 000 , 000 , 000 iterations and samples every 200 , 000 states were needed . To infer the evolutionary and phylogenetic relationships of foxes , the sequences were aligned using ClustalW and subsequently phylogenetic trees were calculated using the maximum likelihood ( ML ) method as implemented in MEGA ( v5 . 2; [32] ) . For these calculations , the best fitting evolutionary model , Tamura and Nei 1993 with rate heterogeneity ( TN93 + G ) , was selected using MEGA’s model test function and 1000 bootstrap replicates were calculated . The resulting phylogenetic trees were visualized in MEGA . Approximate sampling locations in Greenland , Canada and the USA ( Table 1 , Fig 1 ) were visualized using ArcGis 10 . 0 ( ESRI ) at the highest spatial resolution available .
To classify the sequenced viruses globally into pre-defined lineages , only complete N gene sequences ( n = 109 , Tables 1 and 2 ) were used , since to date no full-genome sequences are available for the Arctic rabies strains . All sequenced Greenland RABV ( Table 1 ) clustered within the previously established circumpolar arctic-3 lineage ( Fig 2 ) . This lineage appears to be rather heterogeneous . Older arctic-3 viruses from Greenland ( obtained in 1980/81 ) are clearly separated from recent Greenland RABVs . For higher resolution of the heterogeneous arctic-3 lineage the analysis was repeated using full-genome sequences obtained for 79 RABV samples from Greenland , Canada , USA ( Alaska ) and Russia between 1977 and 2014 ( Table 1 ) . This high resolution analysis confirmed previous delineation of the arctic RABV strain into four major lineages ( arctic 1–4 , Figs 1 and 3 ) , whereby RABV from Greenland exclusively grouped into arctic-3 . Furthermore , it enabled the distinction of seven subclades and seven outliers ( 1977–2013 ) within arctic-3 ( Fig 3 ) , with the most recent common ancestor ( MRCA ) of the analyzed viruses ( Fig 3 ) occurring approximately 82 years ago ( 95% HPD values , 73–92 years ) . The MRCA for the Greenland samples within this dataset dates back circa 35 years ( 95% HPD values , 32–37 ) , whilst the divergence into subclades occurred between 15 years ( subclade 3 . VII; 95% HPD values , 14–17 years ) and 6 years ( subclade 3 . IV; 95% HPD values , 5–8 years ) ago . Of these seven subclades , only arctic-3 . IV and 3 . V contained viruses from both Greenland and Canada ( 2006 to 2014 ) . Greenland RABV from arctic-3 . IV -3 . VI ( n = 10 ) originated from north-western and western Greenland ( regions 1–4 , Fig 1 ) , collected between 2005 and 2014 . Greenland specific subclades 3 . II , 3 . III , and 3 . VII consisted of 24 RABV from the western coastline ( regions 2–6 ) obtained between 2001 and 2014 . In contrast , all but one RABV within subclade 3 . I ( 2005–2011 ) originated from southern Greenland ( region 7 ) , with Gra10 . 08-GRL-6-AF-2008 being the only exception collected in region 6 ( Figs 1 and 3 ) . Both the individual gene sequences and the full-genome alignment were used to estimate the mean nucleotide substitution rate utilizing BEAST [28] . For the N gene sequences ( Tables 1 and 2 , Fig 2 ) an estimate of 2 . 5 E-4 substitutions per site per year ( 95% high posterior density ( HPD ) , 1 . 9 E-4–3 . 1 E-4 ) was obtained . When only N gene sequences from Greenland ( N = 55 ) were considered , the estimate was 3 . 1 E-4 substitutions per site per year , a value in the range of previous studies . Among the different genes , lowest and highest substitution rates were observed in the L gene and the P gene , respectively ( Table 3 ) . The substitution rate of 2 . 5 E-4 per site per year ( 95% HPD values , 2 . 1 E-4–3 . 0 E-4 ) for the full genome sequence is similar to the value observed for the L gene . Direct untargeted NGS determined by protocols 1 and 3 from original rabies positive brain samples from Greenland ( n = 48 ) and Canada ( n = 1 ) yielded a substantial amount of host sequences . This provided the unique opportunity to analyze the genetic structure of the arctic fox population . Analysis of 12 mitochondrial gene sequences revealed three major maternal lineages and additional outliers ( Fig 4 ) . Genetic identity within the main clusters was between 99 . 7% and 100% ( Cluster 1 ) , 99 . 8% and 100% ( Cluster 2 ) , and 99 . 9% ( Cluster 3 ) ; while identity between clusters was between 99 . 4% and 99 . 6% . For all clusters there was a distinct , partly overlapping ( cluster 1 and 2 ) geographical distribution . Twenty-one animals of cluster 1 were detected in North-western and Western Greenland ( regions 1–6 ) , while three foxes originated from the far distant Southern coast ( region 7 ) . Interestingly , a fox sample from the Northwestern Territories of Canada obtained in 1977 was almost identical with two foxes collected in 2007 in Western Greenland ( Fig 1 , Table 1 ) . Half of the arctic foxes from cluster 2 ( n = 19 ) originated from southern Greenland ( region 7 ) , while the remaining foxes were located in the western parts of the island ( regions 2 , 3 , 5 , and 6 ) . The highly conserved cluster 3 comprised exclusively of arctic foxes from region 4 , with samples from 12 years apart ( Fig 4 ) . The outliers originated from the western regions 2 , 3 , and 4 ( Figs 1 and 4 ) . Combinatorial analysis of RABV subclades , fox mitochondrial genes and the geographic origin revealed no association ( Fig 5 ) . The majority of samples were assigned to fox cluster 1 which was detected in all regions and comprised all RABV subclades . Fox cluster 2 was restricted mainly to the southern regions and therefore mostly infected with RABV subclades arctic 3 . I and 3 . II . Fox cluster 3 in contrast is only found in region 4 , but represents 2 different RABV subclades ( Figs 3 and 5 ) .
For a long time , there has been limited knowledge on the epidemiology of arctic rabies in Greenland [9–12] . It was only recently that the temporal occurrence , spatial distribution , and spread of arctic rabies in Greenland was investigated based on historical observations [13] , however , this study did not provide any context to the RABV circulating on the island . While previous molecular epidemiological studies on RABV from Greenland using partial N gene sequences revealed that they belong to the arctic-3 lineage [16 , 17] , unfortunately , the number of samples and the sequence length examined prevented further in-depth phylogenetic analyses . However , with the advent of NGS the determination of whole genome sequences for molecular epidemiological studies of RABV has become more efficient and comprehensive as illustrated recently for skunk variant of RABV from California [33] . Here , NGS derived sequence analysis is simultaneously applied to both arctic RABV strains and arctic fox genetics . A large number of RABV full genome sequences and mitochondrial genes were utilized for the phylogenetic analysis of arctic RABVs and their respective arctic fox host species . This dataset comprises a comprehensive number of RABV samples of the arctic strain ( n = 79 ) , particularly from Greenland ( n = 55 ) , thereby providing an improved knowledge of the molecular evolution of the circulating viruses and a more comprehensive understanding of the historical perspective of the disease across the Arctic . One limitation of this and previous studies is that collection of samples within Greenland , an island comprising 2 . 1 million km² , relies on a passive surveillance system that results in uneven distribution of submissions , i . e . samples are only submitted from areas where close fox-human encounters are possible and there is a lack of samples from central and eastern parts of Greenland ( Fig 1 ) . For instance , in sparsely populated east Greenland only 12 cases have been observed since the late 1960s until 2014 [13] , suggesting either low level of infection or underreporting [17 , 34] . Whilst in previous studies only selected RABV were considered [16–18] , here nearly all viruses from rabies cases detected between 2005 and 2014 were included , together with selected samples from an earlier study [16] that were available for re-analysis using NGS . While in Alaska lineage 4 , in Southern Canada lineage 1 and in Russia mainly RABV of arctic lineage 2 circulate , interestingly , both historic and recent RABV from Greenland exclusively fall within arctic-3 lineage ( Fig 1 ) . At present , based on our sample set of complete genome sequences , at least seven distinct subclades ( arctic 3 . I–3 . VII ) within this lineage have been circulating in Greenland ( Fig 3 ) . This delineation , however , contrasts results of a previous study , in which many of the Greenland samples ( 1990–2002 ) only clustered as one separate sub-group within arctic lineage 3 [18] . This can be explained by the limited length of the partial N gene sequence ( 500 bp ) used which prevents a higher resolution of the tree . In fact , combining all previous partial N gene sequences ( S2 Table ) [16–18] with our dataset resulted in a rather limited alignment of 163 nucleotides in length which is too short to support phylogenetic analysis ( S1 Fig ) . These limitations confound attempts to compare the studies and put them into a larger perspective , both temporally and spatially . Therefore , in this study this issue was addressed as best as possible by obtaining full length genome sequences from a subset of a previous study [16] . From a geographical perspective , the arctic-3 subclades defined here showed certain localized distributions ( Fig 5 ) . Considering the large home ranges of arctic foxes in connection with long distance migration of more than 1000 km [35–37] , a larger geographical overlap would have been expected . However , this holds true only for subclade 3 . II , the oldest cluster with viruses collected between 2002 and 2014 . Phylogenetic analyses showed that all RABV from Greenland analyzed in this study have derived from Canadian incursions ( Fig 3 ) with the oldest sample from 1977 having the most basal position in the tree . In contrast , an incursion and further spread of arctic RABV from Svalbard across the Greenland Sea into eastern and northeastern island as suggested previously [11] is not evident in this present study . In fact , Svalbard RABVs were shown to be more closely related to arctic RABVs from Russia [17 , 19] . The fact that subclades arctic-3 . IV and 3 . V are the only subclades to be identified on the North American mainland while all other subclades are restricted to Greenland’s western and southern parts , respectively , may represent evidence for a more recent exchange of viruses ( Figs 1 and 3 ) . In fact , these subclades 3 . IV and 3 . V were found in regions of Greenland where the distance to neighboring Ellesmere Island , Canada , is shortest . Here , pack ice that frequently bridges these two land masses may facilitate the spread of the disease [16 , 18] . Hence , the Smith Sound , the uninhabited sea passage between Greenland and Canada’s northernmost islands may play an important role in the exchange of arctic RABVs in this part of the Arctic [38] . This is supported by the close genetic relation between RABV Gra03 . 13-GRL-1-AF-2013 and 13N0473AFX-CAN-NU-AF-2013 , with nearly identical genomes ( single substitution ) , despite the fact that they are geographically separated by a distance of 750 km ( Thule Air Base to Resolute Bay ) . In Greenland , viruses with a high nucleotide substitution rate may have evolved into younger subclades that have a narrower geographical distribution and are only identified on the island ( Figs 3 and 5 ) . For instance , arctic subclade 3 . I is only found in the southern parts ( Region 6 , 7; Fig 5 ) . Interestingly , the most ancestral RABV still circulating ( subclade arctic 3 . II ) has the widest geographic spread across the entire western part of Greenland . Such spread has also been demonstrated epidemiologically [13] . The dynamic observed here is further demonstrated by the observation that older subclades previously found in Greenland ( Fig 2 ) , [15–18] seem to have disappeared . It is interesting that arctic lineage 2 ( Figs 1 and 3 ) had not ( yet ) been detected in Greenland , particularly considering that other arctic lineages appear to have spread [18] . As a case in point , one sample from 1990 from Grise Fjord , which has close proximity to Greenland , was arctic lineage 2 , while a sample from the same place twenty-three years later was arctic 3 ( Figs 1 and 3 , Table 1 ) . As regards the other arctic lineages , lineage 4 seems to be restricted to Alaska [18 , 39] ( Fig 1 ) , while circulation of arctic lineage 1 in Southern Ontario , Canada is highly restricted due to oral rabies vaccination [40 , 41] . The observed genetic dynamic within arctic RABVs is also demonstrated by the nucleotide substitution rates inferred from this dataset at the full genome level . Although in comparison with partial sequence analysis of other RABVs this appears low ( Table 3 ) , a substitution rate of 2 . 5 E-4 per site and year still indicates a substantial evolutionary dynamic . Discrepancies in the substitution rates and in the resulting MRCA as discussed before [18] , are a result of the respective partial sequence used for calculation ( Table 3 ) . Thus , not all RABV genes are equal for evolutionary analyses , as previously suggested [42 , 43] . Taken together , use of complete genome sequences should result in a more accurate substitution rate , closely reflecting the actual virus evolution and genetic dynamic . The use of unbiased NGS offered the unique opportunity to simultaneously obtain reservoir host derived RNA sequences from the same sample for population analysis . Similar to previous studies [26 , 44] we initially used D-Loop sequences for this analysis . However , this did not allow a clear distinction because of the high genetic identities observed . Instead , for increased resolution , the 12 protein-coding mitochondrial genes enabled delineation of Greenland’s fox population into three main genetic clusters as shown recently [39] . Still , the high genetic identity both within and between the three main genetic fox clusters ranging between 99 . 4% and 100% may support assumptions that the genetic diversity of island arctic foxes compared to main land populations as a result of colonization is low [37 , 45] . The detection of three almost genetically identical arctic fox samples from the Northwestern Territories of Canada and Western Greenland ( Fig 1 ) three decades apart may be evidence for high gene flow among arctic fox subpopulations , contributing to low genetic differentiation at least in the mitochondrial genome and further corroborate this hypothesis . In contrast , the results from this present study of mitochondrial gene analysis are in agreement with previous observations showing a fine-scale spatial population structure in Alaskan arctic foxes [39] . Future more detailed comparative haplotype analysis of arctic foxes including nuclear loci as well as fox genetic data from Canada’s northern territories and Alaska should corroborate this fact . While previously , mtDNA structure in arctic and red foxes from Alaska did not correspond to RABV variant structure in either species , microsatellite analyses identified 3 and 4 groups of arctic foxes , closely matching the distribution of rabies virus variants in the state [39] . Although the Greenland RABV arctic-3 subclades were not evenly distributed among the different mitochondrial fox clusters ( Fig 5 ) , these data provide no evidence for independent isolated evolutionary development of RABV in different arctic fox lineages but rather resemble geographic separation . In our study we focused on mitochondrial DNA ( mtDNA ) alone for characterization of the population genetics of arctic foxes . Analysis of mtDNA reveals a longer-term view of population structure than do fast evolving nuclear markers ( e . g . , microsatellites ) and analysis of multiple mitochondrial genes , as in this study , yields finer resolution of relationship than studies focusing on a single gene . Nonetheless , any combination of mitochondrial genes is effectively considered a single locus . Recent studies included multiple markers of nuclear genes i . e . microsatellites to investigate host genetics in carnivores , sometimes resulting in conflicting results when compared to parallel investigated mtDNA [46–50] . However , a meta-analysis showed that mtDNA was robust in determining patterns of population history and yielded similar results to microsatellites [51] . Finally , the lack of a standardized approach for using both types of marker genes and the limited information on nuclear genes to those that are expressed as mRNA and are thus part of the dataset precluded further analyses . For instance , in a recent study the transcriptome was used for evolutionary studies of Arctic and red foxes [52] . While this information should be available it is unclear whether it would be sufficient for intra-species genetic studies .
By combining full length RABV genome sequence analysis and host derived sequences the interaction of viruses and their hosts was exemplarily demonstrated and may serve as a model approach for analysis of real-world understanding of infectious disease dynamics and virus-host interdependencies using a landscape genetics approach as suggested for dog mediated rabies [53] . In this study , although no interdependencies based on mtDNA were identified , nevertheless this approach led to a better understanding of the evolution , dynamics and geographical spread of arctic rabies in Greenland . A high degree of genetic identity both of RABVs and arctic foxes from Canada and Greenland suggests the movement of infected animals between the two landmasses . The overall diversity of arctic RABV in Greenland was very limited and only by analyzing the entire genome , a high resolution of the genetic evolution was possible , providing real-time insights into viral evolution . These results may be useful for future control strategies of arctic fox rabies . In contrast to previous statements [13] , given the unique geographical location of Greenland , the expected reduction of connectivity by pack-ice due to climate change [54 , 55] and the geographic separation of individual host and virus genetic subclades despite long distance movement [36] , the idea of arctic rabies control using oral rabies vaccination ( ORV ) in selected coastal areas appears feasible [56 , 57] . While preliminary field trials in Newfoundland ( Canada ) [58] and even northern Greenland [59] demonstrated in principle that ORV could be undertaken in remote northern regions , a targeted vaccination strategy would have to be developed before an elimination program could be implemented . | Next to dog-mediated rabies , wildlife rabies continues to pose a public health problem , particularly in the northern hemisphere . Control of this zoonosis at the animal source has been proven the most efficient route to reduction of human rabies burden . Successful elimination of red fox-mediated rabies in Western Europe and parts of North America has demonstrated the viability of wildlife rabies control strategies . In some regions , the epidemiology of wildlife rabies is well understood; this is not the case for arctic rabies , particularly in Greenland . Previous molecular epidemiological studies demonstrated the occurrence of one particular arctic rabies virus ( RABV ) lineage ( arctic-3 ) but were limited by low sample numbers and limited sequence length so as to preclude generation of high resolution phylogenetic analysis . Here , a unique set comprised of 79 complete genome sequences of RABVs from Greenland , Canada , USA ( Alaska ) and Russia collected over the past four decades was analysed . The use of next generation sequencing ( NGS ) allowed simultaneous determination of host derived sequences encoding mitochondrial proteins from the same brain tissue of 49 arctic foxes . These sequence data combined with geographical and temporal information permit the study of the genetic diversity and evolution of circulating RABVs in Greenland against the background of reservoir host genetics . The results reveal the existence of a single arctic RABV lineage ( arctic-3 ) in Greenland , which has evolved into multiple distinct variants . These analyses provide an improved knowledge of the evolution of the circulating viruses at the molecular level and a better understanding of the historical perspective of the disease in Greenland compared to other parts of the Arctic . This knowledge will support policy on rabies control in mammalian wildlife reservoirs . | [
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] | 2016 | Spatio-temporal Analysis of the Genetic Diversity of Arctic Rabies Viruses and Their Reservoir Hosts in Greenland |
The RNA helicase SUV3 and the polynucleotide phosphorylase PNPase are involved in the degradation of mitochondrial mRNAs but their roles in vivo are not fully understood . Additionally , upstream processes , such as transcript maturation , have been linked to some of these factors , suggesting either dual roles or tightly interconnected mechanisms of mitochondrial RNA metabolism . To get a better understanding of the turn-over of mitochondrial RNAs in vivo , we manipulated the mitochondrial mRNA degrading complex in Drosophila melanogaster models and studied the molecular consequences . Additionally , we investigated if and how these factors interact with the mitochondrial poly ( A ) polymerase , MTPAP , as well as with the mitochondrial mRNA stabilising factor , LRPPRC . Our results demonstrate a tight interdependency of mitochondrial mRNA stability , polyadenylation and the removal of antisense RNA . Furthermore , disruption of degradation , as well as polyadenylation , leads to the accumulation of double-stranded RNAs , and their escape out into the cytoplasm is associated with an altered immune-response in flies . Together our results suggest a highly organised and inter-dependable regulation of mitochondrial RNA metabolism with far reaching consequences on cellular physiology .
The turnover of RNA is determined by its rate of synthesis and degradation , and together with the rate of translation , determines the level of gene expression of a given transcript . These processes are not constant and individual RNA species have different half-lives and rates of translation . RNA degradation is frequently used as an important step in regulating gene expression , allowing the cell to rapidly adapt to different physiological demands . This process is fairly well understood in a variety of systems , including the nucleus and cytosol of cells [1] , but is less clear in mitochondria . The mitochondrial genomes ( mtDNA ) of most metazoans are small , circular , double-stranded , multi-copy genomes , dispersed throughout the mitochondrial network . Gene content and order might vary among species , but the genes are distributed on both strands in most bilaterian animals . Mitochondrial transcription in humans and mice is well defined [2] , initiating from two promoters in the regulatory region on either strand of the mitochondrial genome , leading to the generation of long , polycistronic transcripts , which are processed into their individual primary units by enzymes that recognise the gene junctions [2–7] . Just as its mammalian counterpart , the mitochondrial genome of Drosophila melanogaster ( Dm ) encodes for 13 essential subunits of the oxidative phosphorylation ( OXPHOS ) system , as well as two rRNAs and 22 tRNAs necessary for mitochondrial translation . The mechanism of transcription in Dm is less clear [8] . Dm mtDNA shares none of the sequence elements of the mammalian regulatory region , which in the fly consists almost exclusively of adenine and thymidine residues , giving it the name of the A/T rich region [9] . Its function is not entirely clear , but origins of replication have been associated with this region [10 , 11] , as well as two promoters , similar to the arrangement in mammals [12] . However , the presence of distinct polycistronic transcription units that cover either strand and not originating from the A/T rich region , have led to the suggestion of additional promoter regions in the fly [13 , 14] . Two members of the MTERF transcription termination family , mTTF and mTERF5 , have been suggested to interact with two sequence elements at the boundaries of these transcription units to regulate transcription [13 , 14] , although a role in mtDNA synthesis has also been proposed for these factors [15] . In mammals as well as in the fly , transcription leads to the generation of polycistronic transcripts that need to be processed , and the factors involved are conserved from fly to humans [4 , 5 , 7 , 8 , 16–19] . The circular nature of mtDNA , with promoters on either strand , means that both sense and antisense transcripts are formed during transcription , but the half-lives of these transcripts are vastly different , despite deriving from the same polycistronic transcript . For instance , antisense RNA species are rarely detected under normal physiological conditions [20–23] , and several lines of evidence indicate that processing , maturation and degradation are linked [24–26] . How transcripts are selected for stabilisation or degradation is not known . One factor known to stabilise mitochondrial mRNAs is the leucine-rich pentatricopeptide repeat motif-containing protein , LRPPRC , and its inactivation leads to reduced mitochondrial mRNA steady-state levels in humans [27–29] , mice [30 , 31] and flies [32] . Whether LRPPRC or its Dm ortholog DmLRPPRC1 , also known as bicoid stability factor BSF [33] , are able to distinguish between coding and non-coding RNAs is unclear , and in vitro experiments suggested that human LRPPRC has strong affinity to a broad range of RNA substrates , with lower affinity to poly ( A ) stretches [29] . Two factors , the ATP-dependent RNA helicase SUV3 and the polynucleotide phosphorylase , PNPase ( encoded by PNPT1 ) , have been proposed to form the minimal mitochondrial RNA degrading complex , degrading RNA in a 3′ to 5′ direction [21–23 , 34] . SUV3 belongs to a highly conserved Ski2 family of DExH-box RNA helicases , with orthologs found in eukaryotes to Rhodobacter [35] , and PNPase has been shown to both degrade and extend 3′ tails in vitro [36] . In plants PNPase regulates polyadenylation-dependent mitochondrial RNA decay [37–39] . Additionally , in vitro studies have suggested that SUV3 and PNPase regulate polyadenylation of mitochondrial transcripts by modulating the function of the mitochondrial poly ( A ) polymerase , MTPAP [40] . This is in agreement with previous work , were we demonstrated that loss of DmSUV3 resulted in reduced polyadenylation but increased steady-state levels of mitochondrial transcripts [41] . Here we studied the relationships of PNPase , SUV3 , MTPAP and LRPPRC on mitochondrial RNAs in a series of different Dm models . Our results confirm that PNPase and SUV3 are responsible for mRNA degradation in vivo and show that PNPase and SUV3 have opposing effects on polyadenylation . Further , we demonstrate that antisense RNA is not polyadenylated , and we suggest that these RNA species are not recognised by LRPPRC . However , the accumulation of antisense RNA due to the loss of PNPase , SUV3 or MTPAP leads to the accumulation of double stranded mitochondrial RNA , which may leak out into the cytoplasm , affecting other cellular pathways .
A BLAST search against human PNPase or yeast DSS1p ortholog in Drosophila melanogaster ( Dm ) identified CG11337 as the only candidate , encoding a yet uncharacterised protein , sharing 55 . 1% identity with human PNPase ( Fig 1A ) . In silico analysis predicted a mitochondrial localisation , using TargetP ( 0 . 76 ) or Mitoprot ( 0 . 92 ) , and identified the PNPase family-defining RNase PH , KH , and S1 domains [42] . Mitochondrial localisation was confirmed in HeLa cells expressing a GFP-tagged CG11337 fusion protein ( Fig 1B ) , as well as by Western blot analysis of subcellular fractionations of tissue homogenates from Dm larvae expressing a FLAG-tagged CG11337 fusion protein ( Fig 1C ) . Therefore , we suggest CG11337 is the Dm ortholog of PNPase . In order to explore the involvement of DmPNPase in mitochondrial function , we targeted its locus by CRISPR/Cas9 gene editing technology to generate DmPNPase knockout ( dmpnpaseKO ) flies ( see Materials and methods for details ) . CRISPR/Cas9 editing caused a deletion of 8 nucleotides in exon 2 of the gene , leading to a frame shift and a premature stop codon in the corresponding transcript ( S1 Fig ) . Flies heterozygous for the loss of DmPNPase ( w;;dmpnpaseKO/TM6B ) were viable with no obvious phenotypic changes . Homozygous flies showed a severe reduction in dmpnpase transcript levels ( Fig 1D ) and were larval lethal ( Fig 1E ) . Respirometry measurements of larvae from dmpnpaseKO and control larvae revealed reduced oxygen consumption using both complex I and IV substrates ( Fig 1F ) . This was confirmed by measuring isolated respiratory chain ( RC ) enzyme activities , demonstrating a combined complex I and IV defect ( Fig 1G ) . In organello translation and Western blot analysis using isolated mitochondria from dmpnpaseKO larvae , revealed aberrant translation and reduced steady-state levels of the complex I subunit NDUFS3 and the mitochondrial encoded subunit COX3 upon loss of DmPNPase ( S2A Fig and Fig 1H ) , suggesting instability of the expressed OXPHOS subunits , leading to the observed combined OXPHOS defect observed in the dmpnpaseKO larvae . These findings could be confirmed in flies , where DmPNPase was silenced by RNAi ( dmpnpaseKD ) , leading to the same , albeit milder phenotype ( S2B–S2G Fig ) . In conclusion , removal of the DmPNPase gene results in a severe mitochondrial dysfunction and lethality , comparable to the disruption of PNPase in mice [43] and human patients [44–46] . Loss of DmPNPase in vivo led to a general increase in steady-state levels of all analysed mRNAs ( Fig 2A and S2G Fig ) . Northern blot analysis also revealed the presence of smaller species , which we interpret as degradation intermediates ( S3A Fig ) . Steady-state levels of the ribosomal RNA subunits were not affected ( Fig 2A ) , while the levels of some mitochondrial-encoded tRNAs were decreased ( Fig 2B and S3B Fig ) . Increased steady-state levels could be the result of compensatory mechanisms but in organello transcription experiments in dmpnpaseKD , showed only a mild increase in de novo transcription in comparison to mRNA steady-state levels , suggesting that the mRNAs were indeed stabilised ( S2F and S2G Fig ) . We previously reported that loss of SUV3 in flies leads to pupae lethality with increased mRNA levels and a reduction of mature tRNAs [41] . Together with SUV3 , PNPase has been suggested to form the mitochondrial degradosome [21 , 23] and removing both components simultaneously should therefore lead to additive effects in comparison to removing each individually . Silencing DmPNPase and DmSUV3 by siRNA ( see Materials and methods ) simultaneously ( dmpnpaseKD/dmsuv3KD ) ( S4A and S4B Fig ) had a synergistic effect on mt-nd transcripts , leading to an up to 30-fold increase in mt-nd2 steady-state levels ( Fig 2C ) . In contrast , mitochondrial-encoded cox and cytb transcripts were not further stabilised , in comparison to single DmSUV3 silencing . Silencing of both components of the degradosome had no effect on mt-rRNA steady-state levels ( Fig 2D ) , suggesting differential regulation of the mitochondrial ribosome . To further confirm the role of both DmSUV3 and DmPNPase in the turnover of mitochondrial transcripts , we generated flies overexpressing DmPNPase ( dmpnpaseOE ) , DmSUV3 ( dmsuv3OE ) , or both ( dmpnpaseOE/dmsuv3OE ) ( S4A and S4C Fig ) . Overexpression of DmSUV3 or DmPNPase individually had no or only a mild effect on mRNA steady-state levels , with both fly lines viable ( Fig 2E ) . In contrast , simultaneous overexpression of both components of the degradosome led to larval lethality at the 2nd instar larval stage and a severe reduction of mitochondrial transcript levels , including 12S and 16S rRNA steady-state levels ( Fig 2E and 2F ) . Interestingly , the effects on mt-tRNAs were varied , with steady-state levels increased , unchanged , or decreased ( Fig 2G ) . Taken together , we conclude that in vivo DmPNPase and DmSUV3 compose a functional unit that regulates mitochondrial mRNA levels more efficiently than the individual proteins can , supporting the concept of them forming an active complex . PNPase , together with SUV3 , has been shown to affect mitochondrial mRNA polyadenylation by either inhibiting or stimulating MTPAP activity in vitro [40] , but studies in vivo have not been performed . In agreement with this , we previously demonstrated that loss of DmSUV3 had a negative effect on poly ( A ) tail length [41] , but whether this was a consequence of mRNA abundance was unclear . We therefore analysed the 3′ ends of mitochondrial ND1 transcripts by 3′RACE , followed by cloning and sequencing in larvae with either depletion ( dmpnpaseKD/dmsuv3KD ) or over-expression ( dmpnpaseOE/dmsuv3OE ) of the mitochondrial degradosome ( Fig 3A and 3B ) . Silencing of DmPNPase alone or together with DmSUV3 resulted in significantly increased poly ( A ) tail length ( Fig 3A ) , which we confirmed in samples from dmpnpaseKO larvae ( S3C Fig ) . DmpnpaseKO larvae also presented with an increased amount of shortened tails , which might be attributable to the poor health of the dmpnpaseKO larvae . In contrast , overexpression of the degradosome , which resulted in severe reduction of mRNA steady-state levels , had only a mild effect on poly ( A ) tail length ( Fig 3B ) . Whether this increased degradation requires an additional deadenylase that first removes the poly ( A ) tail , is not clear . Nevertheless , our genetic experiments support the notion that DmSUV3 and DmPNPase have opposing effects on the polyadenylation of mitochondrial transcripts also in vivo . Our data thus far demonstrate that both PNPase and SUV3 are necessary and able to degrade mitochondrial mRNAs in vivo and that PNPase and SUV3 have opposing effects on polyadenylation . We therefore investigated whether polyadenylation could act as a signal for degradation , similar to the situation in bacteria or plant mitochondria [37] . We previously deleted DmMTPAP by homologous recombination in the fly ( dmmtpapKO ) , demonstrating that polyadenylation was necessary for the integrity of the 3′ terminus of mitochondrial mRNAs [47] . Additionally , mRNA levels were increased in most cases , suggesting that the absence of a poly ( A ) tail might have prevented their degradation . To test this hypothesis , we crossed flies overexpressing DmPNPase to dmmtpapKO flies ( dmpnpaseOE/dmmtpapKO ) ( S4D Fig ) and measured mRNA steady-state levels using Northern blot analysis ( Fig 3C and S4E Fig ) . Mt-mRNA steady-state levels were significantly reduced despite the dmmtpapKO background , demonstrating that upon overexpression DmPNPase was able to degrade mitochondrial transcripts in the absence of a poly ( A ) signal . This observation suggests that polyadenylation by MTPAP is not required for the degradation of mitochondrial transcripts , but that other factors might be responsible for regulating mRNA stabilisation . One such factor is LRPPRC , known to stabilise mitochondrial mRNAs [48 , 49] . Additionally , we and others previously demonstrated that LRPPRC is required for sufficient polyadenylation [31 , 32 , 50] . In order to investigate the relationship between PNPase , SUV3 and LRPPRC in vivo , and to probe whether the degradosome is responsible for the degradation of mRNAs in the absence of LRPPRC , we generated flies with depleted DmPNPase or DmSUV3 in addition to DmLRPPRC1 [32] ( dmlrpprc1KD/dmpnpaseKD or dmlrpprc1KD/dmsuv3KD ) ( S4F Fig ) . Decreasing either PNPase or SUV3 in combination with LRPPRC stabilised mRNA steady-state levels in comparison to dmlrpprc1KD alone ( Fig 3D ) , suggesting that indeed , LRPPRC functions as a physical barrier , protecting mRNAs from degradation by the degradosome . Surprisingly though , silencing of DmPNPase did not restore poly ( A ) tail length in the absence of DmLRPPRC1 ( Fig 3E ) . Thus , the degradosome is not responsible for the reduced polyadenylation , but rather MTPAP has inefficient processivity in the absence of LRPPRC . This is consistent with previous observations , where LRPPRC was able to stimulate MTPAP processivity in vitro [50] . Processing of the polycistronic transcripts leads to the formation of non-coding anti-sense RNA , which is rapidly removed under normal conditions [20–23] . However , the mechanism for this selective removal is unknown . PNPase and SUV3 have been suggested to degrade antisense RNA in cells and we and others previously reported that silencing of DmSUV3 resulted in the accumulation of antisense RNA [41] . In agreement , loss of DmPNPase also resulted in stabilisation of antisense RNA ( Fig 4A ) . More surprisingly though , loss of polyadenylation due to the absence of DmMTPAP also led to the accumulation of antisense RNA ( Fig 4A ) . Previous work in mice , using immunoprecipitation ( PAR-CLIP ) against LRPPRC , followed by high-throughput sequencing analysis , suggested that LRPPRC does not bind to antisense RNA [51] . If LRPPRC does not bind antisense RNA , polyadenylation of antisense should be reduced due to the lack of LRPPRC . We therefore analysed the 3′ ends of transcripts antisense to cox1 in control and dmpnpaseKO larvae and found that these antisense transcripts had a mean length of only 5 adenines , suggesting that antisense RNA is not extensively polyadenylated , possibly due to the lack of LRPPRC stimulation ( Fig 4B ) . This suggests that antisense RNA is quickly removed by DmPNPase and SUV3 prior to protection by LRPPRC , although MTPAP is still able to initiate oligoadenylation , i . e . the addition of only a few adenosines . Sense mRNAs , on the other hand , are protected by LRPPRC , polyadenylated and maintained intact in order to be translated and the resulting proteins being assembled . The accumulation of antisense RNA leads to the possibility of the formation of intermolecular double strand RNA ( dsRNA ) , where sense mRNAs hybridise to their antisense counterpart . Indeed , mitochondrial dsRNAs have already been observed previously in vitro [20 , 52] , and their existence in cells was recently demonstrated and even suggested to be able to be released into the cytosol under conditions of perturbed mtRNA degradation [53] . To investigate whether dsRNA also accumulates in flies , we isolated RNA from larvae lacking DmSUV3 , DmPNPase , or DmMTPAP and treated the samples with RNases specific for single- ( RNase T1 ) or double-stranded ( RNase III ) RNA , followed by Northern blot analysis to several mitochondrial targets ( see Materials and methods ) . The accumulation of antisense RNA did indeed lead to the formation of RNA species in the absence of DmPNPase or DmSUV3 , which were resistant to RNaseT1-treatment , but which could be removed by RNase III , a nuclease with preferentially dsRNA as substrate ( Fig 4C ) . Surprisingly , dmmtpapKO larvae also accumulated dsRNA , suggesting that dsRNA can be formed by a range of disrupted processes in mitochondrial RNA metabolism . As expected , larvae lacking DmLRPPRC1 ( dmlrpprc1KD ) did not show any signs of dsRNA ( Fig 4C ) . Recently , Dhir and colleagues demonstrated that the disruption of PNPase or SUV3 in cells can lead to the formation of dsRNA , and that this dsRNA can be released from mitochondria in the absence of PNPase [53] . We therefore performed immunohistochemistry on isolated brains from dmpnpaseKO , dmsuv3KD , dmmtpapKO , and dmlrpprc1KD larvae , using the antibody J2 that recognises dsRNA . Our results showed a punctuate cytosolic pattern in dmpnpaseKO , dmsuv3KD and dmmtpapKO larval brains , while control or dmlrpprc1KD larvae showed a reduced signal ( Fig 4D ) . In none of the models though , the J2 signal clearly co-localised with mitochondria , as determined by counterstaining against the mitochondrial ATPase subunit , ATP5a , which is likely due to the J2 antibody unable to penetrate mitochondria in the experimental conditions . Nevertheless , these results indicate that dsRNAs can accumulate in the cytosol of fly models lacking dmpnpaseKO , dmsuv3KD , or dmmtpapKO . We next performed J2-immunoprecipitation-based dsRNA sequencing ( dsRNA-seq ) on sub-cellular fractions from dmpnpaseKO larvae in order to identify the nature of these dsRNAs ( Fig 5A ) . Purity of our preparations were confirmed by Western blot analysis ( S5A Fig ) . When normalised to control samples , dsRNA was extensively enriched in both mitochondrial and cytosolic fractions in dmpnpaseKO samples ( Fig 5A , and S5B and S5C Fig ) , indicating that mitochondrial-derived dsRNA can be released from mitochondria in flies . To confirm the exclusivity of mitochondrial dsRNA leakage in the absence of PNPase , we investigated the accumulation of dsRNA in dmpnpaseKO , dmsuv3KD , and dmmtpapKO samples with qRT-PCR for mitochondrial transcripts in cytosolic fractions . To our surprise , we observed a significant increase of mitochondrial-derived RNA , sensitive to the dsRNA-specific endoribonuclease RNase III , in cytoplasmic fractions from all 3 models ( Fig 5B ) , suggesting that the dsRNAs observed by immunostaining are indeed of mitochondrial origin . The accumulation of mitochondrial-derived dsRNA in the cytosol could be a consequence of altered mitochondrial morphology . We therefore investigated mitochondrial morphology in the ventral nerve cord of larvae deficient in PNPase , SUV3 , MTPAP , or LRPPRC as well as in larvae overexpressing both PNPase and SUV3 by confocal microscopy [54] . However , we observed no gross difference in any of the mutant fly lines to control larvae ( S6 Fig ) . Mitochondrial-derived dsRNAs in the cytoplasm have recently been shown to activate the MDA5-driven antiviral signalling pathway in human cell lines [53] . Although flies and humans do not share the same antiviral responses , we analysed mRNA levels of the corresponding antiviral response genes Dicer2 , Ago2 , and R2D2 in flies [55 , 56] . Transcript levels of all three factors were significantly decreased in dmpnpaseKO and dmmtpapKO larvae , with a milder response in dmsuv3KD . Silencing of dmlrpprc1 , which has an OXPHOS defect but did not leak dsRNA into the cytoplasm , resulted in a trend for reduced transcript levels , with only Ago2 levels being significantly reduced ( Fig 5C ) . Dicer2 , R2D2 , and Ago2 have been shown to be essential for the fly antiviral defense [57–59] , suggesting that the loss of factors involved in mitochondrial RNA metabolism can result in a hypersensitivity to viral infections . Together , our results suggest that dsRNA can escape the mitochondrial matrix upon disruption of several factors involved in the turnover of mitochondrial RNA .
The mechanisms involved in mitochondrial RNA turnover remain poorly understood , and a number of factors have been associated with the degradation of mitochondrial RNAs . Two of these factors , PNPase and SUV3 , have been extensively studied , and have been suggested to constitute the mitochondrial degradosome . The majority of these studies have been performed in vitro or in cell culture and their in vivo role is therefore not always clear . Additionally , many of these studies only investigated a single protein in isolation , but it is increasingly becoming clear that many of these factors work cooperatively . We therefore used a combination of transgenic Dm models to genetically address the interactions of the mitochondrial degradosome , and how PNPase and SUV3 affect the functions of the mitochondrial mRNA stabilising protein , LRPPRC , or the mitochondrial poly ( A ) polymerase , MTPAP . We provide in vivo evidence that CG11337 is the fly orthologue of PNPase , and its disruption has wide ranging consequences on mitochondrial RNA metabolism and cell function . PNPase has phosphorolytic 3′ to 5′ exoribonuclease activity and its overexpression in Dm led to reduced mitochondrial mRNA steady-state levels , while deletion of DmPNPase led to the accumulation of mitochondrial mRNAs , suggesting a direct role in mRNA turnover . These effects were amplified when both factors of the proposed mitochondrial degradosome were either increased or decreased , further supporting their complementary function . We were surprised , though , that silencing of DmSUV3 had a stronger effect on steady-state levels than DmPNPase knockdown , suggesting that residual PNPase protein levels are highly active , but are dependent on SUV3 . In this case , a strict regulation of PNPase function is required to prevent unwanted degradation . However , the mechanism of such regulation remains unknown . Interestingly , effects on rRNAs and tRNAs were less pronounced , suggesting that PNPase and SUV3 are not involved in their turnover and changes might be a secondary response . The current model of mitochondrial RNA turnover ( Fig 6 ) suggests that the leucine-rich PPR motif-containing protein , LRPPRC stabilises mitochondrial mRNAs , and its disruption leads to a rapid depletion of mitochondrial mRNAs in flies [32] , mice [31] , and cells [60] . Simultaneous loss of DmLRPPRC1 and the degradosome restored mRNA steady-state levels , supporting the role of SUV3 and PNPase as the main responsible factors for the degradation of transcripts not protected by LRPPRC . Besides degrading mRNAs , loss of the degradosome also resulted in the accumulation of antisense RNA , suggesting that PNPase and SUV3 are also responsible for degrading these RNAs . This is in agreement with recent results that observed the accumulation of antisense RNA upon depletion of the degradosome [34 , 53] . Interestingly , MTPAP knockout larvae also accumulated antisense RNA , suggesting that polyadenylation might be required for removal . We did indeed observe short poly ( A ) tails on antisense transcripts , but whether these are required for degradation remains to be investigated . On the other hand , overexpression of DmPNPase could significantly reduce mRNA levels in the absence of polyadenylation , suggesting that transcripts lacking a poly ( A ) tail are more sensitive to increased PNPase levels , or that polyadenylation might not be a prerequisite for degradation . Several lines of evidence suggest that LRPPRC is required for full polyadenylation of mature mitochondrial transcripts , by stimulating MTPAP [31 , 32 , 50 , 51 , 60] . Additionally , PNPase , SUV3 and MTPAP have been proposed to form a transient complex to modulate poly ( A ) tail length in response to cellular energy demands [40] . However , the way how these factors work together in vivo has not been investigated . Our data demonstrate that LRPPRC is required for full polyadenylation in vivo and that PNPase and SUV3 have opposing effects on MTPAP processivity . Loss of both LRPPRC and PNPase did not restore poly ( A ) tail length , supporting the idea that shortening of poly ( A ) tails is not a consequence of degradosome function but rather due to absence of stimulation of MTPAP by LRPPRC , as suggested by in vitro studies [50] . The occurrence of short poly ( A ) tails on antisense RNA suggests two things . First , antisense RNAs are not recognised by LRPPRC , because even when stabilised due to the disruption of PNPase , they are not fully polyadenylated . Further , it also suggests that MTPAP can initiate adenylation in the absence of LRPPRC , but full polyadenylation requires LRPPRC interaction . The notion that LRPPRC does not bind antisense RNA is supported by recent PAR-CLIP experiments against LRPPRC that did not identify unprocessed or antisense RNA [51] . What distinguishes sense from antisense RNA is not clear , but recently the quasi-RNA recognition motif ( qRRM ) protein GRSF1 has been shown to recognise and—together with PNPase and SUV3—resolve and degrade G-quadruplex structures , which predominantly occur in non-coding mitochondrial RNAs [34] . GRSF1 is not conserved outside of vertebrates and thus additional mechanisms are likely to distinguish different RNA species , although structural elements in the RNAs are likely . In addition , the presence of a start codon on sense transcripts and consequently the recruitment of regulatory proteins and translational activators might rapidly target the transcripts for translation and hence make them escape the degradation apparatus . Further , it is possible that the formation of dsRNA prevents LRPPRC binding , and thus , full polyadenylation . Nevertheless , we confirm the involvement of both PNPase and SUV3 in the degradation of both mRNAs and antisense RNAs in vivo , and that both factors are able to modulate polyadenylation of mitochondrial mRNAs . Further , we reveal that this influence acts only on mRNA associated with LRPPRC , but that the stimulatory effect of LRPPRC on polyadenylation occurs after initiation of adenylation by MTPAP . Thus , we provide a hierarchical order of mitochondrial transcript maturation , where immature coding transcripts are first adenylated by MTPAP , followed by stabilisation by LRPPRC and full polyadenylation in a SUV3-dependent manner . Additionally , our data demonstrate that non-coding antisense transcripts , which are routinely generated during mitochondrial transcription , are recognised by MTPAP , but not by LRPPRC and are therefore rapidly degraded in a SUV3/PNPase-dependent manner ( Fig 6 ) . Our results indicate that the accumulation of mitochondrial antisense RNA can lead to the formation of dsRNA species , by disrupting PNPase , SUV3 , as well as MTPAP . DsRNA in mitochondria was first described by Young and Attardi [52] , but recently shown to be released into the cytosol in the absence of PNPase in mammalian cells , where this release could increase interferon B1 ( IFNB1 ) levels via the RNA sensor MDA5 and subsequently via MAVS [53] . Various reports suggest that the accumulation of mitochondrial-derived nucleic acids in the cytosol can trigger the innate immune response in mammals [53 , 61–64] . In human cells , the release of dsRNA seems to be restricted to the loss of PNPase , as silencing of human SUV3 leads to the accumulation of dsRNA but not their release into the cytosol [53] . In contrast , we show that the release of mitochondrial dsRNA can be caused by a variety of cellular stresses in vivo , including the loss of PNPase , SUV3 or MTPAP , suggesting that the release of dsRNA might be a downstream consequence to a mitochondrial defect . Neither the MDA5-MAVS signalling pathway , nor IFNB1 are conserved in Dm . Instead , Dm use Dicer2 , which together with Ago2 and R2D2 , constitutes the main RNA sensor in antiviral signalling [57–59] . We demonstrate a down regulation of these antiviral response factors , suggesting an increased sensitivity to viral infections . Dicer2 processes dsRNA into 21nt duplex siRNA , and the Dm models described here accumulated long stretches of mitochondrial-derived dsRNA into the cytoplasm , which is consistent with the reduced levels of Dicer2 observed in these lines . It is likely that the mitochondrial dysfunction , observed in these flies , has at least a partial impact on the ability to respond to viral infections , although it is not clear whether the release of mitochondrial-derived dsRNA is the primary signal . How and why dsRNAs are released into the cytoplasm is not known , but we failed to observe any obvious morphological changes in the mitochondrial network in the brains of the investigated Dm models . Nevertheless , there is growing evidence for an important role for mitochondrial function in human immunity [61 , 65] . The altered immune responses to mitochondrial defects observed here could explain why some patients with mitochondrial disease respond catastrophically to infections [64 , 66] . However , it will also be important to understand if and in what way the existence of dsRNA inside the mitochondrial network can affect mitochondrial function .
All genomically engineered fly lines were maintained and experiments performed at 25°C and 60% humidity on a 12h:12h light:dark cycle and fed on a standard yeast–sugar–agar ( 10-5-1 ) medium . All fly stocks were backcrossed for at least 6 generations into the white Dahomey Wolbachia-free background ( w ) . For in vivo knockdown of dmpnpase , a w;UAS-dmpnpase-RNAi; line was obtained from the Vienna Drosophila Resource Centre ( VDRC , #108198 ) . For in vivo knockdown of dmsuv3 a w;UAS-dmsuv3-RNAi; line was obtained from the National Institute of Genetics ( NIG-Fly , Japan , #9791R-2 ) . Ubiquitous knockdown of dmsuv3 , dmpnpase or double mutants was achieved by crossing the UAS-RNAi lines to the driver line daughterless GAL4 ( w;;daGAL4 ) . The balancer fly line stocks ( +;CyO/Gla;+ ) ; and ( +;+;TM3 , Sb Ser/TM6B ) were used to generate all the double mutants line stocks . Constructs for the generation of fly lines overexpressing DmPNPase or DmSUV3 were sent for embryo injection to BestGene ( California , USA ) . For adult hatching rate measurements , flies were allowed to lay eggs on grape juice agar plates for 8h . Then , the eggs were collected and transferred to vials ( 100 eggs/vial ) with yeast–sugar–agar medium . Hatching was recorded daily . At least five biological replicates were performed per genotype . The generation of the PNPaseKO line ( dmpnpaseKO ) was performed as previously described [67] , using the transgenic fly line nos-cas9 ( Bloomington stock centre: 54591 ) . Genomic DNA from the cas9-fly strain was used to amplify and sequence the dmpnpase gene for the selection of CRISPR targets . The pnpase sequence was submitted to CRISPR Optimal Target Finder [68] and two target sites ( 5′: GACCTTCAGTTCCGGCCGCC and 3′: ATCTAATATTCTGGACATC ) with the lowest off-target cleavage score were selected . The cloning strategy of the gRNAs into pCFD4 plasmid ( AddGene plasmid 49411 ) was followed as in [67] . Briefly , the dmpnpase target sites were included in forward and reverse primers with homology to pCFD4 , and used on a PCR using the plasmid as template . PCR products were then cloned into the BbsI-digested pCFD4 vector by Gibson assembly . The pCFD4 plasmid containing the gRNAs was injected into embryos in Bestgene to generate a gRNA expressing fly line . To induce germ line cleavage , the transgenic nos-cas9 virgin females were crossed to gRNA–expressing males . Resulting chimaeras were individually crossed to TM3/TM6B balancer flies , and the offspring was screened by for deletions at the dmpnpase locus . Individual candidate flies were again crossed to the TM3/TM6B balancer line , followed by screening for homozygous lethality in the offspring of intercrosses . PCR screening was performed as follows . The wings of candidate flies were incubated at 37°C for 45 min in 50 μl of freshly prepared adult fly homogenisation buffer ( 10 mM Tris-HCl , pH 8 . 2 , 25 mM NaCl , 1 mM EDTA , 0 , 2 μg/μl proteinase K ) and 2 μl of the homogenate was used for the PCR , subsequent screening and sequencing . The obtained dmpnpaseKO lines were backcrossed for 6 generations to a wDah background to remove possible off-target effects . Full-length dmpnpase cDNA was obtained from the Drosophila Genomics Resource Centre ( LD03255 ) . dmpnpase cDNA was cloned into pEGFP-N3 plasmid ( Clontech ) to generate a DmPNPase-GFP fusion construct . To generate a dmpnpase-Flag fusion construct the cDNA was amplified with a reverse primer carrying an in-frame FLAG tag . The cDNAs were cloned into pUAST plasmid to generate the dmpnpase-overexpressing fly lines . Primers used for the cloning of dmpnpase are listed in S1 Table . HeLa cells were cultured in high-glucose DMEM ( Thermo Scientific ) supplemented with 10% foetal bovine serum ( BSA ) at 37°C in a 5% CO2 atmosphere . For co-localisation studies , HeLa cells were transfected with a dmpnpase-GFP fusion construct , using a calcium phosphate transfection kit ( Sigma-Aldrich ) , following the manufacturer’s instructions . 48 hours after transfection HeLa cells were fixed with 4% PFA and decorated with anti-TOM20 antibody ( Santa Cruz , sc-11415 ) . Images were obtained with a Nikon Confocal Microscope at the Live Cell Imaging Unit , Karolinska Institutet . Dm nuclear , cytoplasmic and mitochondrial fractions were prepared from larvae four days after egg lay ( AEL ) by differential centrifugation as previously described [69] . Purity was assessed by Western blotting , using primary antibodies against Histone H3 ( Santa Cruz Biotechnology , dilution 1:200 ) , Complex I-subunit NDUFS3 ( Mitoscience MS112 , dilution 1:1000 ) , tubulin ( Sigma , dilution 1:1000 ) , porin ( Abcam , dilution 1:5000 ) and FLAG ( Sigma , dilution 1:1000 ) . Protein bands were visualised with Clarity Western ECL substrate ( Bio-Rad ) . To obtain Percoll purified mitochondria , 3 mg of crude mitochondria were layered on 20% Percoll in STE buffer ( 250 mM sucrose , 5 mM Tris , 2 mM EGTA , pH 7 . 4 ) and centrifuged at 40 , 000g for 30 min at 4°C in a Beckman SW41 rotor . The pure mitochondrial fraction was pipetted off the bottom of the tube and washed twice in STE buffer at 7 , 000g to dilute residual Percoll . Mitochondria were pelleted at 9 , 000g , flash-frozen in liquid nitrogen and stored at -80°C until further analysis . For mitochondrial isolation third-instar larvae were homogenised in ice-cold isolation buffer STE ( 250 mM sucrose , 5 mM Tris , 2 mM EGTA , pH 7 . 4 ) + 5% BSA ( w/v ) , using a Dounce homogeniser . Cellular debris was pelleted at 1 , 000g for 5 min and supernatants were transferred to new tubes . Mitochondria were washed twice at 3 , 000g and the final mitochondrial fraction was pelleted at 7 , 000g and resuspended in STE buffer . For determination of the activities of respiratory chain complexes , protein concentration of the mitochondrial preparations was determined using a Qubit fluorometer and mitochondria were resuspended in 250 mM sucrose , 15 mM KH2PO4 , 2 mM MgAc2 , 0 . 5 mM EDTA and 0 . 5 g/L BSA , pH 7 . 2 . Biochemical activities of the respiratory chain complexes were determined as previously described [70] . For oxygen consumption measurements , larvae at 4 days AEL were dissected and resuspended in Mir05 respiratory buffer ( 110 mM sucrose , 10 mM KH2PO4 , 3 mM MgCl2 , 0 . 5 mM EGTA , 60 mM lactobionic acid , 20 mM taurine , 20 mM HEPES , BSA 1 g/l pH 7 . 1 ) . Oxygen consumption was measured at 25°C using an oxygraph chamber ( OROBOROS ) . Larvae were permeabilised with 0 . 02 mg/ml digitonin . Complex I-dependent respiration was assessed by adding the substrates malate ( 2 mM ) and glutamate ( 10 mM ) , followed by addition of 2 . 5 mM ADP . Complex I-independent respiration was measured in the presence of 0 . 5 μM rotenone . Complex II-dependent respiration was measured using 10 mM succinate ( SUCC ) . Complex III was inhibited with 2 . 5 μM antimycin A . Finally , Complex IV activity was measured by addition of 2 mM ascorbate and 0 . 5 mM N , N , N’ , N’-tetramethyl-p-phenylenediamine dihydrochloride ( TMPD ) and subsequent addition of 1mM potassium cyanide . Assessment of the quality of the measurements was performed by adding 10 μM cytochrome c to the samples . Finally , the protein content was determined by the Bradford method ( BioRad ) in order to normalise the oxygen consumption flux to protein content . BN-PAGE and in-gel staining for complex I and IV activities was performed on isolated mitochondria . Mitochondria were pelleted and lysed in digitonin buffer ( 1% digitonin , 20 mM Tris pH 7 . 4 , 0 . 1 mM EDTA , 50 mM NaCl , 10% glycerol , 1 mM PMSF ) . After 15 min of incubation on ice , insolubilised material was removed by centrifugation at 4°C . The supernatant was mixed with 10x loading dye ( 5% ( w/v ) Coomassie Brilliant Blue G-250 , 100 mM Tris pH 7 , 500 mM 6-aminocaproic acid ) and loaded on 4–10% gradient BN-PAGE gels . In-gel complex I activity was determined by incubating the BN-PAGE gels in 2 mM Tris-HCl pH 7 . 4 , 0 . 1 mg/ml NADH and 2 . 5 mg/ml iodonitrotetrazolium chloride . In-gel complex IV activity was determined by incubating the BN-PAGE gels in 0 . 05mM phosphate buffer pH 7 . 4 , 0 . 5 mg/ml 3 . 3′-diamidobenzidine tetrahydrochloride ( DAB ) , 1 mg/ml cytochrome c , 0 . 2 M sucrose and 1 μg/ml catalase . Stainings were carried out at room temperature . Genomic DNA was isolated from 4 days AEL larvae with the DNeasy Blood and Tissue Kit ( Qiagen ) , following manufacturer’s instructions . Mitochondrial DNA levels were determined by quantitative real-time PCR ( qRT-PCR ) on a QuantStudio 6 Flex Real-Time PCR System ( Thermo Scientific ) , using Platinum SYBR Green qPCR supermix-UDG ( Thermo Scientific ) . Reactions were carried out in triplicates in a final volume of 20 μL with 5 ng of DNA and 10 pmol of specific primers ( primers are listed in S1 Table ) . Total RNA was isolated , using the ToTALLY RNA kit ( Thermo Scientific ) and quantified with a Qubit fluorometer ( Thermo Scientific ) . Reverse transcription was performed using High Capacity cDNA Reverse Transcription Kit ( Thermo Scientific ) . qRT-PCR was performed on a QuantStudio 6 Flex Real-Time PCR System , using the TaqMan Universal Master Mix II , with UNG and TaqMan assays ( Thermo Scientific ) or Platinum SYBR Green qPCR supermix-UDG ( Life Technologies ) . TaqMan assays and primers used for qPCR are listed in S1 Table . For Northern blot analysis 3 μg of total RNA was separated by neutral 10% PAGE for mitochondrial tRNA separation or 1% MOPS-formaldehyde agarose gels for mitochondrial mRNAs . Separated RNAs were transferred to Hybond-N+ membranes ( GE Healthcare ) and hybridised with either randomly [32P]-labelled dsDNA probes , [32P]-labelled strand-specific RNA probes or with strand-specific [32P]-end labelled oligonucleotide probes . Oligonucleotide used to generate all the probes are listed in S1 Table . Mitochondria were isolated from 4 days AEL larvae and in organello transcription assays were performed as previously described [32] . In brief , 200μg of fresh mitochondria were incubated for 45 min in transcription buffer ( 30μCi [32P]-UTP , 25mM sucrose , 75mM sorbitol , 100mM KCl , 10mM K2HPO4 , 50μM EDTA , 5mM MgCl2 , 1mM ADP , 10mM glutamate , 2 . 5mM malate , 10mM Tris-HCl pH 7 . 4 and 5% ( w/v ) BSA ) , followed by RNA extraction , separation on a 1% MOPS-formaldehyde agarose gel and transferring to Hybond-N+ membranes ( GE Healthcare ) . Mitochondrial de novo translation in isolated mitochondria was assayed as previously described [32] , using easy-tag EXPRESS 35S protein labelling mix ( Perkin Elmer ) . Equal amounts of mitochondrial protein were separated on 17% SDS-PAGE gels , followed by staining with 1g/L Coomassie Brilliant Blue in 20% ethanol and 10% acetic acid . Gels were then destained , dried and exposed to a PhosphorImager screen to visualise the mitochondrial translation products . The assay was performed as previously described [71] . In summary , 3 μg of isolated RNA was ligated to a phosphorylated oligonucleotide linker using T4 RNA ligase 1 ( New England Biolabs ) . RNA was precipitated and cDNA synthesis was performed using a primer complementary to the linker sequence ( anti-linker ) and SuperScript II Reverse Transcriptase ( Thermo Scientific ) . The 3′ end of mitochondrial RNAs was PCR amplified using the anti-linker and gene-specific primers . The PCR products were then cloned into pCRII-TOPO and transformed in One Shot TOP10 E . coli ( Thermo Scientific ) according to manufacturer’s instructions . The plasmids were purified and the insert was sequenced using M13 sequencing primers . The linker and primers for the 3′RACE experiments are listed in S1 Table . Western blot analyses were performed using mitochondrial protein extracts according to the Cell Signaling Technology protocol ( CellSignaling ) . Protein extracts were separated on 4–12% or 12% NUPAGE acrylamide gels ( Thermo Scientific ) and after transfer to PVDF membranes ( Millipore ) decorated with the following antibodies: Complex I-subunit NDUFS3 ( Abcam ab14711 , dilution 1:1000 ) , complex IV-subunit COX3 ( Abcam ab110259 , 1:500 ) , Tubulin ( Sigma , T6199 , dilution 1:2000 ) , Histone H3 ( Sigma , H0164 , dilution 1:1000 ) , Flag ( Sigma , F3165 , dilution 1:1000 ) and VDAC1 ( Abcam ab14734 , dilution 1:1000–2000 ) . Protein bands were visualised with Clarity western ECL substrate ( Bio-Rad ) . For J2 immunofluorescence on larvae brain , tissues were dissected in PBS , fixed for 5 minutes in 4% formaldehyde , and washed for 5 minutes . Larvae brain were then permeabilised for 2 hours in 0 . 5% Triton X-100 in PBS , and saturated for 1 hour in 0 . 5% BSA , 0 . 1% Tween 20 in PBS ( PBTB ) . Primary antibodies anti-dsRNA ( Scicons J2: anti-dsRNA/100105500 ) and anti ATP synthase subunit 5a ( Abcam ab151229 ) were used at 1:200 in PBTB for overnight at 4°C , and later washed for 1 hour in 0 . 1% Tween in PBS ( PBTW ) . Secondary antibodies ( Molecular Probes , IgG , 568 , A-11031 , and Life Technologies , Alexa Fluor488 , A-11008 ) were used at 1:500 for 2 hours and washed for 1 hour in PBTW . Preparations were mounted in Vectashield/DAPI ( Vector ) . A LSM880 Zeiss confocal microscope was used for imaging . Mitochondria of the ventral nerve cord were visualised by crossing dmpnpaseKO , dmsuv3KD , dmlrpprc1KD , dmmtpapKO , or dmpnpaseOE/dmsuv3OE flies to previously generated UAS-mit::dendra2 flies ( w;elav-gal4 , uasmit::dendra2; ) [54] . For in situ detection of mit::dendra2 , living larval central nervous systems were rapidly dissected , mounted into PBS , and immediately imaged , using a LSM880 Zeiss confocal microscope . Human PNPase ( PNPT1; NM_033109 ) or yeast DSS1p ( UniProtKB—P39112 ) were used in protein BLAST searches against the Dm reference protein database . ClustalW alignment was performed using Geneious R6 software ( Biomatters; http://www . geneious . com ) . DsRNA-seq was performed as described previously [53] with slight modifications . 5μg of RNA from purified mitochondrial and cytoplasmic fractions were used for IP with anti-dsRNA J2 ab . The RNA was diluted in 1 . 5ml NET-2 buffer ( reconstituted to 10mM MgCl2 and 0 . 1% NP-40 ) and incubated with 5μg of J2 ab bound to 50μl Protein-G beads for 2 hrs in cold room . Washings were done twice with HSWB and NET-2 buffer respectively . The bound RNA was extracted using Trizol . The resulting 100 ng of J2-IPed dsRNA were used to make the libraries according to the manual of NEBNext Ultra II Directional RNA Library Prep kit for Illumina ( New England Biolabs ) . Libraries were quantified using Agilent 2100 Bioanalyzer ( Agilent Technologies ) . Libraries were sequenced on Illumina NextSeq 550 with 42bp paired-end reads . Transcripts per million ( TPM ) values were determined by quasi-mapping of paired-end reads with salmon v0 . 11 . 3 [72] against a cDNA library of BDGP6 ( release 94 ) . Normalisation factors were calculated from the average TPM values of all transcripts with average TPM values above 14 , which were 28SrRNA-Psi:CR40596 , 18SrRNA-Psi:CR41602 , 28SrRNA-Psi:45851 , 5 . 8SrRNA-Psi:CR45854 , 28SrRNA-Psi:CR45855 , 28SrRNA-Psi:CR45859 , 18SrRNA-Psi:CR45861 and 28SrRNA-Psi:CR45862 , excluding mitochondrial lrRNA and srRNA . Data was visualised with R v3 . 5 . 1 ( accessed July 2018 ) ( R code Team 2018 ) . IGV views were generated after mapping paired fastq-files with bowtie2 v2 . 3 . 4 . 3 ( September 2018 , [73] ) against a BDGP6 index , and conversion and sorting with samtools v1 . 9 [74] . Sorted bam-files were indexed and opened with the Integrated Genomics Viewer 2 . 4 . 16 Java application [75] . All data were analysed using Prism 6 software and are represented as mean ± standard error of the mean ( SEM ) . An unpaired t-test was used to analyse the statistical significance of the results . The exception is for the poly ( A ) tail data , where the error bars represent the mean ± SD and Mann-Whitney test was used to analyse the statistical significance of each experimental group sets . | Although a number of factors have been implemented in the turnover of mitochondrial ( mt ) DNA-derived transcripts , their exact functions and interplay with one another is not entirely clear . Several of these factors have been proposed to co-ordinately regulate both transcript maturation , as well as degradation , but the order of events during mitochondrial RNA turnover is less well understood . Using a range of different genetically modified Drosophila melanogaster models , we studied the involvement of the RNA helicase SUV3 , the polynucleotide phosphorylase PNPase , the leucine-rich pentatricopeptide repeat motif-containing protein LRPPRC , and the mitochondrial RNA poly ( A ) polymerase MTPAP , in stabilisation , polyadenylation , and degradation of mitochondrial transcripts . Our results show a tight collaborative activity of these factors in vivo and reveal a clear hierarchical order of events leading to mitochondrial mRNA maturation . Furthermore , we demonstrate that the loss of SUV3 , PNPase , or MTPAP leads to the accumulation of mitochondrial-derived antisense RNA in the cytoplasm of cells , which is associated with an altered immune-response in flies . | [
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] | 2019 | Defects of mitochondrial RNA turnover lead to the accumulation of double-stranded RNA in vivo |
Understanding the genetic and environmental factors that affect variation in life span and senescence is of major interest for human health and evolutionary biology . Multiple mechanisms affect longevity , many of which are conserved across species , but the genetic networks underlying each mechanism and cross-talk between networks are unknown . We report the results of a screen for mutations affecting Drosophila life span . One third of the 1 , 332 homozygous P–element insertion lines assessed had quantitative effects on life span; mutations reducing life span were twice as common as mutations increasing life span . We confirmed 58 mutations with increased longevity , only one of which is in a gene previously associated with life span . The effects of the mutations increasing life span were highly sex-specific , with a trend towards opposite effects in males and females . Mutations in the same gene were associated with both increased and decreased life span , depending on the location and orientation of the P–element insertion , and genetic background . We observed substantial—and sex-specific—epistasis among a sample of ten mutations with increased life span . All mutations increasing life span had at least one deleterious pleiotropic effect on stress resistance or general health , with different patterns of pleiotropy for males and females . Whole-genome transcript profiles of seven of the mutant lines and the wild type revealed 4 , 488 differentially expressed transcripts , 553 of which were common to four or more of the mutant lines , which include genes previously associated with life span and novel genes implicated by this study . Therefore longevity has a large mutational target size; genes affecting life span have variable allelic effects; alleles affecting life span exhibit antagonistic pleiotropy and form epistatic networks; and sex-specific mutational effects are ubiquitous . Comparison of transcript profiles of long-lived mutations and the control line reveals a transcriptional signature of increased life span .
Understanding the genetic and environmental factors affecting variation in life span and health span is of major interest for human health and evolutionary biology . As the world population ages , the incidence of age-related diseases , such as Alzheimer's disease , cancer , cardiovascular disease and Huntington's disease , is concomitantly increasing . From the evolutionary perspective , we seek to understand why aging occurs , and why there is variation in aging between and within species [1] , [2] . Multiple mechanisms affecting longevity have been documented , many of which are conserved across species . Dietary restriction [3]–[6] , oxidative stress [7]–[8] and insulin/IGF signaling ( IIS ) [9]–[17] all affect longevity . Additional processes that change with age include stress response [18] , [19] , telomere shortening [20] and gene silencing [21] , [22] . Life span extension is often accompanied by a decline in reproduction [23]–[27] , a well–known trade–off that could explain limits to life span and maintenance of genetic variation for longevity within species [28]–[30] . However , this relationship is not universal [31]–[34] . Similarly , positive correlations between life span and stress resistance [18] are not always observed [35] . Given the heterogeneity of mechanisms affecting life span and the need to understand the genetic networks underlying each mechanism as well as cross–talk between networks , there is a clear need for unbiased , genome–wide screens to identify genes and genetic networks affecting life span . Studies using microarray technology to observe changes in gene expression during normal aging or following exposure to conditions that extend or reduce life span have indeed confirmed that expression of a substantial fraction of the genome changes with age [36]–[44] . However , these analyses are correlative , and cannot distinguish between changes in gene expression that cause aging from changes in gene expression that are a consequence of aging . Genetic screens for mutations affecting life span give unambiguous insight regarding the genes and pathways required for normal aging , as elegantly demonstrated by mutagenesis and RNAi screens in the short-lived model organism , C . elegans [45]–[49] . Genetic screens for mutations affecting life span are more laborious in longer-lived species , such as Drosophila , and consequently there have been relatively few studies reporting mutations increasing life span in this organism [50]–[54] . Here , we report the results of a screen for mutations affecting Drosophila life span , utilizing a collection of over 1 , 000 single , homozygous P–element insertion lines that were constructed in isogenic backgrounds [55] . We identified 58 mutations with increased longevity , only one of which is in a gene previously associated with life span . The effects of the mutations are highly sex–specific , with life span extensions ranging from 5%–33% . The mutations have pleiotropic effects on resistance to starvation stress , chill coma recovery time , and locomotion , but the pleiotropic effects are highly variable . All of the mutations associated with increased life span have at least one deleterious pleiotropic effect on stress resistance or general health , indicating the complicated mutational basis of trade–offs between putative fitness components . We performed a quantitative genetic analysis of epistasis [56]–[58] among ten of these mutations to derive genetic interaction networks , and found that epistasis is pervasive and sex–specific . Finally , we obtained whole genome transcript profiles of seven of the mutant lines and the wild type control to evaluate the biological impact of the mutant alleles [59]–[61] and derive a common transcriptional signature of increased life span .
To identify mutations affecting Drosophila life span , we quantified the life span of males and females of 1 , 332 homozygous P{GT1} insertion lines [55] , [57] , [58] , [62] , [63] simultaneously with their co–isogenic control lines ( Table S1 ) . Analysis of variance ( ANOVA , Table 1 ) revealed significant variation in life span among the P–element insert lines ( P<0 . 0001 ) as well as significant sex–specific effects on life span ( P<0 . 0001 ) . Our estimates of the broad–sense mutational heritability ( HM2 ) and the cross–sex mutational genetic correlation ( rFM±SE ) of life span were HM2 = 0 . 557 and rFM = 0 . 555±0 . 025 . Averaged over all mutations , the standardized effects ( a/σP [64] ) of the P–element insertions on life span were slightly negative , with a/σP = −0 . 41 in females and a/σP = −0 . 45 in males . To identify the individual P–element insert lines that contributed to the significant variation in life span , we computed the confidence intervals ( CIs ) of deviations of line means from their corresponding controls ( Figure 1 ) , and performed Dunnett's t–tests to assess deviations of insert lines from the control line within each experimental block . Combining the results of both analyses , we identified 296 lines associated with reduced life span , 135 with increased life span , and 5 with sexually antagonistic effects on life span . At the 95% , 99% and 99 . 9% CIs , respectively , 139 ( 194 ) , 55 ( 95 ) and 12 ( 49 ) lines had significantly increased ( decreased ) life span in at least one sex or averaged across sexes ( Table S1 ) . The Dunnett's tests indicated 70 ( 270 ) lines had increased ( decreased ) life span ( P<0 . 05 , after correction for multiple tests ) ( Table S1 ) . Both analyses indicate an asymmetrical distribution of mutational effects , with more mutations decreasing than increasing longevity , as expected for components of fitness . It is generally assumed that mutations decreasing life span are less interesting than mutations increasing life span , since the former category of mutations could be generally deleterious and affect all aspects of fitness , while the latter are more likely to have specific effects on life span . Thus , we concentrated on confirming the effects of mutations associated with increased life span . We chose 83 mutations with increased life span and re-assessed their life span using larger sample sizes . We found that 58 of the 83 mutations ( 70% ) remained formally significant for at least one sex , and 43 lines had effects that were significant following a Bonferroni correction for multiple testing ( P≤0 . 0006 ) ( Table 2 ) . Thus , 4 . 4% of the mutations we screened are associated with increased longevity . This indicates a large mutational target for longevity and extensive pleiotropy among genes affecting life span . We quantified the magnitude of the mutational effects on life span for the 58 mutations with increased life span in terms of percentage increase over the control strain , and by computing their standardized mutational effects , a/σP [64] ( Table 2 ) . The effects of the mutations on life span correspond to an average change in longevity relative to the control of 12% pooled across sexes , 17% in males and 15% in females . The average absolute value of a/σP is 0 . 27 pooled across sexes , 0 . 43 in males and 0 . 39 in females . Thus the average effects of P–element insertions on longevity , although statistically significant , are subtle , but effects range from 5% to as large as 33% . We observed substantial variation in sexual dimorphism for the effects of P{GT1}–element insertions on longevity , as indicated by significant line by sex interaction terms in the ANOVAs pooled across sexes ( Table 2 ) . The cross–sex mutational genetic correlation for longevity among the 58 long-lived mutant lines was negative and significantly different from zero ( rFM = −0 . 295±0 . 128 , t56 = 2 . 308 , P<0 . 05 ) . Mutations associated with an increase in longevity have highly sex–specific effects , with a trend towards opposite effects in males and females . We used the pattern of significance of the line ( L ) and line by sex ( L×S ) terms from ANOVAs comparing the life span of each long-lived mutant line to the control to infer whether the mutations affected both sexes , or were sex–specific , sex-biased , or sex antagonistic ( Table 2 ) . Mutations in 17 lines affected both sexes ( the L term was significant , but the L×S term was not significant ) . The remaining 41 mutations ( 70 . 7% ) affected males and females differently . We categorized the mutational effects as “sex–specific” if the L×S interaction from the analysis pooled across sexes was significant , and the L term from the separate sex analysis was significant only in one sex; “sex-biased” if the L and L×S terms from the analysis pooled across sexes were both significant , and the L term from the separate sex analysis was significant in both sexes; and “sex–antagonistic” if the L term from the analysis pooled across sexes was not significant , but the L×S interaction was significant , and the L term from the separate sex analysis was significant in both sexes . We found 22 male–specific , two male-biased , nine female–specific , two female-biased , and six sex–antagonistic mutations ( Table 2 ) . To identify candidate genes affecting life span , we mapped the sequences flanking the P–element insertion sites to the reference genome ( Table S1 , Table 3 ) . The flanking sequences of 47 of the P–element mutations associated with increased life span mapped to unique insertion sites ( Table 3 ) . Eight of the P–element insertions were ≥2 kb from the nearest annotated gene , and either have long–range effects on the nearest neighboring gene ( s ) or affect an un-annotated gene in the more immediate vicinity . The remaining 39 P–element inserts were <2 kb from the nearest gene . Of these , 27 were adjacent to or within the predicted transcript of the only gene in the region , and most parsimoniously affect these genes . A total of 13 inserts were located in an intergenic region , <2 kb from each flanking gene , and could affect either or both adjacent genes . Only one of the P–element tagged candidate genes , forkhead box , sub–group O ( foxo ) has been previously implicated to affect adult life span [26] , [34] . All others are novel candidate genes affecting longevity , and fall into a wide range of gene ontology categories , including early development , metabolism , chemosensation , immune response and transcription factors ( Table 3 ) . Several of the P–elements inserted into identical or nearly identical positions: five inserts in the first intron of mushroom–body expressed ( mub ) , two inserts 500 base pairs upstream of polychaetoid ( pyd ) , two inserts adjacent to CG9238 , two inserts in the Tre1/Gr5a intergenic region , and two inserts between CG8418 and Gef64C . Since this screen is far from saturation , these sites likely represent hotspots for P{GT1} element insertion [65] . The effects of multiple inserts in the same genomic region are often , but not always , heterogeneous . Two of the inserts in mub affected both sexes , two were male–specific , and one was female–specific . One insert near CG9238 was female–specific , while the other affected both sexes . One of the inserts in the CG18418/Gef64C intergenic region affected both sexes , while the other was strongly female-biased . On the other hand , both inserts in the Tre1/Gr5a intergenic region affected both sexes , and both inserts near polychaetoid were male–specific . To add to the complexity , not all inserts in the same gene affect longevity in the same direction . The mutations in esg , pyd and mub associated with increased life span were all in the Canton S F genetic background . In addition , seven mutations in esg , two mutations in pyd , and two mutations in mub were associated with reduced life span in the initial screen . All of these mutations were in the Canton S A or B genetic backgrounds , and with one exception ( mubBG02497 ) were in different locations from the mutations in these genes associated with increased life span ( Table S1 ) . We re-mobilized the P–element insertions in three of the lines associated with increased life span ( mubBG00043 , crolBG00346 and esgBG01042 ) to create revertant alleles in which the P–element was precisely ( or nearly precisely ) excised , while maintaining the co–isogenic background . We measured the life span of the revertant alleles , the parental strains , and the P–element insert line simultaneously . If the disruption of the adjacent gene by the P–element insertion causes the increase in life span , we expect that the life span of the revertant alleles will not be significantly different from the control . This expectation was realized for each of the revertant alleles . The mubBG00043 allele was associated with increased life span in both sexes . We obtained one precise revertant ( mubrev1 ) and one imprecise revertant ( mubrev3 ) . Both revertant alleles had mean female life spans that fully reverted to the control , whereas the mean male life spans were intermediate between the control and mutant line ( Figure 2 ) . The crolBG00346 and esgBG01042 alleles were both associated with increased male life span , and the male life spans of the precise revertant alleles crolrev4 and esgrev3 were not significantly different from the control ( Figure 2 ) . These results show that the P–element mediated gene disruption is indeed responsible for the mutant phenotypes . Since all independently isolated long-lived P–element insertions result in increased life span , we asked whether these genes would be part of interacting genetic networks , and , if so , to what extent such networks would differ between the two sexes . We selected 10 P–element insertion lines in the Canton S F genetic background to assess epistatic interactions affecting life span , using a half–diallel crossing scheme in which all possible double heterozygotes were constructed ( without reciprocals ) [56]–[58] . The mean life spans of all double heterozygote genotypes are given in Table S2 . We observed significant variation in life span among the double heterozygote genotypes ( P<0 . 0001 ) , between males and females ( P<0 . 0001 ) , and the genotype by sex interaction ( P<0 . 0001 ) ( Table S3 ) . The effect of double heterozygous genotype was also highly significant in the individual analyses of males and females ( Table S3 ) ; however , the cross–sex genetic correlation , rFM = −0 . 276±0 . 146 , is not significantly different from zero ( t43 = 1 . 88 , P>0 . 05 ) . Thus , the effects of the double heterozygous genotypes on life span are independent in the two sexes . Variation among the double heterozygote genotypes can arise from two sources: variation in mean heterozygous effects of the different mutations , and variation from epistatic interactions . Since all mutations are in the same co–isogenic background , all genetic variation among the genotypes must be attributable to one these sources . Diallel cross analysis enables us to partition the variation among the double heterozygous genotypes into their general ( GCA ) and specific ( SCA ) combining abilities . The GCA of each mutation estimates its average dominance in combination with all other mutations . The SCA of each double heterozygous genotype is the difference in the observed life span of the genotype from that expected given the GCAs of the two parental lines . Since alleles at all other loci are fixed and homozygous , any statistically significant SCA values must be due to dominance×dominance epistasis . We found significant variation in GCA and SCA values ( P<0 . 0001 ) when pooled over both sexes , as well as significant GCA×Sex and SCA×Sex interaction terms ( P<0 . 0001 ) , indicating sex–specific GCA and SCA effects ( Table S3 ) . We estimated the GCA effects of each mutation and the SCA effects of all double heterozygous genotypes ( Table S4 ) . Epistatic effects can either suppress or enhance the mutant phenotype: the former occurs when the life span of a double heterozygote genotype is less than expected ( closer to the wild–type , with a negative SCA ) , and the latter when the life span of the double heterozygote genotype is greater than expected ( longer-lived , with a positive SCA ) . We identified eight statistically significant epistatic interactions in the analysis pooled across sexes; 10 significant interactions for females and 14 significant interactions for males ( Figure 3 , Table S4 ) . The cross–sex correlation of SCA values was rFM = −0 . 137±0 . 151 , which is not significantly different from zero ( t43 = 0 . 907 , P>0 . 05 ) . Thus , when examined separately the two sexes displayed vastly different epistatic interactions ( Figure 3 , Table S4 ) . Of the 21 significant epistatic interactions in males and/or females , only one was common to both sexes , 16 were unique to each sex , and three epistatic interactions were sexually antagonistic , with enhancing effects in one sex and suppressing effects in the other ( BG00817–BG00004 , BG00817–esg , BG00004–CG9238 ) . We assessed whether mutations with significantly increased life span had pleiotropic effects on stress resistance ( chill coma recovery and starvation resistance ) as well as a general measure of health ( climbing activity ) at one week and six weeks of age . We observed substantial pleiotropy . Of the 50 lines tested for starvation resistance , 44 were significantly different from the control at one week ( 16 with increased starvation resistance and 28 with decreased starvation resistance in one or both sexes ) , and 46 were significantly different from the control at six weeks ( five with increased starvation resistance and 42 with decreased starvation resistance – one line had sexually antagonistic effects ) ( Figure 4 , Table S5 ) . Of the 50 lines tested for chill coma recovery , 32 were significantly different from the control at one week ( 15 with decreased chill coma recovery times and 17 with increased chill coma recovery times ) , and 42 were significantly different from the control at six weeks ( 29 with decreased chill coma recovery times and 13 with increased chill coma recovery times ) ( Figure 4 , Table S5 ) . We only assessed 40 of the lines for climbing ability . Of these , 23 were significantly different from the control at one week ( 14 with increased climbing ability and nine with decreased climbing ability ) , and 30 were significantly different from the control at six weeks ( 28 with increased climbing ability and two with decreased climbing ability ) ( Figure 4 , Table S5 ) . Thus , on average , by six weeks of age the lines with increased longevity have overall decreased resistance to starvation stress , but increased resistance to chill coma stress and increased general activity relative to the controls . There was significant variation among the lines and significant sex by line interactions for all three traits ( Table S6 ) , indicating that the mutations do indeed have heterogeneous pleiotropic effects , and that the effects are sex–specific . Broad sense mutational heritabilities ranged from H2 = 0 . 43–0 . 60 for starvation resistance and chill coma recovery , but were lower for climbing ability ( H2 = 0 . 21 averaged over week 1 and week 6 measurements ) ( Table 4 ) . Although all cross–sex genetic correlations were significantly different from unity , the estimates of rMF were high for all traits except for climbing ability at six weeks ( Table 4 ) . If the mutations affecting increased life span are generally more robust , we would expect positive correlations between life span and stress resistance and general health , expressed as deviations from the control . Similarly , if the mutations affecting increased life span have delayed senescence , the correlations between longevity and the other traits should be positive at six weeks of age , when the control line flies are beginning to die , but the long-lived mutant individuals are still alive . However , this was not the pattern observed . We consider the overall pleiotropic effects of the mutations separately for males and females , since the effects of the mutations on life span were not correlated between the sexes . In females , the correlation ( ±SE ) between longevity and chill coma recovery time was positive and significant at both one week ( r = 0 . 328±0 . 136 , t48 = 2 . 41 , P = 0 . 020 ) and six weeks ( r = 0 . 418±0 . 131 , t48 = 3 . 19 , P = 0 . 0025 ) ( Table 5 ) . Thus , there is a tendency for mutations affecting female life span to be inversely associated with resistance to chill coma stress , at either age . The correlation between starvation resistance and climbing ability was significant and negative at one week ( r = −0 . 329±0 . 153 , t38 = 2 . 15 , P = 0 . 038 ) . None of the other correlations were significantly different from zero ( Table 5 ) . In males , however , the correlation ( ±SE ) between longevity and starvation resistance was positive and significant at both one week ( r = 0 . 303±0 . 138 , t48 = 2 . 20 , P = 0 . 038 ) and six weeks ( r = 0 . 554±0 . 120 , t48 = 4 . 61 , P = <0 . 0001 ) ( Table 5 ) . Further , the correlation between male life span and chill coma recovery time was negative and significant at six weeks ( r = −0 . 577±0 . 118 , t48 = 4 . 90 , P = <0 . 0001 ) ( Table 5 ) . Thus , mutations affecting male life span do show the expected positive associations with stress resistance and delayed senescence for stress resistance . However , the correlation between male starvation stress resistance and climbing activity was significant and negative at one week ( r = −0 . 619±0 . 127 , t38 = 4 . 86 , P = <0 . 0001 ) ; i . e . , mutations associated with increased stress resistance were less active ( Table 5 ) . The combination of significant pleiotropy but little directional correlation between longevity and other traits indicates that the pleiotropic effects are highly variable , as illustrated in Figures S1 , S2 , S3 , S4 . The complex pattern of variation in pleiotropic effects among the P–insert lines associated with increased life span in at least one sex is depicted in Figure 4 . Notably , all of the mutations are associated with at least one deleterious pleiotropic effect on stress resistance or general health , indicating the complicated mutational basis of trade–offs between putative fitness components . Genetic networks of mutations that affect a common phenotype can serve as focal points for the identification of additional candidate genes affecting that phenotype by transcript profiling [59] . Transcripts that are co-regulated in the genetic background of the mutant lines are themselves candidate genes affecting longevity , and the clustering of co-regulated transcripts can yield insights about the function of predicted genes tagged by the mutations . We assessed the extent to which seven of the mutations associated with increased life span ( pydBG00028 , mubBG00043 , crolBG00346 , CG10990BG00495 , CG9238BG00761 , BG00817 and esgBG01042 ) affected whole genome transcriptional regulation . We performed these analyses at six weeks of age for all mutant lines and the Canton S F co–isogenic control – the age at which the control lines are beginning to die , but at which most of the P–element insert lines remain alive , and at which we assessed differences among these lines in senescence . The survival curves for this experiment are given in Figure 5 . We independently confirmed the effects of all mutations on life span , with one exception . In our initial and secondary screens , mubBG01042 females had reduced longevity , but in this assay , both males and females were long-lived . Not all transcripts on the array are expressed in six week old adults . We eliminated all transcripts that were not considered present in both replicates of at least one line and sex , leaving 12 , 636 transcripts for analysis . We performed several analyses of variation of gene expression ( Table S7 ) . First , we assessed the extent to which there was variation in the main effects of sex , genotype , and the genotype by sex interaction among all lines for each transcript , using a false discovery rate criterion to account for multiple tests [66] . At a q–value of 0 . 001 ( 0 . 0001 ) , we found 11 , 111 ( 10 , 603 ) sexually dimorphic transcripts . Remarkably , genotype was significant for 4 , 488 transcripts ( 35 . 5% ) at q≤0 . 001 , and 1 , 996 transcripts ( 15 . 8% ) at q≤0 . 0001 . The genotype by sex interaction was significant for 1 , 621 transcripts ( 12 . 8% ) at q≤0 . 001 , and 434 transcripts ( 3 . 4% ) at q≤0 . 0001 . We also ran reduced ANOVAs separately for each sex , and for each of the mutant lines compared to the control . A total of 619 and 561 transcripts were significant at q≤0 . 001 for females and males , respectively . The magnitude of transcriptional co–regulation varied among the mutant lines . At a significance level of q≤0 . 05 , we observed 276 significant transcripts for CG10990BG00495; 313 for pydBG00028; 777 for CG9238BG00761; 1 , 815 for BG00814; 2 , 141 for crolBG00346; 2 , 193 for esgBG01042; and 3 , 969 for mubBG00043 . We analyzed the Biological Process Gene Ontology ( GO ) categories represented by the significant transcripts to determine if particular categories are over-represented . In the separate sex analyses of all genotypes , there was over–representation of significant transcripts in the DNA integration , metabolism ( particularly carbohydrate metabolism ) and proteolysis categories in both sexes ( Table S8 ) . Genes affecting detection of external stimuli , particularly light and abiotic stimuli , were enriched in females , while genes affecting mating and reproductive behavior and muscle development were enriched in males ( Table S8 ) . Although all of the mutations assessed are long-lived , they have variable and sex–specific pleiotropic effects on longevity , resistance to starvation and chill coma stress , and climbing activity ( Figure 4 and Figure 6 ) . Thus , we expected to find both common and variable patterns of transcriptional co–regulation among the mutations . This is indeed what we observed . pyd affects the biological processes of the cell–cell adhesion , fusion cell fate specification and branch fusion in the open tracheal system ( Table 3 ) . Over-represented co-regulated transcripts in the pydBG00028 mutant background fell into the categories of DNA integration; prosthetic group , pyruvate , nucleoside , lipid , chitin and glucosamine metabolism; proteolysis; and mating and reproductive behavior ( Table S8 ) . mub is a regulator of alternative nuclear mRNA splicing via the spliceosome , and is hence likely to have far-reaching pleiotropic effects ( Table 3 ) . Categories that are over-represented among co-regulated mubBG00043 probe sets are consistent with this annotation , and include DNA replication and repair , RNA processing , the cell cycle , and chromatin modification and silencing . However , the largest over-represented categories in this mutation were in DNA , RNA , cellular and macromolecular metabolism ( Table S8 ) . crol is an RNA polymerase II transcription factor that has pleiotropic effects on cell adhesion and proliferation , regulation of transcription , wing morphogenesis and regulation of the mitotic cell cycle and the Wnt receptor signaling pathway . Over-represented transcripts in the crolBG00346 mutation primarily affect ribosome biogenesis , histone mRNA 3′ end processing and metabolism , transcription , protein metabolism and proteolysis , sleep , and reproductive and mating behavior ( Table S8 ) . CG10990 is a predicted gene of unknown function ( Table 3 ) . The top over-represented GO categories in CG10990BG00495 mutants are DNA integration , peptidyl–proline modification and amino acid derivative metabolism; but insulin signaling , proteolysis , and mating , reproductive and locomotor behavior are also over-represented ( Table S8 ) . CG9238 is a predicted gene that is annotated to regulate protein phosphatase type 1 activity . Type 1 protein phosphatase is involved in the regulation of many processes so it is not that surprising that the CG9238BG00761 mutant background is over-represented in several categories , including metabolism , embryonic and larval development , as well as visual , locomotor , mating , reproductive and rhythmic ( circadian ) behaviors ( Table S8 ) . We do not know the exact insertion site of the P–element in line BG00817 . However , many GO categories associated with muscle development are highly over-represented among significant co-regulated transcripts in this line . Lipid catabolism , proteolysis , and lipid , carbohydrate and protein metabolism are also over-represented ( Table S8 ) . esg is an RNA polymerase II transcription factor with pleiotropic effects on multiple biological processes: central nervous system development , germ–line stem cell maintenance , regulation of compound eye pigmentation , olfactory behavior , asymmetric neuroblast division and maintenance of imaginal histoblast diploidy . The large number of over-represented GO categories among the co-regulated transcripts in the esgBG01042 mutation is consistent with highly pleiotropic functions of esg . Genes involved in RNA processing and localization , ribosome biogenesis , RNA and DNA metabolism , primary metabolism and fertilization are over-represented . However , the most significant over-representation of co-regulated transcripts in esgBG01042 is related to vision ( response to light , visual perception , phototransduction ) ( Table S8 ) . Since all seven mutant lines have increased life span relative to the control , we sought to define the transcriptional signature of increased life span from the probe sets with common patterns of co–regulation across multiple lines . A total of 553 transcripts were common to four or more of the mutant lines; of these , 187 probe sets were up-regulated relative to the control and 270 were down-regulated relative to the control ( Table S9 ) . The up-regulated probe sets are enriched for genes affecting proteolysis , whereas the down-regulated transcripts are enriched for genes affecting gene expression and RNA metabolism . However , the transcriptional signature of increased life span is most notable for the large number of computationally predicted transcripts of unknown function as well as the diversity of biological functions represented . The transcripts in common to four or more of the mutant lines are encoded by genes affecting reproduction , chemosensation , metabolism , immunity/defense response , function of the nervous system and development . Genes that are co-regulated in the mutant backgrounds are themselves candidate genes affecting life span . Therefore , we tabulated variation in gene expression of known genes affecting life span in the mutant lines associated with increased life span ( Table S10 ) . First , five of the six focal genes for which we know the genes tagged by the P–element ( pyd , mub , CG10990 , CG9238 and esg ) are themselves significantly differentially expressed in the analysis considering all genotypes . Three of these genes ( mub , CG10990 and CG9238 ) are also differentially expressed relative to the control in their own mutant backgrounds . Further , mub is differentially expressed in the pydBG00028 and esgBG01042 mutant lines , and esg is differentially expressed in the pydBG00028 , crolBG00346 and CG9238BG00761 mutant lines . Six additional genes in which P–element mutations were associated with increased life span in our screen were differentially regulated among the seven mutations profiled in the array analysis ( CG31531 , Trapped in endoderm–1 , CG18418 , meiotic from via Salaria 332 , kayak and Dek ) . A further 13 genes in which P–element insertions were associated with decreased life span had differentially regulated transcripts in the mutant backgrounds ( CG14478 , CG31176 , CG4004 , CG6854 , couch potato , inaF , ken and barbie , Laminin A , Lipid storage droplet–2 , Malic enzyme , Protein kinase 61C , Rab23 and singed ) . Finally , eight genes in which mutations have been described to negatively regulate life span were also differentially co-regulated in the mutant backgrounds ( I'm not dead yet , chico , Insulin–like receptor , Superoxide dismutase , Alcohol dehydrogenase , Sirt2 , Vacuolar H+–ATPase SFD subunit and CTP:phosphocholine cytidylyltransferase 1 ) .
We performed an unbiased , forward genetic screen of 1 , 332 P{GT1} insertional mutations that were generated in one of six Canton S co–isogenic backgrounds for mutations affecting Drosophila longevity . In the initial screen , we identified 436 ( 32 . 7% ) mutations with mean life spans that were significantly different from their co–isogenic control . Of these , 296 ( 67 . 89% ) were associated with reduced life span , 135 ( 30 . 96% ) were associated with increased life span , and 5 ( 1 . 15% ) had sexually antagonistic effects on life span . The sample size per mutation in the initial screen was not large; therefore , many of the significant effects could be false positives . Nevertheless , if nearly one–third of mutations affect life span , the mutational target size for longevity must be large , consistent with the many mechanisms that are known to affect life span . We know the locations of the P–element inserts for 290 of the mutations associated with significant effects on longevity . Of these , 56 map to gene deserts ( regions of the genome with no computationally predicted genes ) and likely define novel un-annotated genes . With the exception of foxo [26] , [34] , none of the mutations tagged genes that have been previously associated with life span . Thus , forward genetic screens for mutations with subtle , quantitative effects on life span in a co–isogenic background is an efficient method for identifying novel genes affecting longevity and other complex traits [57] , [58] , [62] , [63] , [67] . Substantially more mutations were associated with decreased than increased life span . It is generally assumed that mutations decreasing life span are less interesting than mutations increasing life span , since the former category of mutations could be generally deleterious and affect all aspects of fitness , while the latter are more likely to have specific effects on life span . Thus , we concentrated on confirming the effects of mutations associated with increased life span with larger sample sizes in a secondary screen , and identified 58 mutations associated with increased life span . The mutations associated with significant increases in life span represent pathways known to affect life span ( e . g . , the insulin and metabolic pathways ) , as well as novel pathways involving taste , the nervous system and embryonic development . Mutations reducing life span are typically inferred to be in genes required for normal life span; over–expression of such genes may extend longevity , as has been observed for dFOXO [26] . Conversely , mutations increasing life span are thought to be in genes that normally function to limit life span; reducing expression of these genes thus extends longevity [27] , [68] . This logic presumes that all mutations in genes affecting life span have effects in the same direction . The proclivity of P–elements to insert in genomic hot–spots generated many insertions in the same genes enabled us to observe directly the distribution of mutational effects in the same genes . While many mutations in the same genes did indeed have similar effects on life span , this was not always true . Mutations in the same gene can be associated with both increased and decreased life span , often in a sex–specific manner , depending on the location and orientation of the P–element insertion , and genetic background . Examples include insertions in the Tre1/Gr5a intergenic region [63] , mub , pyd and Defense repressor 1 ( Dnr1 ) ( Table 2 , Table S1 ) . These observations highlight the inaccuracy of referring to genes that are required for normal life span or that normally limit life span . Mutational analysis identifies genes that are relevant to the modulation of life span , but variable allelic effects preclude inferring directionality of wild type function . Mutations in different locations in the same gene could have variable effects on longevity if they interfere with different aspects of gene regulation , or if some are in regulatory regions and others directly affect the protein . Different mutational effects could also arise due to variation in the amount of the vector inserted into the genome or by partial genomic excision during the insertion process . Variable effects of mutations in the same location and orientation but different genetic backgrounds may also be attributable to epistatic interactions with different alleles . Indeed , diallel crosses among just 10 of the mutations associated with increased life span revealed a surprisingly complex network of epistatic interactions involving all 10 mutations , suggesting pervasive epistasis between alleles affecting life span . Mutations in the Tre1/Gr5a intergenic regions interact epistatically with mutations in genes affecting insulin signaling [63] . It will be interesting to determine to what extent the other mutations interact with components of this well-established pathway , and to what extent the effects on life span are independent of insulin signaling . Epistasis has repeatedly been observed between QTL alleles affecting variation in life span [69] , [70] as well as between QTL alleles without main effects on life span [71] , although the identities of the genetic loci underlying the QTLs are not known . Further evidence for the importance of epistasis in the genetic architecture of Drosophila life span comes from observations that the effects of transgene over–expression and single mutations on longevity vary according to genetic background . The effect on increased life span of over-expressing Drosophila Superoxide dismutase was greater in the background of a relatively short-lived strain than in a long-lived strain background [72] . Similarly , the Indy mutation increased life span by 40–80% in the short-lived Shaker , Hyperkinetic and drop dead strains , but only by 15% in a strain selected for increased life span [52] . Over–expression of human SOD in Drosophila motor neurons increases life span [7] , but the magnitude of the increase varies in different wild type genetic backgrounds in a sex–dependent manner [73] . Likewise , introgression of each of three morphological mutations into seven wild-derived backgrounds showed considerable background–dependent effects on life span [74] . These observations highlight the importance to assess the effects of the mutations increasing life span in a range of naturally derived genetic backgrounds , and to identify the genes with which the mutations interact . A striking feature of our screen is that the effects of mutations increasing life span are highly sex–specific , with a low , but significant , negative cross sex–genetic correlation of rMF∼−0 . 3 . Epistatic effects were similarly sex–specific , and in three cases the direction of the epistasis was opposite in males and females . This observation is consistent with previous studies documenting sex–specific effects on life span , beginning with Maynard Smith's [75] analysis showing that the genetic control of longevity was independent in D . subobscura males and females . More recently , QTLs affecting variation in life span between two laboratory strains , Oregon and 2b , have sex–specific effects [69] , . Most studies of aging examine only one sex [80] , but when both sexes are included , sex–specific mutational effects are surprisingly common . For example , the effects of mutations in the Drosophila insulin–like receptor ( InR ) [16] , the insulin receptor substrate chico [15] and DTS–3 , a gene involved in ecdysone biosynthesis [54] had female-biased or female–specific effects on life span . As noted above , over–expression of human SOD in Drosophila motor neurons in different genetic backgrounds has sex–specific effects on life span [73] . Further , the benefits of dietary restriction on increased life span of D . melanogaster are greater in females than males [81] . Conditional over–expression of both wild type and mutant p53 transgenes has sexually antagonistic effects on male and female life span that are in opposite directions depending on the developmental stage of over–expression [82] . The causes of the sex–specific effects remain mysterious [80] . However , it should be noted that sex–specific effects of mutations and QTLs are a common feature of the genetic architecture of complex traits in Drosophila and other organisms [83] , although such effects on life span are particularly extreme . It remains to be seen whether a common mechanism underlies sex–specificity for all traits . The concept of trade–offs ( antagonistic pleiotropy ) is central to many evolutionary hypotheses for limited life span and senescence . Such trade–offs were historically envisioned to be governed by alleles with beneficial fitness effects early in life , when the force of natural selection is strong , but detrimental effects later in life , when natural selection is weak [2] , [28] . Kirkwood [84] phrased this concept in terms of a physiological trade–off caused by the need to optimally allocate resources to reproduction and somatic maintenance . Support for antagonistic pleiotropy comes from quantitative genetic studies documenting negative genetic correlations between early and late fitness components [28]–[31] , [85] , [86]; but these negative genetic correlations are not always found [87]–[90] . Drosophila mutations affecting increased life span often exhibit antagonistic pleiotropy: mutations in chico and InR show a dwarf phenotype and have reduced fecundity [15] , [16] , and mutations of Indy have decreased fecundity under adult caloric restriction [91] . We have shown here that antagonistic pleiotropy is pervasive , in that all P–element insert lines associated with increased longevity were also associated with at least one deleterious pleiotropic effect on resistance to starvation stress , recovery after chill coma , and/or a general measure of health ( climbing activity ) at one week and/or six weeks of age ( Figure 4 ) . On average , the lines with increased longevity have overall decreased resistance to starvation stress and increased resistance to chill coma stress and increased general activity relative to the controls at six weeks of age . Mutations in genes in the insulin signaling pathway tend to have increased resistance to starvation and oxidative stress , accompanied by a trade–off in growth and fecundity [23] , [25] , [26] , [32] , [33] , [92]–[94] . Thus , our observation that resistance to starvation stress actually decreases in older flies from the long-lived strains runs counter to this theme . It will be interesting in the future to assess early and late age fecundity on these mutations . However , it should be noted that the negative genetic correlation between the sexes for longevity is itself a trade–off , and that patterns of pleiotropy are different for males and females . Mutations affecting female life span have antagonistic pleiotropic effects on resistance to chill coma stress . Mutations affecting male life span have positive pleiotropic effects on resistance to starvation and chill coma stress , but there is antagonistic pleiotropy between male starvation stress resistance and climbing activity . Whole genome expression profiling of mutations that have been derived in the same co–isogenic background is a powerful tool for identifying networks of co-regulated genes that potentially affect the trait ( s ) affected by the mutations . Taken at face value , our analysis of gene expression of six week old adults in seven mutant lines associated with increased life span and the control strain indicate that many genes affect life span . We identified 4 , 488 transcripts that were differentially expressed among all eight genotypes using a false discovery rate criterion of q≤0 . 001 [66] . Transcripts from many of the candidate genes identified in the P–element screen and from genes that have been previously shown to affect life span were also differentially expressed in the background of the seven focal lines . This suggests that the co-regulated genes are indeed excellent candidate genes affecting life span . The fact that transcripts of three of the focal mutations were differentially expressed in the appropriate mutant background provides independent evidence that the P–element does affect the gene in which it has inserted . Further , mutations in co-regulated genes may interact epistatically with mutations in the focal genes [59] , defining genetic networks affecting longevity . The large number of co-regulated genes in each mutant background is consistent with the large number of epistatic interactions we observed among just 10 mutations associated with increased life span . The mutations in pyd , mub , crol , CG10990 and esg affected a diverse array of biological processes that were somewhat unexpected , given their functional annotations . For example , these genes were not expected a priori to affect metabolism and reproduction; yet these categories were over-represented overall . These observations suggest that these loci may interact with insulin signaling and other well-described pathways affecting life span . Several other studies have reported whole genome changes in gene expression in aging Drosophila and C . elegans . Pletcher et al . [40] examined both aging and caloric restriction , and found considerable over–representation for biological processes involving the cell cycle , metabolism , DNA repair and replication , transcription , RNA processing , gametogenesis and perception of light . Similarly , we observed over–representation of gene ontologies for metabolism , cell cycle , mating behavior and response to light . Unfortunately , the expression data of Pletcher et al . [40] are not publicly available , precluding a direct comparison of the lists of genes that were co-regulated by mutation associated with increased life span and those implicated in the analysis of normal aging and prolonged life span through caloric restriction . However , we were able to compare the genes that were co-regulated in the seven P–element lines associated with increased life span with the analysis of normal aging in two Drosophila strains [44] . We observe extensive overlap with the 48 candidate genes postulated by Lai et al . [44] , on the basis that they exhibited significant changes in transcript abundance with age and between the two strains , and that were located in known life span QTL [74] , [75] , [80] , [87] . Almost 23% ( 11 ) of these genes were significantly different between our genotypes at q<0 . 0001 , 50% ( 24 ) were significant between our genotypes at q<0 . 001 . There was also significant overlap of the genes that were co-expressed in Drosophila mutations associated with increased life span with many of the C . elegans orthologs that were co-regulated in the long-lived daf–2 mutant background [59] . 30 of the 39 up-regulated genes and 11 of the 20 down-regulated genes identified by Murphy et al . [60] had Drosophila homologs . 30% ( 9 ) of the up-regulated genes were significant in our study at q<0 . 0001 , and 63% ( 19 ) were significant at q<0 . 001 . For the down-regulated genes , only 27% ( 3 ) were significant at q<0 . 001 ( none were significant at q<0 . 0001 ) . These numbers are slightly inflated as several heat shock genes in C . elegans are homologous to a single Drosophila gene , lethal ( 2 ) essential for life . Many genes that have been previously shown to affect life span showed differential expression in the mutant lines associated with increased life span . For example , InR was down-regulated in both the CG9238 and CG10990 backgrounds , consistent with the previously observed decrease of InR expression associated with increased life span [16] . Alcohol dehydrogenase ( Adh ) was up-regulated in the mutant pyd , mub and esg backgrounds . Adh expression has been shown to decrease with age [37] so an increase in expression could conceivably be associated with an increase in life span . The expression of Sirt2 , a member of the Drosophila Sirtuin family [95] , was strongly decreased in the mub , BG00817 and esg mutant backgrounds . The mub mutant displayed an increase in Sod expression and a decrease in chico expression which mirrors previous reports of changes of the expression of these genes in association with increased life span [7] , [15] . We performed an unbiased , forward genetic screen for mutations affecting Drosophila longevity , and identified 58 mutations associated with increased life span . These mutations represent pathways known to affect life span ( e . g . , the insulin and metabolic pathways , gene silencing and immune response ) , as well as novel pathways involving taste and nervous system and embryonic development . Mutations in the same gene can be associated with both increased and decreased life span , which could be caused by different insertion sites or epistatic interactions with different genetic backgrounds . Pervasive epistasis for mutations affecting life span is indicated by a diallel cross analysis of ten of the mutations associated with increased life span . A striking feature of our screen is that the main and epistatic effects of mutations increasing life span are highly sex–specific . Further , antagonistic pleiotropy of mutational effects is pervasive , in that all P–element insert lines associated with increased longevity were also associated with at least one deleterious pleiotropic effect on a component of fitness . However , the patterns of pleiotropy are also sex–specific and different for males and females . The 4 , 488 transcripts that are differentially expressed among all eight genotypes provide a glimpse into complex genetic networks affecting longevity , which include many genes previously shown to affect life span . Further studies are required to establish that P–element disruptions of all candidate genes cause the changes in longevity and to determine interactions of these novel mutations with mutations in genes of the insulin signaling pathway and other pathways known to affect life span . The causes of the sex–specific and background–dependent epistatic effects remain to be elucidated , as do any effects on early and late reproduction , and the contribution of the novel loci to naturally occurring variation in life span – in Drosophila , and other organisms .
The P{GT1} insertion lines [55] used in this study were constructed in six co–isogenic w1118 Canton–S backgrounds ( A , B , C , D , E and F ) as part of the Berkeley Drosophila Gene Disruption Project [55] , and were obtained from Hugo Bellen ( Baylor College of Medicine , Houston , TX ) . All lines were maintained at 60–80% humidity and 25°C under a 12∶12 hour light∶dark cycle . We screened 1 , 332 homozygous viable P{GT1} insertion lines for changes in life span relative to their control line . The initial screen was conducted in blocks of 50–100 insert lines and the appropriate Canton S control line . Each block was initiated with virgin males and females that had eclosed within 48 hours of each other , with two replicate vials per sex per insert line , and ten replicate vials per sex of the control line . Each replicate vial contained five flies of the same sex and 5 ml cornmeal/molasses medium . We recorded deaths every two days until all flies were dead , and transferred the flies to fresh culture medium every 1–2 days . We evaluated mutational variation for life span using analyses of variance ( ANOVAs ) of the mean life span of replicate vials , expressed as deviations from the appropriate contemporaneous control means for each sex . The full two–factor mixed effects model for pooled sexes was Y = μ+L+S+L×S+ε , and the reduced model for the analysis of sexes separately was Y = μ+L+ε , where μ is the overall mean , L is the line effect ( random ) , S is the sex effect ( fixed ) and ε is the environmental variance between replicate vials . We computed the mutational broad sense heritability ( HM2 ) from the full model as HM2 = ( σL2+σSL2 ) / ( σL2+σSL2+σε2 ) , where σL2 , σSL2 and σε2 are , respectively , line , sex by line , and environmental variance components; and the cross–sex genetic correlation ( rMF ) as rMF = covFM/σLFσLM , where covFM is the covariance between the mean life span of males and females , and σLF and σLM are the square roots of the variance components from the separate sex analyses of females and males , respectively . We used two methods to identify insert lines with mean life spans that were significantly different from the control . We computed the 95% , 99% and 99 . 9% confidence intervals of the deviations from the control mean , assuming normality , as ±zα σ/ ( n ) ½ . zα is the critical value for the normal distribution ( 1 . 96 , 2 . 575 and 3 . 3 respectively for the 95% , 99% and 99 . 9% confidence intervals ) . σ is the phenotypic standard deviation , estimated as ( σL2+σSL2+σε2 ) ½ for the full model and ( σL2+σε2 ) ½ for the reduced models . n is the number of replicate vials for each insert line ( n = 4 in the full model and n = 2 in the reduced models ) . We also used the Dunnett's t–test , which corrects for multiple tests of different insert lines relative to a common control , to identify insert lines that were significantly different from the control within each block . We re-assessed the life span of 83 lines with increased life span under the same conditions as the previous assay , but with larger sample sizes of at least 12 replicate vials per sex per line . We evaluated the significance of the difference in life span between each insert line and the control by ANOVAs pooled across sexes and for each sex separately , using the models Y = μ+L+S+L×S+R ( L×S ) +ε ( full model ) and Y = μ+L+R ( L ) +ε ( reduced model ) ; where μ and S are defined above; L , the fixed effect of line , is the difference between the P–element insertion line and the co–isogenic control; R is the random effect of replicate vial; and ε is the environmental variance between individuals within each replicate vial . We computed the standardized effect of each mutation as a/σP , where a is one–half the difference in mean life span between the homozygous mutant and the corresponding control line , and σP is the phenotypic variation of the control line [64] . Bellen et al . [55] identified flanking sequences for the majority of lines using inverse PCR . We used the same technique to identify several more insertion sites . We isolated DNA from ∼25 individuals using the Puregene protocol , digested the DNA with Hinp1I and ligated it to obtain circular fragments containing both genomic and P–element DNA from both ends of the insert . We used PCR to amplify the 5′ end with oligonucleotide primers pGT1 . 5 ( CCGCACGTAAGGGTTAATG ) and pGT1 . d ( GAAGTTAAGCGTCTCCAGG ) and the 3′ end with primers Pry1 ( CCTTAGCATGTCCGTG–GGGTTTGAAT ) and Pry4 ( CAATCATATCGCTGTCTCACTCA ) , at annealing temperatures of 55°C . We sequenced the resulting products using ( 5′ ) Sp1 ( ACACAACCTTTCCTCTCAA–CAA ) and ( 3′ ) Spep1 ( GACACTCAGAATACTATTC ) . We aligned the flanking sequences to the D . melanogaster genome using BLAST [68] . The inverse PCR protocol failed for lines BG00121 , BG01700 and BG00817 . For these lines , we determined the cytological location of the inserts by in situ hybridization to polytene chromosomes . We generated biotin-labeled probes using the BioNick Labeling System ( Invitrogen ) protocol , and used the Vectastain ABC kit ( Vector Laboratories ) for signal detection . We generated revertant lines for two chromosome 2 insert lines ( BG00346 , BG01042 ) and one chromosome 3 insert line ( BG00043 ) , using crossing schemes that preserved the co–isogenic background of the revertant lines . To construct the chromosome 2 revertant lines , we crossed w1118; P; iso3 females to w1118; CyO/Sp; SbΔ2–3/TM6 , Tb males . We mated male offspring of genotype w1118; CyO/P; SbΔ2–3/iso3 to w1118; CyO/Sp; iso3 females , and single male offspring of genotype w1118; CyO/P−; iso3 in which the P–element had excised were crossed to w1118; CyO/Sp; iso3 females . In the following generation , males and females of genotype w1118; CyO/P−; iso3 were mated inter se to make a homozygous revertant stock of genotype w1118; P−; iso3 . To construct the chromosome 3 revertant lines , we crossed w1118; iso2; P females to w1118; CyO/Sp; SbΔ2–3/TM6 , Tb males . We mated male offspring of genotype w1118; CyO/iso2; SbΔ2–3/P to w1118; iso2; TM3 , Sb/H females , and single male offspring of genotype w1118; iso2; H/P− were crossed to w1118; iso2; TM3 , Sb/H females . In the following generation , males and females of genotype w1118; iso2; TM3 , Sb/P− were mated inter se to make a homozygous revertant stock of genotype w1118; iso2; P− . Here w1118 , iso2 and iso3 are the three isogenic chromosomes of the Canton S F strain; P refers to the chromosome from the Canton S F strain with the P–element insertion associated with increased life span; and P− indicates a P–element excision allele . We assessed the life span of the revertant lines simultaneously with the corresponding insert and control lines , with 12 replicates for each line and sex . The analysis of the BG00043 revertants was done in Raleigh under the same conditions described for the previous tests . The analysis of the BG00346 and BG01042 revertants was done in Moscow , Russia . We used the same ANOVA models described above for the second analysis of life span to assess differences in life span among the lines , and Tukey tests to identify significant differences between mutant , revertant and control lines . We evaluated epistatic interactions among 10 mutations , generated in different genes in the F background , that had increased life span relative to the Canton S F strain ( BG00004 , BG00010 , BG00028 , BG00043 , BG00297 , BG00346 , BG00495 , BG00761 , BG00817 , BG01042 ) . We crossed these lines in a half–diallel crossing scheme ( excluding homozygous insert lines and reciprocal crosses ) to create all 45 possible double heterozygote F1 genotypes following Griffing's [96] Method 4 and Model 1 . We measured the life span of each F1 genotype as described above , with eight replicate vials per genotype per sex . The general combining ability ( GCA ) for each mutation is the difference between the mean life span of all genotypes containing that mutation and the overall mean [97] . We estimated GCA values as GCAi = [Ti/ ( n−2 ) ]−ΣT/n ( n−2 ) , where Ti is the sum of mean life spans for all genotypes with the ith mutation , ΣT is twice the sum of mean life spans of all double–heterozygote genotypes , and n is the total number of mutant lines [64] . The specific combining ability ( SCA ) for any particular genotype is the difference between the mean life span of the genotype and the life span expected from the sum of the GCAs of the mutants involved in the cross [97] . We estimated SCA values as SCAij = Xij− ( Ti+Tj ) / ( n−2 ) +ΣT/ ( n−1 ) ( n−2 ) , where Xij is the mean life span of the offspring resulting from the cross of the ith and jth mutant lines . We also estimated GCAs and SCAs separately for each sex . We used Diallel–SAS05 [98] to estimate individual GCA and SCA effects and their standard errors; to perform ANOVAs to assess the significance of variation among the double heterozygous genotypes ( G ) for the full model pooled across sexes ( Y = μ+G+S+G×S+R ( G×S ) +ε ) and for the analyses of each sex separately ( Y = μ+G+R ( G ) +ε ) ; and to partition the G term into its GCA and SCA components , pooled across sexes ( Y = GCA+SCA+S+GCA×S+SCA×S+R ( S ) +ε ) and separately by sex ( Y = GCA+SCA+R ( S ) +ε ) . We assessed pleiotropic effects of mutations with significantly increased life span on stress resistance ( chill coma recovery and starvation resistance ) as well as a general measure of health ( climbing ability ) for virgin flies at one week and six weeks of age . We tested the F lines in three blocks , the A lines in two blocks , and all B lines simultaneously . Each block included the appropriate control line . We measured chill coma recovery time and climbing ability for individuals within each block within 48 hours , and scored all individuals within a block for starvation resistance at the same time . We chose seven P–element mutations in the Canton S F genetic background that were associated with increased life span in at least one sex , and for which we knew the exact P–element insertion sites ( with the exception of BG00817 ) for whole genome expression analysis . These seven lines were a subset of the lines used for the epistasis analysis: BG00028 , BG00043 , BG00346 , BG00495 , BG00761 , BG00817 and BG01042 . We collected over 100 virgin flies of each sex over a 4–day interval from each of the P–element insert lines and the co–isogenic Canton S F control line , and maintained them as described for the previous life span assays . We froze 42 day–old flies , and created two replicate pools of 25 flies per sex per line for RNA extraction . We used a TRIZOL ( Gibco BRL ) /chloroform protocol to extract RNA from whole flies , and prepared cRNA from 5 µg RNA following the recommended protocol for eukaryotic one–cycle target labeling . We hybridized fragmented cRNA to Affymetrix Drosophila Genome 2 . 0 GeneChip arrays . We performed all statistical tests using SAS V8 . 2 , V9 . 1 and Microsoft Office Excel . We used QVALUE software [66] to compute q–values . | Recent advances in medical science as well as vastly improved living conditions have resulted in a steady increase in human life span , with a concomitant increase in health issues associated with aging . In addition , understanding life history evolution requires that we know why organisms age and why there is variation in aging and senescence . To identify genes involved in aging , we assessed longevity in a collection of over 1 , 300 Drosophila lines homozygous for a single P transposable element mutation . We found 58 mutations in novel loci that increase life span by up to 33% . Most mutations had different effects on male and female life span , and for some the effects were opposite between the sexes . Effects of these mutations on starvation resistance , chill coma recovery , and climbing ability varied , but all had a deleterious effect on at least one other trait . A sample of ten mutations with increased life span formed genetic interaction networks , but the genetic interactions were different , and sometimes in opposite directions , in males and females . Transcript profiles of seven long-lived mutations and the control line reveal a core transcriptional signature of increased life span involving novel candidate genes for future analysis . | [
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] | 2010 | Quantitative and Molecular Genetic Analyses of Mutations Increasing Drosophila Life Span |
Behavioral evidence suggests that instrumental conditioning is governed by two forms of action control: a goal-directed and a habit learning process . Model-based reinforcement learning ( RL ) has been argued to underlie the goal-directed process; however , the way in which it interacts with habits and the structure of the habitual process has remained unclear . According to a flat architecture , the habitual process corresponds to model-free RL , and its interaction with the goal-directed process is coordinated by an external arbitration mechanism . Alternatively , the interaction between these systems has recently been argued to be hierarchical , such that the formation of action sequences underlies habit learning and a goal-directed process selects between goal-directed actions and habitual sequences of actions to reach the goal . Here we used a two-stage decision-making task to test predictions from these accounts . The hierarchical account predicts that , because they are tied to each other as an action sequence , selecting a habitual action in the first stage will be followed by a habitual action in the second stage , whereas the flat account predicts that the statuses of the first and second stage actions are independent of each other . We found , based on subjects' choices and reaction times , that human subjects combined single actions to build action sequences and that the formation of such action sequences was sufficient to explain habitual actions . Furthermore , based on Bayesian model comparison , a family of hierarchical RL models , assuming a hierarchical interaction between habit and goal-directed processes , provided a better fit of the subjects' behavior than a family of flat models . Although these findings do not rule out all possible model-free accounts of instrumental conditioning , they do show such accounts are not necessary to explain habitual actions and provide a new basis for understanding how goal-directed and habitual action control interact .
There is now considerable evidence from studies of instrumental conditioning in rats and humans that the performance of reward-related actions reflects the involvement of two learning processes , one controlling the acquisition of goal-directed actions and the other of habits [1]–[4] . This evidence suggests that goal-directed decision-making involves deliberating over the consequences of alternative actions in order to predict their outcomes after which action selection is guided by the value of the predicted outcome of each action . In this respect , action evaluation relies on the representation of contingencies between actions and outcomes as well as the value of the outcomes , which in sum constitute a model of the environment . In contrast , habitual actions reflect the tendency of individuals to repeat behaviors that have led to desirable outcomes in the past and respect neither their causal relationship to , nor the value of their consequences . As such , they are not guided by a model of the environment , and are relatively inflexible in the face of environmental changes [5]–[7] . Although these features of goal-directed and habitual action are reasonably well accepted , the structure of habitual control , and the way in which it interacts with the goal-directed process in exerting that control , is not well understood . Two types of architecture have been proposed: a hierarchical architecture and a flat architecture . We have recently described a version of the hierarchical structure in the context of advancing a new theory of habits [8] . Although habits are usually described as single step actions , their tendency to combine or chunk with other actions [9]–[15] and their insensitivity to changes in the value of , and the causal relationship to , their consequences [2] , [16] suggests that they may best be viewed as action sequences [8] . On this view habit sequences are represented independently of the individual actions and outcomes embedded in them such that the decision-maker treats the whole sequence of actions as a single response unit . As a consequence , the evaluation of action sequences is divorced from offline environmental changes in individual action-outcome contingencies or the value of outcomes inside the sequence boundaries and , as they are no longer guided by the model of the environment [8] , are executed irrespective of the outcome of each individual action [12] , [17]; i . e . , the actions run off in an order predetermined by the sequence , without requiring immediate feedback . On this hierarchical view , such action sequences are utilized by a global goal-directed system in order to efficiently reach its goals . This is achieved by learning the contingencies between action sequences and goals and assessing at each decision point whether there is a habit that can achieve that goal . If there is , it executes that habit after which control returns to the goal-directed system . In essence , the goal-directed system functions at a higher level and selects which habit should be executed whereas the role of habits is limited to the efficient implementation of the decisions made by the goal-directed process [8] , [18] ( see [19] for a review of other schemas ) . Assume , for example , you are deciding whether to go to a restaurant on this side of the road or on the other side of the road ( Figure 1A ) . The goal-directed system evaluates both options , and decides to go to the restaurant across the road . It thus triggers a ‘crossing the road’ habit , and transfers the control to the habitual system . The habit is an action sequence composed of several individual actions: ( 1 ) head to the crossing point , ( 2 ) look left , and ( 3 ) cross the road . Individual actions are executed one after another , and after they finish , the control transfers back to the goal-directed system to make the next decision such as , for example , choosing from the menu in the restaurant . In contrast to the hierarchical architecture , the flat architecture treats habits as single step actions rather than action sequences ( e . g . [5] ) . At each step , an arbitration mechanism decides whether the next action should be controlled by the goal-directed system or the habitual system . In the context of the above example , at the beginning the arbitration mechanism selects one of the systems to decide whether to go to the restaurant on this side of the road or to the crossing point . Again , at the crossing point , the arbitration mechanism selects one of the systems to decide whether to look left , or right , and similarly at each future step the arbitration mechanism selects one of the systems to control behavior ( Figure 1B ) . It should be clear , therefore , that , in the flat approach , both systems are at the same level and action evaluation happens in both processes; both systems evaluate available alternatives , and the arbitration mechanism determines how these two evaluations combine to make the final decision . From the flat perspective , another difference between goal-directed and habitual processes lies in how they evaluate actions . The goal-directed process obeys the same principles sketched earlier: learning the model of the environment , and making predictions based on that model ( model-based evaluation ) . In contrast , the habitual system is model-free and evaluates actions based on their ‘cached’ reward history without searching through the action-outcome contingencies [5] , [7] , [20]–[22] . More recently , Daw et al [23] have exploited the difference between model-free and model-based evaluation to investigate the interaction of goal-directed and habit processes in a flat structure reasoning that , because model-free evaluation is retrospective , chaining predictions backward across previous trials , and model-based evaluation is prospective , directly assessing available future possibilities , it is possible to distinguish the two using a sequential , multistage choice task . In this task subjects first make a binary choice ( the first stage ) then transition to the second stage in which they make a second choice to earn a reward . The best choice at the second stage varies depending on the first choice and , to maintain a constant trade-off between habitual and goal-directed systems , the reward probabilities in the second stage are continually varied . By examining first stage choices , Daw et al [23] were able to find evidence of mixed goal-directed and habitual predictions . Here we show that first stage habitual actions , explained by the model-free evaluation in previous work , can also be explained by assuming that first stage actions chunk with second stage actions , reducing the source of habitual actions to the formation of action sequences . Based on this finding we next examined specific predictions of each account . With regard to the two-stage task , the flat account predicts that feedback received after the execution of an action will affect subsequent decisions and , therefore , that arbitration between goal-directed and habit controllers will recur anew at each stage . As a consequence , action-control at each stage of the task should be independently established; in particular it should be noted that action control in stage two should not depend on stage one . In contrast , because our hierarchical account treats habits as action sequences , and because the execution of habits is open-loop , it predicts that , during the execution of a habit , actions will be executed one after another without considering feedback from the environment during the sequence and , therefore , that , when habitual , the action taken at stage 2 is already determined when starting the habit sequence at stage 1 . We made two further predictions from the hierarchical account: first , because of their relative freedom from feedback , action sequences should be elicited more quickly than single actions [24] predicting that , when habitual , reaction times between stage 1 and stage 2 actions will be faster than when non-habitual . Second , and based on these predictions , we anticipated that the hierarchical model would better fit the performance of subjects working on this two-stage task than the flat model .
In the analysis , we first sought to establish whether decision-making in this task is goal-directed , habitual or a mixture of both and , if both , to assess whether goal-directed and habitual control interact according to a flat structure or a hierarchical structure . The first question can be answered by looking at the likelihood of the subjects repeating the same first stage action on each trial based on feedback received on the previous trial [23] . Take for example a trial in which a subject presses A1 and transfers to the S2 slot machine ( which is rare result of choosing A1 ) . If the participant presses a button of that slot machine and receives a reward , this implies S2 is probably a good slot machine and , if the decision-making is goal-directed , in the first stage of the next trial the subject should try to reach this S2 slot machine again . It is expected therefore , that the probability that the subject will press A2 will increase because it is this key that ( in this example ) commonly leads to S2 ( cf . Figure 3B ) . In contrast , if decisions are habitual , subjects should not be guided by contingencies between the responses and slot machines , and should tend to stay on the previously rewarded action , A1 ( Figure 3A ) . The results are presented in Figure 3C , which shows the probability of repeating the same action computed across all subjects and trials . We analyzed the data using mixed-effects logistic regression analyses by taking all coefficients as random effects across subjects ( see Materials & Methods: Behavioral Analysis ) . Results show that being rewarded in the previous trial increased the chance of staying on the same action , irrespective of whether it was a rare or a common transition ( main effect of reward; coefficient estimate = 0 . 61; SE = 0 . 09; p<3e-11 ) , which suggests that habits constitute a component of the behavior . On the other hand , this increase was higher if the previous trial was a common transition ( and lower after an unrewarded trial ) , suggesting that subjects also utilized their knowledge about the task structure ( reward-transition interaction; coefficient estimate = 0 . 41; SE = 0 . 11; p<5e-4 ) . Therefore , the subjects' behavior was a mixture of both goal-directed and habitual actions . Also , as the figure shows , the probability of staying on the same action is generally higher than not staying on it , irrespective of reward and transition type in the previous trial ( the intercept term is significantly positive; estimate = 1 . 52; SE = 0 . 20; p<10e-14 ) , which reflects a general tendency of animals and humans to repeat previous actions [25]–[27] . In previous studies , a hybrid model of model-free and model-based reinforcement learning ( RL ) was advanced to explain the behavior of subjects on this task based on the flat structure [23] , [28]–[30] . According to this model , action values learned in model-free RL , roughly , reflect the frequency of the action rewarded on previous trials irrespective of the action-outcome contingency ( i . e . , in the current task , which key generates which slot machine ) and , as such , these values underlie the habitual component of the model . These model-free values are then mixed with the values provided by the goal-directed system ( modeled by a model-based RL ) to produce the final values which guide action selection . As a consequence , and consistent with the above results , we should expect to see a combination of both habitual and goal-directed actions . The prediction from this hybrid model is illustrated in Figure 4A . A hierarchical structure can , however , also be used to explain these results . For example , assume that a subject presses A1 in the first stage , and A2 in the second stage and receives a reward . As a result , the goal-directed system learns that contingency between the A1A2 action sequence and the reward is increased and so it should be more likely to repeat the action sequence in the next trial , whether or not the reward was received from the S1 or S2 slot machine ( i . e . , the common or rare transition ) . As the evaluation and performance of an action sequence is not guided by the task structure ( i . e . the key-slot machine association ) , from this perspective it constitutes the habitual component of the behavior . All actions - either single action ( e . g . , A1 ) or action sequences ( e . g . , A1A2 ) - , will be subject to the goal-directed action selection process , such that actions with higher values will be selected with a higher probability . As a consequence , this implies that the behavior will be a mixture of habitual ( when action sequences are selected ) and goal-directed ( when single actions are selected ) actions and that this mix of actions can be generated without the need for the model-free component or an explicit arbitration mechanism used in the flat structure . This prediction is illustrated in Figure 4B . Although both approaches are able to explain the mixture of behavioral control in the first stage , they make different predictions about second stage choices . This is because , if the observed habitual behavior is due to the execution of an action sequence , rather than cached values as the model-free account supposes , then we expect the subject to repeat the whole action sequence in the next trial , not just the first stage action . Staying on the same first stage action in the next trial after being rewarded implies that this is probably a habitual response and so we expect the subject to repeat the second stage action as well , even if the slot machine is different from the one in the previous trial . In contrast , if the subject switches to the other first stage action , the previous action sequence is not repeated , and thus the second stage action is not expected to be repeated if the subject ends with a different slot machine in the next trial . In order to test this prediction , we looked at the trials that had a different slot machine to the one in their previous trial . Figure 5A shows the probability of repeating the same second stage action as a function of whether this action was rewarded on the previous trial and the subject had subsequently taken the same first stage action . Logistic regression conducted on second stage choices using factors of reward , separating rewarded and non-rewarded trials , and action , separating trials on which the first stage action was the same from those on which it differed , found neither an effect of reward ( p>0 . 05 ) , nor of action ( p>0 . 05 ) but found a significant interaction between these factors ( coefficient estimate = 1 . 02; SE = 0 . 38; p<0 . 008 ) , indicating that , during the execution of habitual responses , subjects tended to repeat the second stage action . This interaction remained significant even when we restricted the analysis either to trials after rare transitions ( coefficient estimate = 1 . 33; SE = 0 . 60; p<0 . 05 ) or after common transitions ( coefficient estimate = 0 . 93; SE = 0 . 38; p<0 . 05 ) . Importantly , the fact that the effect of the reward was not significant rules out the possibility that the effect was due to the generalization of the values across slot machines . Simulations of the flat and hierarchical models are presented in Figure 5B and C , respectively . As predicted , the hierarchical structure captures the pattern of the subjects' second stage actions ( the interaction between the reward and the same first stage action; p<0 . 001 ) , whereas the flat structure is not consistent with repeating the same action in the second stage ( p>0 . 05 ) . Previously , we focused on trials with a different slot machine to the one in the previous trial . This was because , in this condition , flat and hierarchical accounts provide different predictions . When the slot machine is the same , both accounts ( flat and hierarchical ) predict that being rewarded in the previous trial increases the probability of staying on the same second stage action . In addition to this prediction , the hierarchical account predicts that when the slot machine is the same as the one on the previous trial , this increase should be higher than the increase when the slot machine is different . This is because , when the slot machine is different , staying on the same second stage action is drive by execution of the previous action sequence whereas , when the slot machine is the same , executing either the previous action sequence or a goal-directed decision at the second stage can result in staying on the same second stage action . As a consequence we looked at the effect of being rewarded in the previous trial , and whether the slot machine was the same as the one in the previous trial , on the probability of staying of the same second stage action ( in the trials in which the first stage action was the same as the previous trial ) . Figure 6A shows the results . A significant main effect of reward was found ( coefficient estimate = 0 . 69; SE = 0 . 21; p<0 . 002 ) indicating that being rewarded in the previous trial increases the probability of taking the same second stage action , irrespective of whether the slot machine was the same as the previous trial or not , which is consistent with the hierarchical account . In addition , we found a significant interaction between the effect of reward and whether the slot machine being the same ( coefficient estimate = 3 . 46; SE = 0 . 51; p<3e-11 ) , consistent with the finding that the probability of staying on the second stage action was higher when the second stage action was the same . Figure 6B shows the probability of staying on the same second stage action when the subject takes a different first stage action . As predicted , because the subject did not execute the previous action sequence , the main effect of reward was not significant ( p>0 . 05 ) but the interaction between reward and the second stage being the same was significant ( coefficient estimate = 1 . 72; SE = 0 . 40; p<3e-5 ) which means that subjects tend to take the same action on the same slot machine after being rewarded , as predicted by both accounts . In the previous section we showed that if , after being rewarded , the subject repeats the same first stage action , they are probably repeating the previous action sequence and , as such , they tend to repeat the second stage action as well . However , even in the situation in which the subject is executing an action sequence there will be trials on which they might not repeat the same second stage action . In such conditions , we should suppose that either ( i ) the subject took an exploratory goal-directed action in the first stage , or ( ii ) the subject started an action sequence but its performance was inhibited and control returned to the evaluation system in the second stage . In both cases , the hierarchical account predicts that reaction times on trials in which the second stage action is not taken should be higher . Figure 7A illustrates these reaction times as a function of whether the previous trial was rewarded and the subject takes the same second stage action ( only in trials on which the slot machine is different from that on the previous trial and the subject subsequently takes the same first stage action ) . If the previous trial is rewarded , reaction times were lower when a subject completes an action sequence than when the second stage action was not executed as a part of a sequence ( coefficient estimate = −1 . 66; SE = 0 . 45; p<3e-4 ) . Importantly , the effect was not significant when the previous trial was not rewarded ( p>0 . 05 ) , which rules out the possibility that the observed increase in the reaction times was because of the cost of switching to the other second stage action . We further asked whether the model can predict the reaction times in the second stage . As mentioned above , at the first stage , the goal-directed process more frequently selects actions that have a higher contingency to reward ( either single actions , or action sequences ) . As such , if an action sequence has a high value , it is likely to be selected for execution , and so we expect a low reaction time in the second stage . For example , assume the subject has executed action A1 in the first stage , and A2 in the second stage and the aim is to predict whether A2 has a high or low reaction time . It can be argued , if the value of the A1A2 action sequence is high , that it was probably executed in the first stage , and thus the execution of A2 is part of an action sequence ( A1A2 ) started in the first stage , implying the subject should show a low reaction time . In general we assume that the reaction time in the second stage is inversely related to the value of the action sequence that contains that action ( see Material & Methods: Hierarchical sequence based , model based RL ) . In the case of this example we will have:Based on this , we calculated the predicted reaction time of the action taken by the subject in the conditions shown in Figure 7A . The results are shown in Figure 7B . As the figure shows , the predicted reaction times by the model are consistent with the pattern of reaction times observed in the data . In general , the above analysis of stage 2 performance and this analysis of reaction times implies that ( i ) when the previous trial is rewarded , ( ii ) the same first stage action is taken , and ( iii ) the reaction time is low , then the subject is most likely performing an action sequence . As a consequence it is expected to repeat the same second stage action , even on a different slot machine to the one in the previous trial . In order to more closely examine this relationship we used conditional inference trees and partitioned second stage actions into whether they involved staying or switching to the other action based on the above three factors ( see Materials & Methods: Behavioral Analysis for more details ) . The results are shown in Figure 8 . As the figure shows , when the previous trial was not rewarded ( node # 1 ‘no reward’ condition ) , staying on the same second stage action was independent of either whether the first stage action was repeated or the reaction time was low ( p>0 . 05; permutation test ) . If the previous trial was rewarded ( node # 1 ‘reward’ condition ) then , if the reaction time was high ( node #2 RT>0 . 437s ) or the reaction time was low but the subject doesn't repeat the first stage action ( node #3 ‘different’ condition ) , then again the second stage action was not repeated . Only when: ( i ) the previous trial was rewarded , ( ii ) the subject took the same first stage action , and ( iii ) their reaction time was low ( node #3 ‘same’ condition ) , did the subject repeat the second stage action , consistent with the prediction of the hierarchical account . The results described in the previous sections suggest that a hierarchical structure better characterizes the effect of feedback from the previous trial on performance on the subsequent trial . However , choices are generally guided by the feedback from all previous trials , not just the immediately prior trial . As such , it is still to be established which framework better captures behavior in this more general condition . We used a Bayesian model selection method to establish which framework produces choices that are the most similar to the subjects' actions . Both flat and hierarchical architectures have different variants with different degrees of freedom . As such , we compared a family of flat models with a family of hierarchical models [31] , where each family consists of a complex model , and its nested simpler models . The results ( Table 1 ) show that , given the subjects' data , the hierarchical family is more likely than the flat family to produces choices similar to those made by the subjects . We found that the exceedance probability in favor of the hierarchical family was 0 . 99 meaning , roughly , that we can be 99% confident that the hierarchical family generated the observed data . In the hierarchical family , the probabilities of taking actions in the second stage are partially based on the probability of taking an action sequence in the first stage . As these second stage choices are the canonical difference between the two families , we expected that removing the effect of action sequences on the second stage choices would reduce the fit of the hierarchical account to data . Thus we generated a family of hierarchical models similar to Table 1 . but with the effect of action sequences on the second stage actions removed , and compared the generated family with the family of hierarchical models presented in Table 1 ( see Materials & Methods: Hierarchical model-based , sequence-based RL ) . Results indicated that the exceedance probability in favor of the family in which the performance of action sequences was reflected in second stage choices was 0 . 99 , confirming that the selection of an action sequence in the first stage increased the probability of taking the second element of the action sequence in the next stage . Table 1 represents the model comparison results within each family . The parameter estimates for the best fitting model from each family in terms of the exceedance probabilities [32] are presented in Table 2 . The best fitting models from each family were simulated in the task conditions to produce Figure 4 and Figure 5 .
A number of studies have previously investigated the relationship between hierarchical RL and decision-making [7] , [19] , [38]–[43] . We extended these studies by showing how the formation of action sequences can lead to decisions that are insensitive to ( i ) the values of the outcomes [8] and ( ii ) the contingency between specific actions and their outcomes ( i . e . the key press–slot machine associations in this study ) , the two defining characteristics of the habitual behavior . The other difference between the hierarchical RL model that we used here and previous work is that we assumed that performance of action sequences is insensitive to the feedback received during execution [12] , [17] , whereas , in general , previous work based on hierarchical RL theory has assumed that action selection is based on the state of the environment [44]–[46] . Within this latter framework , one can posit that habits are hierarchically organized actions but that their performance is sensitive to the feedback received after execution of each individual action . Although this class of models can explain habitual behavior executed in the first stage of the current task , this approach predicts that second stage actions will , ultimately , be similar to those of the flat architecture discussed earlier , which is not consistent with the data observed in this study . In the hierarchical account advanced here we assumed , based on the previous findings in rodents [18] , [47] , that , similar to single actions , action sequences are also under goal-directed control . Alternatively , it is possible that the value of any action sequence is learned in a model-free manner ( for example using Q-learning ) without learning the identity of the particular outcome that it predicts . Our results are silent with respect to this latter assumption; nevertheless , whatever the case , the conclusion that habitual responses in the first stage were due to the execution of an action sequence still holds . One way to study this issue is to add another choice to the end of the task , making it a three stage task , and then asking whether performance of for example A1A2 action sequence is goal-directed or habitual , which can be answered by devaluation of outcome of A1A2 , or using the same task structure that we used here to distinguish habitual and goal-directed actions . However , again , if it were found that the selection of the A1A2 sequence was not sensitive to environmental contingencies , or outcome values , this could be due either to the formation of A1A2A3 action sequence ( since outcome of A1A2 falls within sequence boundaries [8] ) , or it could be because action sequences are open to model-free evaluation . Similar to the study here , these accounts can be distinguished by examining whether the subject selects A3 during habitual selection of A1A2 irrespective of the outcome of A1A2 performance . If so , it can be concluded that the observed habitual behavior is due to the formation of an action sequence , not model-free RL . Along the same lines , it is possible to assume that , in the current study , first stage habitual responses were guided by a flat model operating in parallel to the hierarchical model we propose here . Again , although the task results are neutral with respect to this assumption , adding a parallel model increases the model's complexity , is not required to account for the current data , and so it necessity should be motivated by additional behavioral data . It might also be argued that , although the current predictions apply to the modified two-stage discrimination task used here , they may not apply to previous versions of the task . In previous versions , subjects at each stage chose between two symbols instead of two fixed actions and the symbols moved from side to side at each trial ensuring there was no consistent mapping between the button presses and the symbols . There are two points to make here: First , the fact that specific ( e . g . left- or right-hand ) actions are degraded in their contingency with the outcome on this version of the task raises the issue of stimulus control; either the stimuli exclusively mediate the predictions of second stage outcomes or the concept of action needs to be made more liberal to the selection of a symbol . The former approach would , of course , render the task Pavlovian , rather than instrumental , and the applicability of model-based control problematic . Second , and relatedly , in order to apply our hierarchical model to the earlier task , we also need to extend the concept of an ‘action’ from pressing a button ( as in our task ) , to selecting a symbol; if this is accepted then , using the logic laid out earlier , the hierarchical goal-directed/habit sequences model can explain the results of the task . In the prior version of the task , symbols in the second stage were different from each other , for example in one of the second stage states subject could choose between symbols ‘C’ and ‘D’ , but in the other second stage state , the choice was between symbols ‘E’ and ‘F’ . As such , we cannot directly assess the probability of staying on the same second stage action if the subjects end up in a different second stage state . Nevertheless , the hierarchical theory predicts that if the subject selects same first stage action , and ends up with the same second stage state and selects the same second stage action , then the reaction time will be faster than when they end up with in a different second stage state . Predictions of both models ( flat and hierarchical ) were found to deviate from the behavior of the subjects in two cases . In the first case , if , after being rewarded , the subject switches to the other action then both accounts predict that the probability of staying on the second stage action should be on average 0 . 5 ( Figure 5B , C ) . However , in the actual data it is below 0 . 5 ( Figure 5A ) . In the second case , both accounts predict that the difference between stay probability in common and rare transitions should be equal in both the reward and no-reward conditions ( Figure 4A , B ) , however , as Figure 3C shows , the difference is larger in the reward condition . It is possible to capture these two deviations by adding more free parameters to the models; however , since the deviations exist for both the flat and hierarchical families and so do not affect the comparison between them , we didn't add further parameters to account for these two deviations . As in previous work , we interpreted the interaction between being rewarded and the type of transition in the previous trial ( rare or common ) as the evidence for goal-directed behavior . It should , however , be noted that , if there is a strong initial bias in total possible reward for one action vs . the other at the first-stage , and reward transitions are slow , then it is possible to observe an interaction between reward and transition type without engaging a goal-directed system . As a consequence of the higher overall probability of reward for taking , say , action ‘A1’ in the first stage , the subject can establish that action has a higher value ( without relying on the task structure ) and so will take that action , i . e . ‘A1’ , more frequently than the other , i . e . action ‘A2’ , which means that the probability of staying on action ‘A1’ will be higher than action ‘A2’ in general . At the same time , because action ‘A1’ is better than the other action , most of the rewarded common transitions and unrewarded rare transitions result from taking action ‘A1’ . Likewise , most of the unrewarded common transitions and rewarded rare transitions will be the result of taking action ‘A2’ . This fact , and the fact that stay probability on action ‘A1’ is generally higher , will produce a reward-transition interaction , without having a goal-directed system , at least in the period that action ‘A1’ is better than the other action . This bias is proportional to how fast the bias in first stage values changes and cannot account for the current data . It should also be noted that , as the comparison between the flat and hierarchical model families was based on model fit , those results don't suffer from this problem . Although , on the hierarchical goal-directed/habit sequence model advanced here , habits are integrated with the goal-directed process to reach the goals selected by this latter system , competition can also occur between these two systems when the further execution of an ongoing habit sequence is found to be inappropriate by the goal-directed system and it attempts to take back control . This type of competition resembles the situation in an inhibitory control task , such as the stop-signal task , in which subjects must respond quickly when a ‘go’ signal appears but must stop the action if a stop-signal appears [48] . In the context of our task , seeing a slot machine in the second-stage is the ‘go’ signal , which causes the execution of the next action in the sequence . The stop signal comes from the goal-directed system when the pending response is identified as inappropriate . Consistent with this conception in conditions in which sequence performance is inhibited , reaction times are slower . In the stop-signal task , subjects are typically able to inhibit their responses when the stop signal is temporally close to the ‘go’ signal . Although the stop signal task is more global in terms of response inhibition , whereas in the current task the inhibition is specific to one as opposed to an alternative action , this implies that the ability of the goal-directed system to override habits depends on how fast it calculates the correct action: the faster it calculates , the higher the chance of taking control back before action execution . It is also interesting to consider the relationship between habit sequences and stimulus-response ( S-R ) theories of habit learning . The S-R theory of habit learning maintains that habits are responses that are elicited by antecedent stimuli rather than their consequences [49] , [50] . Such S-R theories maintain that stimuli trigger their associated behavioral responses due to an association between the stimulus and the response . According to the habit sequence theory , however , the stimulus instead signals that the next action in the sequence should be executed; i . e . , in the context of our task , seeing a slot machine signals that it is time for the next action to be executed . Although the next action to be executed is determined by the sequence , the response is still stimulus-bound to some extent and is elicited only when the next expected stimulus is encountered . Nevertheless , these two theories provide different predictions . For example , S-R theory predicts that , in the presence of the appropriate stimulus the response will be performed , irrespective of whether that stimulus was encountered as part of the habit sequence or not . In contrast , habit sequence theory predicts that the individual will respond to the stimulus only when the appropriate habit sequence has already been launched by the goal-directed system . In the two-stage task that we used in this study , there are few possible action sequences , and so it is easy for the subject to enumerate all of them during decision making . However , in general , the number of action sequences grows exponentially with number of individual actions , and , as such , it will rapidly become impractical to consider all of them at the choice point . As a consequence , the decision-maker needs to discover ‘useful’ action sequences , and to limit consideration to those for action selection rather than all the possible action sequences . In the context of the hierarchical RL literature , this problem is known as ‘option discovery’ and various methods has been proposed to address it ( see [19] for a review ) . In particular , we have previously shown how action sequences can be formed using a reward prediction error signal [8] , which has the benefit of forging a bridge between habit sequence formation , and reward prediction error which has been shown to be coded by the phasic activity of dopamine neurons in midbrain [51] , [52] . The flat architecture also utilizes reward prediction error , but for the learning of S-R associations instead of action sequences [5] . Here one critical difference lies in the fact that the hierarchical architecture maintains that the reward prediction error is not computed at the second stage when actions are executed habitually in contrast to the flat architecture according to which reward prediction errors are computed in all conditions .
Fifteen English speaking subjects ( seven females; eight males; mean age 23 . 8 years [SD 4 . 3] ) completed a two-stage decision-making task . After a description of the study , written consent was obtained . This study was approved by the Sydney University Ethics Committee . Each subject completed 270 trials , with a break after the first 120 trials ( Figure 2 ) . Each trial started with the presentation of a black square and subjects could choose between pressing either ‘Z’ ( using left hand ) or ‘/’ ( using right hand ) . After pressing the key , a slot machine appeared on the screen , and the subject could make the next response , which would result in either a monetary reward or no reward . The outcome was shown for two seconds and after that an inter trial interval started and lasted for one second , after which the next trial began . The probability of earning money at each choice was randomly set to either 0 . 2 or 0 . 7 at the beginning of the session , and in each trial , with the chance of 1/7 , they were again randomly set to 0 . 2 or 0 . 7 . This later step was to encourage searching for the best keys throughout the session . Subjects were instructed that the chance of reaching each slot machine by pressing each key will not change throughout the task , but the goodness of the keys in terms of leading to rewards will change over time . If a first stage action is the best action ( the maximum probability of receiving reward on the keys of the slot machine that it commonly leads to is greater than the other action ) , and slot machines reset in the next trial , the probability that the action remains the best action is 3/16 . Based on this , and given that probability of resetting is 1/7 , the average number of trials for which a first stage action remains the best action is as follows: ( 1 ) The fact that a first stage action remains the best for a few numbers of trials ensures that reward-transition interaction does not emerge as the result of developing bias toward the best action . For all the analyses , we used R [53] , and the R package lme4 [54] . In the analysis presented in the section headed ‘Goal-directed and habitual performance on the two-stage task’ , we used mixed-effects logistic regression in which whether the previous first stage action is repeated was a dependent variable , and the transition type ( rare or common ) , and reward received in the previous trial were explanatory variables . We treated all the explanatory variables as random effects . In the analysis in the section headed ‘The interaction of goal-directed actions and habit sequences in stage 2 performance’ , staying or switching to the other second stage action is the dependent variable , and the reward received in the previous trial and staying on the first stage action were the explanatory variables . Only trials in which the second stage states were different from previous trials were included in this analysis . All the explanatory variables were used as random effects . In the second analysis of this section , staying on the same second stage action is dependent variable , and whether second stage state is the same , and whether previous trial was rewarded , are explanatory variables , and also random effects . Only trials in which first stage action is the same as the previous trail were included in this analysis . The third analysis is similar to the third one , except that trials in which first stage action is not the same as the previous trial are included in the analysis . For analysis of the model behavior in the section headed ‘The interaction of goal-directed actions and habit sequences in stage 2 performance’ , each model was simulated 3000 trials in the task with the best fitting parameters of each individual ( see the section headed ‘Computational Modeling’ below for more information ) . Then we analyzed data using linear mixed-effects regression in which the probability of selecting the same second stage action by the model was taken as the dependent variable , and the reward received in the previous trial and staying on the first stage actions were explanatory variables . The intercept was treated as the random effect , and reported p-values are MCMC-estimated using R package LanguageR [55] . In the analysis in the first part of the section headed ‘Reaction times during habit execution’ , staying on the same second stage action was a dependent variable , and the reaction time was an explanatory and random effect . Only trials in which the previous trial was rewarded ( first analysis ) or not rewarded ( second analysis ) , the first stage action was repeated , and the second stage state was not the same , were included in this analysis . In the second analysis of this section , we applied a recursive partitioning method by taking ( i ) whether the previous trial is rewarded , ( ii ) whether the same first stage action is being taken , and ( iii ) reaction time as covariates , and staying on the same second stage action as response . We used R package ‘party’ [56] for the analysis which employs conditional inference trees for recursive partitioning . In short , the partitioning method works as follows: at each stage of partitioning the algorithm performs a significance test on independence between any of covariates and the response using permutation tests . If the hypothesis is rejected ( in the current analysis p-value less than 0 . 05 ) , it selects the covariate which has strongest association with the response , and performs a split on that covariate . | In order to make choices that lead to desirable outcomes , individuals tend to deliberate over the consequences of various alternatives . This goal-directed deliberation is , however , slow and cognitively demanding . As a consequence , under appropriate conditions decision-making can become habitual and automatic . The nature of these habitual actions , how they are learned , expressed , and interact with the goal-directed process is not clearly understood . Here we report that ( 1 ) habits interact with the goal-directed process in a hierarchical manner ( i . e . , the goal-directed system selects a goal , and then determines which habit should be executed to reach that goal ) , and ( 2 ) habits are learned sequences of actions that , once triggered by the goal-directed process , can be expressed quickly and in an efficient manner . The findings provide critical new experimental and computational information on the nature of habits and how they interact with the goal-directed decision-making . | [
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] | [] | 2013 | Actions, Action Sequences and Habits: Evidence That Goal-Directed and Habitual Action Control Are Hierarchically Organized |
A network of lineage-specific transcription factors and microRNAs tightly regulates differentiation of hematopoietic stem cells along the distinct lineages . Deregulation of this regulatory network contributes to impaired lineage fidelity and leukemogenesis . We found that the hematopoietic master regulator RUNX1 controls the expression of certain microRNAs , of importance during erythroid/megakaryocytic differentiation . In particular , we show that the erythorid miR144/451 cluster is epigenetically repressed by RUNX1 during megakaryopoiesis . Furthermore , the leukemogenic RUNX1/ETO fusion protein transcriptionally represses the miR144/451 pre-microRNA . Thus RUNX1/ETO contributes to increased expression of miR451 target genes and interferes with normal gene expression during differentiation . Furthermore , we observed that inhibition of RUNX1/ETO in Kasumi1 cells and in RUNX1/ETO positive primary acute myeloid leukemia patient samples leads to up-regulation of miR144/451 . RUNX1 thus emerges as a key regulator of a microRNA network , driving differentiation at the megakaryocytic/erythroid branching point . The network is disturbed by the leukemogenic RUNX1/ETO fusion product .
The transcription factor RUNX1 ( or AML1 , acute myeloid leukemia 1 ) is a critical regulator of embryonic and adult hematopoiesis ( reviewed in [1–3] ) . Alteration in RUNX1 due to chromosomal translocations and mutations are causally connected to the onset of acute myeloid leukemia in humans [4] . RUNX1 possesses a pivotal role in myeloid lineage differentiation , is a crucial regulator of gene expression at the megakaryocytic/erythroid branching [5–7] and is down-regulated during erythropoiesis [8 , 9] . We recently reported that RUNX1 inhibits erythroid differentiation by repressing the erythroid gene expression program [5] . During megakaryopoiesis sustained RUNX1 expression represses the erythroid master regulator KLF1 [5] . RUNX1 is involved in the t ( 8;21 ) chromosomal translocation found in approximately 15% of acute myeloid leukemia cases , where the DNA binding runt homology domain ( RHD ) of RUNX1 and almost the entire ETO ( MTG8 ) protein are fused [10–12] . The resulting RUNX1/ETO fusion protein can act as a constitutive transcriptional repressor , which occupies RUNX1 binding sites [13 , 14] . RUNX1/ETO does not induce leukemia on its own [15–17] . However , it may contribute to outgrowth of a pre-leukemic clone , which by gathering additional mutations evolves into leukemia [18] . A shorter variant of RUNX1/ETO , RUNX/ETO9a , lacking the C-terminal domain of ETO induces leukemia in murine bone-marrow transplantation models [19–21] . Similar to RUNX1 , full length RUNX1/ETO has an inhibitory effect on erythropoiesis [5 , 22] . Furthermore , both RUNX1 and its leukemic fusion protein RUNX1/ETO influence expression of a number of microRNAs in normal differentiation and leukemia [23] . Thus , we posit that the disturbance of lineage differentiation such as erythropoiesis by RUNX1/ETO might be mediated through alterations of microRNA expression , in addition to the disturbance of transcriptional networks [24] . Because RUNX1 inhibits erythroid gene expression [5] and RUNX1/ETO interferes with erythroid differentiation [22] , we were interested in downstream microRNAs at the megakaryocytic/erythroid bifurcation . The microRNAs miR144 and miR451 are up regulated during erythroid differentiation [25–31] . MiR144 and miR451 are transcribed as one pri-microRNA ( referred to as miR144/451 ) , which is regulated by the activity of the transcription factor GATA1 [26] . Interestingly , maturation of miR144 and miR451 are distinct , as miR451 is processed dicer independently [32–34] . Knock-down experiments have established a positive effect of mature miR451 on erythropoiesis [26 , 27 , 29 , 35 , 36] but little effect of mature miR144 [26 , 37] . In this study , we identified microRNAs downstream of RUNX1 in human hematopoietic cells . We found that the erythroid specific miR144/451 cluster is transcriptionally regulated by RUNX1 and TAL1 , in addition to GATA1 [26] . We show that RUNX1 binds to the promoter of miR144/451 and is an epigenetic repressor of miR144/451 expression during megakaryocytic differentiation . Thus RUNX1 contributes to the down-regulation of the erythroid gene expression program by repressing miR451 transcription . Furthermore , the leukemogenic RUNX1/ETO fusion protein interferes with miR144/451 expression and disturbs miR451 function . This RUNX1/ETO mediated repression of miR451 activity can be reversed by inhibition of RUNX1/ETO .
We previously showed that RUNX1 represses the erythroid gene expression program during megakaryocytic differentiation [5] . Thus we wanted to examine specifically which microRNAs contribute to the biological function of RUNX1 at this differentiation point . To determine , which microRNAs are regulated by RUNX1 at the megakaryocytic/erythroid branching we used K562 erythroleukemia cells , as they have the potential to differentiate towards the erythroid or megakaryocytic lineage . We performed small-RNA sequencing upon over-expression of RUNX1 in K562 cells ( Fig 1A ) and observed 588 altered small RNAs ( Fig 1B and S1 File ) . Of these altered small RNAs , 31 were snRNAs ( small nuclear RNA ) , 45 rRNAs ( ribosomal RNA ) , 142 snoRNAs ( small nucleolar RNA ) and 370 microRNAs ( Fig 1B ) . A total of 237 microRNAs were up-regulated and 133 microRNAs were down-regulated upon RUNX1 over-expression ( Fig 1C ) . Because RUNX1 is an important transcription factor for megakaryocytic and erythroid differentiation we were especially interested in microRNAs connected to RUNX1 with a known role during erythroid or megakaryocytic differentiation ( Fig 1D ) [38 , 39] . Subsequently , we analysed the relative expression of a subset of these microRNAs by q-RT-PCR detecting the mature microRNAs in parental versus RUNX1 expressing cells ( Fig 1E ) . All microRNAs measured by q-RT-PCR were regulated in the same direction as measured by RNAseq , except for miR144 ( S1 Fig ) . In line with published data miR27a , miR126 , miR222 , miR223 were sensitive to changes in RUNX1 expression [40–44] . The expression of most differentially expressed microRNAs was up-regulated , except miR126 , miR144 and miR451 . MiR144 and miR451 are transcribed as one pri-microRNA , driven by a shared promoter . Because RUNX1 is a transcriptional regulator we analysed expression of the transcriptionally regulated pri-micro RNA and found that expression of the pri-microRNA ( referred to as miR144/451 ) was decreased in RUNX1 over-expressing K562 cells ( Fig 1F and S2 Fig ) . To evaluate if RUNX1 directly influences miR144/451 expression , we analysed the 5’-region of the miR144/451 locus in-silico , using the human genome browser [45] . Two regions in the 5’-area of miR144/451 display a high degree of homology between species ( Fig 2A and S3 Fig ) , these areas are separated by a region of lower sequence homology ( Fig 2A , not conserved or n . c . ) . We identified potential RUNX1 , TAL1 and GATA1 binding sites close to the transcriptional start site and at an upstream ( enhancer ) region using TESS [46] . GATA1 binding at the enhancer region had previously been demonstrated [26] . The enhancer and promoter regions displayed high activity in a reporter gene assay in hematopoietic K562 cells compared to embryonic kidney HEK293 cells ( Fig 2B ) , indicating that the miR144/451 regulatory elements are active in hematopoietic cells . By performing systematic over-expression and knock-down experiments of RUNX1 , TAL1 and GATA1 in K562 cells , we found that RUNX1 repressed miR144/451 expression , while TAL1 and GATA1 activated it ( S2 Fig ) . Chromatin immunoprecipitation ( ChIP ) in primary hCD34+ and K562 cells revealed that TAL1 mainly binds to the enhancer region of miR144/451 and to a less degree at the promoter region ( Fig 2C and S4 Fig ) . In contrast , RUNX1 binding was mainly detected at the promoter region of miR144/451 ( Fig 2D ) . This promoter region harbours a RUNX1 binding site , which is also present in the mouse promoter sequence ( Fig 2E ) . We tested the influence of RUNX1 on the miR144/451 promoter in a luciferase reporter assay and found that RUNX1 repressed miR144/451 promoter activity ( Fig 2F ) . Interestingly , the oncogenic RUNX1 t ( 8;21 ) fusion proteins RUNX1/ETO ( R/E ) and also the truncated RUNX1/ETO ( R/Etr ) repressed miR144/451 promoter activity ( Fig 2F , wt prom ) . In contrast , neither RUNX1 , nor R/E or R/Etr were able to repress the promoter when the RUNX1 binding site was mutated ( Fig 2F , RUNXmut prom , compare Fig 2E ) . This indicates that both RUNX1 and its RUNX1/ETO leukemogenic fusion protein repress the miR144/451 promoter by directly binding to the RUNX1 binding site . MiR144/451 expression is down-regulated during megakaryocytic and up-regulated during erythroid differentiation of hCD34+ ( Fig 3A ) and K562 cells ( S5 Fig ) . We found that RUNX1 binding at the promoter region of miR144/451 was reduced upon erythroid differentiation and strongly up-regulated upon megakaryocytic differentiation of hCD34+ cells ( Fig 3B ) and K562 cells ( S5 Fig ) . Concomitant to the enhanced RUNX1 binding to the miR144/451 promoter , RUNX1 expression increased upon megakaryocytic differentiation at the protein level in CD34 cells ( Fig 3C ) and K562 cells ( S5 Fig ) . Interestingly , upon megakaryocytic differentiation of primary hCD34+ cells an additional RUNX1 band appeared . These RUNX1 isoforms have been described before as the RUNX1b ( upper band ) and RUNX1a ( lower band ) isoforms [47] . Increased RUNX1 binding at the promoter region during megakaryocytic differentiation was accompanied by an increase of the RUNX1 associated corepressor protein PRMT6 [6 , 7] ( Fig 3D ) . At the same time binding of the coactivators p300 and WDR5 decreased upon megakaryocytic differentiation in hCD34+ and K562 cells ( Fig 3E and 3F ) . Concomitantly , the repressive H3R2me2 histone modification mark increased , while the activating modification marks , H3K9ac and H3K4me3 , were significantly decreased in hCD34+ and K562 cells ( Fig 3G , 3H and 3I and S4 Fig ) . These data are in agreement with a repressive role of RUNX1 on erythroid genes during megakaryocytic differentiation [5] . A positive role of miR451 in erythropoiesis in cell lines , zebrafish and mouse models was demonstrated [26 , 27 , 29 , 35] . This notion is also strengthened by our observation that the mature miR451 is more abundant than mature miR144 upon erythroid differentiation ( S6 Fig ) . To test the influence of the single microRNAs in our differentiation system with primary hCD34+ cells ( S7 Fig ) , we constructed miR144/451 expression vectors ( Fig 4A ) . These contained the genomic region of the miR144/451 locus including the entire pri-microRNA ( S8 Fig ) . The different constructs expressed either the wild type miR144/451 or versions , in which the seed sequence of miR144 or miR451 was mutated , respectively ( Fig 4B ) . We transduced human CD34+ stem-/progenitor cells with the given constructs and performed colony-forming assays ( Fig 4C and S7 Fig ) . The total number of colonies was increased upon over-expression of miR451 and reduced with miR144 compared to the empty vector control ( Fig 4D ) . Expression of the wild type miR144/451 construct did not alter erythroid colony formation , whereas expression of the double mutant slightly reduced erythroid colony number . MiR451 ( miR144mt/451 ) increased erythroid colony number , whereas miR144 ( miR144/451mt ) increased CFU-G number , but had no effect on erythroid colonies ( Fig 4E ) . In context of the wild type miR144/451 construct miR144 seemed to counteract miR451 influence on erythopoiesis in this assay ( Fig 4E ) . Taken together , our data confirm that miR451 positively influences erythroid differentiation of primary hCD34+ progenitor cells . The RUNX1/ETO ( R/E ) fusion protein and its truncated variant ( RUNX1/ETO9a similar to R/Etr ) were shown to interfere with normal gene regulation by RUNX1 [49] and negatively impact erythroid differentiation [22 , 50–52] . Furthermore , RUNX1/ETO repressed the miR144/451 promoter in a reporter gene assay ( compare Fig 2F ) . Thus , we wondered if some of the RUNX1/ETO effects might be mediated through the modulation of endogenous miR144/451 . We found that RUNX1/ETO expression in primary hCD34+ cells reduced the colony number in a CFU assay ( Fig 5A ) . Of the remaining colonies , the frequency of erythroid colonies was significantly reduced with the full-length RUNX1/ETO ( R/E ) and the truncated form ( R/Etr ) ( Fig 5B ) . Furthermore , RUNX1/ETO expression reduced expression of the erythroid marker GYPA ( Fig 5C ) . Notably , RUNX1/ETO inhibited the expression of miR144/451 in primary hCD34+ cells ( Fig 5D ) . Over-expression of RUNX1/ETO full length and RUNX1/ETOtr in K562 cells ( Fig 5E and 5F ) left endogenous RUNX1 protein expression largely unaffected and led to inhibition of miR144/451 expression ( Fig 5G ) similar to hCD34+ cells . RUNX1/ETO also inhibited induction of miR144/451 expression under conditions that promote erythroid differentiation of K562 cells ( Fig 5G ) . We then knocked-down R/Etr in K562 cells , which over-expressed R/Etr ( Fig 5I and 5J ) with an shRNA targeting the fusion site of RUNX1/ETO [53 , 54] . This did not influence endogenous RUNX1 protein expression ( Fig 5J ) . In line with a repressive role of RUNX1/ETO , we observed increased miR144/451 expression upon RUNX1/ETOtr knock-down ( Fig 5K ) , showing that the effect of RUNX1/ETO on miR144/451 expression is reversible . To determine if RUNX1/ETO binds endogenously to the miR144/451 promoter , we performed a ChIP-assay in K562 cells expressing an HA-tagged RUNX1/ETO and in Kasumi1 cells expressing endogenous RUNX1/ETO . In both cases , we detected RUNX1/ETO at the miR144/451 promoter ( Fig 5L and 5M ) . Expression of RUNX1/ETOtr reduced the level of the activating histone mark H3K4me3 at the miR144/451 promoter ( Fig 5N ) and led to decreased occupancy of RNApol-II in K562 cells ( Fig 5O ) . Because RUNX1/ETO influences miR144/451 expression in transduced K562 and hCD34+ cells , we analysed miR144/451 expression in two RUNX1/ETO dependent cell lines ( Kasumi1 , SKNO1 ) and RUNX1/ETO positive primary AML samples ( Fig 6A and 6B ) . RUNX1/ETO expression of Kasumi1 , which is an established RUNX1/ETO model cell line , was set as one . We measured 5-fold higher RUNX1/ETO expression in SKNO1 cells , the patient samples #1 expressed 3-times less RUNX1/ETO than Kasumi1 cells and patient sample #2 expressed the highest R/E mRNA level ( Fig 6A ) . Human primary CD34+ and HEK293 cells expressed no RUNX1/ETO ( Fig 6A ) . Subsequently , we determined miR144/451 expression levels . Compared to normal hCD34+ cells , RUNX1/ETO expressing samples exhibited lower levels of miR144/451 ( Fig 6B ) . To determine if endogenous RUNX1/ETO contributes to the repression of miR144/451 we knocked-down RUNX1/ETO in Kasumi1 cells as described ( Fig 6C ) [53] . This indeed led to increased expression of miR144/451 ( Fig 6C ) . As it was demonstrated that the HDAC inhibitor trichostatin-A ( TSA ) leads to degradation of RUNX1/ETO [55] , we treated Kasumi1 cells ( Fig 6D ) with TSA . We found that treatment of Kasumi1 cells with 0 . 01 uM TSA lead to degradation of RUNX1/ETO and the appearance of a degradation band , at higher TSA levels the RUNX1/ETO protein entirely disappeared ( Fig 6D ) . When we measured miR144/451 levels in TSA treated Kasumi1 cells , we found that miR144/451 levels increased upon treatment ( Fig 6E ) . Similarly , treatment of the primary RUNX1/ETO positive patient samples with TSA led to increase of miR144/451 expression ( Fig 6F ) . These data strengthen the notion of a link between RUNX1/ETO and miR144/451 expression , which is sensitive to pharmacological inhibition of RUNX1/ETO . Our results suggest a connection between RUNX1/ETO expression and disturbed erythroid differentiation . As we found that mostly miR451 ( and not miR144 ) acts on erythropoiesis ( Fig 4 ) , we were interested if RUNX1/ETO would influence the expression of miR451 targets . We over-expressed miR451 in K562 cells ( Fig 7A ) and analysed the expression of the known miR451 targets UBE2H , 14-3-3 and IL6R [25 , 27 , 56 , 57] . As microRNAs can act on mRNA stability and translation we examined the mRNA level ( Fig 7 ) and the protein amount ( S9 Fig ) . UBE2H , 14-3-3ξ and IL6R mRNA was decreased upon miR451 over-expression ( Fig 7B–7D ) . Furthermore , we detected a reduction of UBE2H and 14-3-3ξ at the protein level , whereas IL6R remained unchanged ( S9 Fig ) . In contrast , RUNX1/ETO increased UBE2H and 14-3-3ξ mRNA expression ( Fig 7E and 7F ) , however RUNX1/ETO did not influence IL6R significantly ( Fig 7G ) . Taken together , we detected an effect of miR451 on reported target mRNAs and could also show that RUNX1/ETO influences expression of the miR451 targets UBE2H and 14-3-3ξ . Thus , we propose that RUNX1/ETO represses expression of the miR144/451 cluster , which leads to up-regulation of miR451 targets , contributing to altered differentiation ( Fig 7H ) .
Our study shows that RUNX1 influences the expression of specific microRNAs involved in megakaryocytic/erythroid differentiation . Interestingly , RUNX1 acts as an activator of some microRNAs , which suppress erythropoiesis . Thus inversely , the down-regulation of RUNX1 during erythropoiesis would diminish expression of these microRNAs and further support erythropoiesis . For example the microRNAs miR221 and miR222 are negatively influencing erythropoiesis through targeting the c-kit receptor and are both down-regulated during erythroid differentiation [58] . Similarly , down-regulation of miR223 enhances erythropoiesis through de-repressed expression of its target LMO2 [59] . MiR221/222 and miR223 are directly repressed by RUNX1/ETO [42 , 43] , which provides a good example of the intricate relationship between RUNX1 , RUNX1/ETO and microRNAs in normal differentiation and leukemia . Another group of microRNAs , which we found up-regulated by RUNX1 are known to positively influence megakaryopoiesis , such as miR34a , miR146 and miR150 [60–65] . Furthermore , we could reproduce the previously described up-regulation of miR27a by RUNX1 , which was itself shown to target RUNX1 , thereby implementing a feed back regulatory loop [40] . Our study also revealed that RUNX1 represses the expression of two microRNAs namely miR126 and the microRNAs of the miR144/451 cluster . MiR126 inhibits erythropoiesis from embryonic stem cells , partly by targeting the protein tyrosine phosphatase , nonreceptor type 9 ( PTPN9 ) [66] . Interestingly , alteration of miR126 expression promotes leukemogenesis in cooperation with RUNX1/ETO [67] . Our observation that RUNX1 influences miR126 suggests a relationship between RUNX1 and RUNX1/ETO in the regulation of miR126 . However , it is not known if miR126 is directly influenced by RUNX1 or RUNX1/ETO . Recently , miR126 was demonstrated to regulate self-renewal and quiescence of normal hematopoietic stem cells ( HSC ) and acute myeloid leukemia stem cells ( LSC ) [68 , 69] . In the same landmark paper the authors found that miR451 expression is high in the non-LSC population [68] . Whether the lower amount of miR451 in LSCs compared to HSCs is functionally significant currently remains elusive . Because of its role in erythropoiesis we focused on miR144/451 [26 , 27 , 29 , 35] , as we recently reported that RUNX1 epigenetically represses the key erythroid transcription factor KLF1 [5] . In line with the notion that RUNX1 inhibits the erythroid gene expression program during megakaryopoiesis , our data reveal that RUNX1 is a repressor of the erythroid miR144/451 microRNA cluster . We found that RUNX1 and TAL1 are bound to miR144/451 regulatory sequences in undifferentiated hCD34+ cells , which allows some degree of expression . However , upon megakaryocytic differentiation RUNX1 binding is increased , concomitantly RUNX1 associated corepressors such as PRMT6 ( protein arginine methyltransferase 6 ) [6 , 7] are recruited . These trigger repressive histone modification marks and miR144/451 is down-regulated . Repression of miR144/451 expression by RUNX1 is in line with the observation that expression of miR144/451 increases during erythroid differentiation , when RUNX1 expression is down-regulated [8 , 9] . In contrast miR144/451 expression is positively regulated by the transcription factors TAL1 and GATA1 . Both transcription factors have been shown to be important activator of the erythroid gene expression program [70–74] . Taken together our data confirm that miR144/451 is an erythroid microRNA [26 , 35 , 37] , which is activated by TAL1/GATA1 but repressed by RUNX1 during megakaryopoiesis . RUNX1 has been mainly described as a transcriptional activator , however it can also act as a transcriptional repressor [5 , 6 , 75–79] . In contrast , RUNX1/ETO mostly acts as a transcriptional repressor , which recruits a corepressor complex including N-CoR and Sin3 [11 , 13 , 49] although in some cases RUNX1/ETO also activates genes [80] . Because RUNX1 and RUNX1/ETO are simultaneously expressed in leukemic cells it is an interesting question how they interact on miR144/451 expression . Genome wide studies have shown that RUNX1/ETO and RUNX1 compete for binding to the same binding sites [24] and it has been suggested that RUNX1/ETO serves as a dominant negative inhibitor of RUNX1 function [49] . However recent data imply that RUNX1/ETO does not act exclusively as a dominant negative repressor of RUNX1 function [80] . Knock-down of RUNX1/ETO leads to genome wide changes of the chromatin structure and to novel RUNX1 binding sites at places where no RUNX1/ETO was bound before [81] . Furthermore , cell lines expressing RUNX1/ETO , such as Kasumi1 , grow dependent on the presence of wild type RUNX1 [82] . At the molecular level there is evidence of a dynamic balance of RUNX1 and RUNX1/ETO activity in leukemia cells [24] . Our data show that RUNX1 and RUNX1/ETO not exclusively compete functionally , as both can act as repressor on the miR144/451 promoter . This confirms that both can have a similar function on a target gene . Our data imply that both RUNX1 and RUNX1/ETO inhibit the erythroid gene expression program ( [5] , this study ) . However , RUNX1 represses erythroid gene expression upon megakaryocytic differentiation , upon up-regulation of RUNX1 [5] . In contrast , the t ( 8;21 ) takes place in an early stem cell or progenitor stage and can even be detected in healthy newborn children [83] . Accordingly , differentiation of RUNX1/ETO cells is blocked at an early myeloid stage and about 40% of the immature M2-type of leukemia are RUNX1/ETO dependent [10] . Our data imply that RUNX1/ETO would repress expression of miR144/451 similar to RUNX1 . But RUNX1/ETO would repress miR144/451 at an inappropriate differentiation stage and thus interfere with normal differentiation contributing to impaired lineage fidelity of RUNX1/ETO expressing cells . In line with this notion the RUNX1/ETO fusion protein was found to repress erythroid differentiation [22 , 50–52] . This effect was dependent on the presence of the NHR4 domain within the C-terminal domain of the fusion protein [22] . However , the inhibitory effect of RUNX1/ETO was not consistent in all studies [84] and the effect of the RUNX1/ETO truncated form ( REtr or RE9a ) was not tested . Our results show that full length RUNX1/ETO and the truncated RUNX1/ETOtr equally decreased erythroid differentiation of primary hCD34+ cells . Notably , the repression of miR144/451 by RUNX1/ETO was released by knock-down of RUNX1/ETO in Kasumi1 cells . Furthermore , treatment of primary AML patient samples with TSA , which leads to degradation of RUNX1/ETO [55] , increases miR144/451 expression . Additionally , we showed that RUNX1/ETO expression leads to an up-regulation of the miR451 targets UBE2H and 14-3-3ξ . Notably , the connection of miR451 with 14-3-3ξ expression has been linked to erythroid differentiation [27] . This supports the idea that RUNX1/ETO represses miR451 expression thereby inducing the expression of miR451 target mRNAs . Our results and data gathered by others [23 , 43] suggest that RUNX1 acts as master regulator of a regulatory microRNA network in hematopoietic differentiation , which is disturbed by the leukemogenic RUNX1/ETO fusion product .
K562 , Kasumi1 , SKNO1 and HEK293T/17 ( ATCC CRL-11268 ) cells were cultured in Roswell Park Memorial Institute 1640 medium ( RPMI; Life Technologies ) and Dulbecco’s modified eagle medium ( DMEM; Life Technologies ) , respectively . Supplements were 10% fetal calf serum ( FCS ) , 2 mM glutamine and 1% penicillin/streptomycin . Kasumi1 cells were supplemented with 20% FCS . SKNO1 cells were supplemented with 10 ng/mL granulocyte-macrophage colony stimulation factor ( GM-CSF ) . K562 cells were treated with 30 nM 12-o-tetradecanylphorbol-13-acetate to induce megakaryocytic differentiation . For erythroid differentiation K562 cells were incubated with 30 μM hemin . For TSA treatment three concentrations of trichostatin-A ( TSA ) ( Sigma Aldrich ) , 0 . 01 μM , 0 . 5 μM and 1 . 0 μM were used . Cell density was set to 0 , 5x106 cells/ml incubation with TSA was for 24 hours . Gene expression was analysed by quantitative real-time PCR . Primary AML-patient samples were gathered with written informed consent . G-CSF mobilized human primary CD34+ cells were from healthy volunteer donors with written informed consent . The local ethics committee approved the experiments ( permit #329–10 ) . hCD34+ cell were isolated from G-CSF mobilized peripheral blood using immunomagnetic selection according to the manufacturer’s instructions ( Miltenyi Biotec , Bergisch Gladbach , Germany ) . Thereafter , the hCD34+ cells were expanded for 3 days in serum-free expansion medium ( StemSpan SFEM , Stemcell Technologies , Grenoble , France ) supplemented with 100 ng/ml FLT-3 , 100 ng/ml SCF , 20 ng/ml IL-3 and 20 ng/ml IL-6 ( Miltenyi Biotec ) . hCD34+ cells were transduced with lentiviral vectors expressing also green fluorescent protein ( GFP ) , which enabled sorting the cells according to their GFP signal ( FACSAria , BD-Biosciences , Heidelberg ) . Transductions were performed with an MOI of 25 . Knock-down constructs for shRUNX1/ETO were designed using the SEW-backbone as described [85 , 86] . The SiEW or the LEGO lentiviral vector was used for over-expression [48] . Transduced hCD34+ cells were either seeded out on methylcellulose plates for CFU assay according to the manufacturer’s instructions ( StemMACS HSC-CFU with Epo , Miltenyi Biotec ) . Colonies were counted on day 12 after seeding . To induce erythroid differentiation the expanded hCD34+ cells were cultured in StemSpan serum-free expansion medium II ( SFEM II , Stemcell Technologies ) supplemented 20 ng/ml SCF , 5 ng/ml IL-3 , 2 μM dexamethasone ( Sigma-Aldrich , Munich , Germany ) , 0 , 2 μM estradiol ( Sigma-Aldrich ) and 1U/ml erythropoietin ( PeproTech , Hamburg , Germany ) [87] . For megakaryocytic differentiation hCD34+ cells were incubated with SFEM II supplemented with megakaryocyte expansion cytokine cocktail ( StemSpan Megakaryocyte Expansion Supplement , Stemcell Technologies ) . Differentiation was verified by RT-PCR for differentiation markers [5 , 6] and by FACS ( S7 Fig ) . ChIP experiments were performed with hCD34+ differentiated cells ( upon 6 days ) and compared to hCD34+ cells cultured in expansion medium for the same time . Experiments were performed with hCD34+ cells from at least two independent donors . Preparation of cell lysates for chromatin immunoprecipitation ( ChIP ) assays was performed according to the X-ChIP protocol ( Abcam ) with modifications [5] . 2 . 5–10 μg of antibodies were used for immunoprecipitation . ChIP-DNA was purified using DNA purification columns ( ChIP DNA Clean and Concentrator , Zymo Research , USA ) and eluted with 40 μl TE-buffer . DNA was analyzed by quantitative PCR . Antibodies and ChIP-PCR primers are given in S2 File . DNA recovery was calculated as percent of the input , bars represent the standard deviation from at least four independent determinations . ChIP-values of histone modification were corrected for nucleosome density using values gathered by a Histone 3 ChIP . The human miR144/451 promoter region ( -750 bp ) was cloned into the pGL4 . 10 vector . The enhancer region ( 3410–4680 bp ) of miR144/451 was cloned in front of a minimal promoter into the pGL4 . 23 vector . K562 and HEK293T cells were transfected with Metafectene ( Biontex , Martinsried/Planegg , Germany ) with the reporter plasmids and a β-galactosidase expressing vector . 48 h after transfection the luciferase and β-galactosidase activity was analyzed . To control for transfection efficiency the firefly luciferase activity was normalized to β-galactosidase . For RNA isolation the miRNeasy Mini Kit ( Qiagen , Valencia , CA , USA ) was used to purify a miRNA-enriched fraction and a total RNA fraction separately . cDNA was synthesized from the total RNA fraction using Omniscript reverse transcriptase kit ( Qiagen ) . Quantitative RT-PCR was performed using a LightCycler 480 ( Roche , Mannheim , Germany ) and SYBR-Green PCR MasterMix ( Eurogentec , Köln , Germany ) . Relative amounts of mRNA were calculated by the ΔΔCt method using GAPDH or TBP as controls . To detect and quantify mature miRNAs a Taqman assay ( Life Technologies ) or the miScript SYBR Green PCR Kit ( Qiagen ) was used . Primer pairs , Taqman probes and primer assays are given in supplementary material . Total RNA was isolated from K562 cells using the Trizol ( Invitrogen ) method according to the manufacturer's recommendations . Afterwards , the samples were DNAse I ( Sigma ) treated in order to remove DNA contamination . RNA quality was determined using the Agilent 2100 Bioanalyzer ( Agilent Technologies , Santa Clara , CA , USA ) microfluidic electrophoresis . Only samples with comparable RNA integrity numbers were selected for deep sequencing . Library preparation for small RNA-Seq was performed using the TruSeq Small RNA Sample Preparation Kit from Illumina ( catalog RS-200-0012 ) starting from 1000 ng of total RNA . Accurate quantitation of cDNA libraries was performed using the QuantiFluorTM dsDNA System ( Promega ) . The size range of cDNA libraries was determined applying the DNA 1000 chip on the Bioanalyzer 2100 from Agilent ( 140–160 bp ) . cDNA libraries were amplified and sequenced by using the cBot and HiSeq2000 from Illumina ( SR , 1x51 bp , 4 GB per sample ) . Sequence images were transformed with Illumina software BaseCaller to bcl files , which were demultiplexed to fastq files with CASAVA ( version 1 . 8 . 2 ) . Quality check was done via FastQC ( version 0 . 10 . 1 , Babraham Bioinformatics ) . Differentially expressed small RNA molecules were identified using the OASIS platform with standard parameters . RNAs were included for further analysis if they displayed an at least -0 . 5 or +0 . 5 log2-fold change and a P-value <0 . 05 . Sequencing data are available at the GEO-database under the accession number GSE70942 . | The regulatory network between transcription factors , epigenetic cofactors and microRNAs is decisive for normal hematopoiesis . The transcription factor RUNX1 is important for the establishment of a megakaryocytic gene expression program and the concomitant repression of erythroid genes during megakaryocytic differentiation . Gene regulation by RUNX1 is frequently disturbed by mutations and chromosomal translocations , such as the t ( 8;21 ) translocation , which gives rise to the leukemogenic RUNX1/ETO fusion protein . We found that RUNX1 regulates microRNAs , which are of importance at the megakaryocytic/erythroid branching point . Specifically , RUNX1 down-regulates expression of the microRNA cluster miR144/451 during megakaryocytic differentiation by changing the epigenetic histone modification pattern at the locus . We could show that miR451 , one of the micorRNAs of the miR144/451 cluster , supports erythroid differentiation . We found that expression of miR451 is repressed by the RUNX1/ETO fusion protein , resulting in up regulation of miR451 target genes . Our data support the notion that RUNX1 suppresses the erythroid gene expression program including the erythroid microRNA miR451 and that the RUNX1/ETO fusion protein interferes with normal gene regulation by RUNX1 . | [
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] | 2016 | MiR144/451 Expression Is Repressed by RUNX1 During Megakaryopoiesis and Disturbed by RUNX1/ETO |
The cellular composition of heterogeneous samples can be predicted using an expression deconvolution algorithm to decompose their gene expression profiles based on pre-defined , reference gene expression profiles of the constituent populations in these samples . However , the expression profiles of the actual constituent populations are often perturbed from those of the reference profiles due to gene expression changes in cells associated with microenvironmental or developmental effects . Existing deconvolution algorithms do not account for these changes and give incorrect results when benchmarked against those measured by well-established flow cytometry , even after batch correction was applied . We introduce PERT , a new probabilistic expression deconvolution method that detects and accounts for a shared , multiplicative perturbation in the reference profiles when performing expression deconvolution . We applied PERT and three other state-of-the-art expression deconvolution methods to predict cell frequencies within heterogeneous human blood samples that were collected under several conditions ( uncultured mono-nucleated and lineage-depleted cells , and culture-derived lineage-depleted cells ) . Only PERT's predicted proportions of the constituent populations matched those assigned by flow cytometry . Genes associated with cell cycle processes were highly enriched among those with the largest predicted expression changes between the cultured and uncultured conditions . We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity .
Heterogeneity as a description of a biological sample typically refers to the co-existence of phenotypically and functionally distinct cell populations therein . In a dynamic system such as in vitro stem cell growth and differentiation , cells continuously self-renew , differentiate and die in response to a changing microenvironment . The ability to elucidate compositions of heterogeneous samples with respect to their constituent ( homogeneous ) populations is a pre-requisite for identifying the parameters governing these dynamic systems . Although cellular compositions can be deconvolved using flow cytometry gated on constituent population-associated surface antigens or fluorescent intracellular proteins , these approaches are constrained by their requirements for sample formats – only cells in suspension media can be analysed – and have limited power to discover novel populations emerging from heterogeneous samples . A more efficient , unbiased cellular decomposition technique that recapitulates flow cytometry-based deconvolution of heterogeneous samples using less material is desirable . For elucidating compositions of highly heterogeneous samples , gene expression-based cellular deconvolution is more efficient , unbiased and economical . The technique has been used to decompose samples from yeast cell culture [1] , tumor tissues [2] , and peripheral blood of systemic lupus erythematosus [3] and multiple sclerosis patients [4] . Existing studies model gene expression profiles of heterogeneous samples ( termed mixed profiles ) as positively weighted sums of the gene expression profiles of pre-specified reference populations , where these reference profiles are chosen to represent constituent populations within the heterogeneous samples . The task is to estimate the proportion of each reference population within the heterogeneous samples . These models have two major limitations . First , reference profiles for all constituent populations of the heterogeneous samples of interest have to be available; however , new cell types or populations may have emerged from cell differentiation in dynamic circumstances , and cannot be accounted for by existing methods . Second , reference profiles must accurately represent the gene expression profiles of the actual constituent populations ( termed the constituent profiles ) of the heterogeneous samples of interest . However , because reference population samples and heterogeneous samples of interest are likely collected separately and therefore may exhibit transcriptional variations due to microenvironmental ( e . g . , inter-cellular communication ) and developmental ( e . g . , culture conditions ) changes , reproduction of flow cytometry analysis under such transcriptional variations cannot be achieved by existing methods . Thus , we aimed to develop flexible deconvolution models that consider the presence of new cell types or populations in heterogeneous samples , and also consider systematic fluctuations in gene expression between reference profiles and constituent profiles . Recently , Quon and Morris developed ISOLATE [5] based on the Latent Dirichlet Allocation ( LDA ) model [6] for estimating proportions of cancer cells in tumor samples using quantitative gene expression data . In contrast to the linear regression models , these models use a multinomial noise model [7] that is a better fit to measurement noise in gene expression data [8] . We hypothesized that these models could be extended to allow transcriptional variations between reference and constituent populations . Here we compare four models: a linear regression model called the non-negative least squares model ( NNLS ) [9] , the non-negative maximum likelihood model ( NNML ) , the non-negative maximum likelihood new population model ( NNMLnp ) , and the perturbation model ( PERT ) . NNLS assumes all constituent populations are represented in the reference profiles , and uses a linear regression framework to estimate the proportion of each heterogeneous sample attributable to each of the reference populations . NNML makes the same assumptions and solves the same problem as NNLS , but uses the LDA [6] framework for posing and solving the problem . NNMLnp is a version of ISOLATE [5] that assumes there is an additional constituent population in the heterogeneous samples that is not represented by the available reference profiles , and is therefore estimated . PERT is our new model that is based on the NNML framework but accounts for transcriptional variations between reference and constituent profiles . The models were applied to uncultured mono-nucleated and lineage-depleted ( Lin- , where cells expressing blood cell lineage-associated cell surface antigens are removed ) cells enriched from fresh human umbilical cord blood , and cultured-derived Lin- cells . Model predictions were validated using an established flow cytometry assay . Overall , our analysis demonstrated that averaged absolute differences between PERT's predictions and flow cytometry measurements were significantly lower than the other models for uncultured mono-nucleated cells , uncultured Lin- cells , and culture-derived Lin- cells . Gene Ontology enrichment analysis of the genes that underwent 2-fold perturbation when comparing uncultured with culture-derived cells suggested that the transcriptional variations between these two cell populations were the result of up-regulation of cell cycle related genes in culture-derived cells . We show that ( i ) cells presenting the same cell surface antigens can exhibit differences in transcriptional programs when they are subjected to different microenvironmental and developmental conditions; ( ii ) these variations cannot be corrected using current batch effect models , highlighting the need for care when comparing primary cells subjected to different exogenous perturbations; and ( iii ) these variations can be captured by modeling a shared gene-specific rescaling ( in other words , a multiplicative perturbation ) as part of the expression deconvolution . Our new model , PERT , is a deconvolution model that addresses transcriptional variations between reference and constituent profiles . The model is readily applicable to circumstances where available reference profiles are collected under different microenvironmental or developmental conditions from the heterogeneous samples .
In this study , four models , NNLS , NNML , NNMLnp and PERT , were compared for their ability to deconvolve uncultured and culture-derived heterogeneous human blood samples . We used two measures of success: deconvolution accuracy defined as the proportion of variance ( R2 ) in the measured proportions of constituent populations explained by the model's predictions , and averaged absolute difference between model predictions and experimental measurements . Given the gene expression profile of a heterogeneous sample that is a physical mixture of its constituent populations ( Figure 1A ) , NNLS ( Figure 1B-i ) assumes that both the reference populations ( whose gene expression profiles were provided for deconvolution ) and the constituent populations were subjected to the same microenvironmental and developmental conditions and thus were equivalent . Therefore , a mixed profile is modeled as a positively weighted sum of reference profiles . Weight wi indicates the proportion of reference population i within the heterogeneous sample , and is fit by minimizing the least squares error between the estimated and observed mixed profiles under an additive Gaussian measurement noise model [1] , [3] , [4] , [10] while constraining the weights to be non-negative [9] . However , several studies have shown that the variance in gene expression measurement noise scales with the mean [8] , [11] , [12] , contrary to the assumption of the additive Gaussian noise model . NNML [6] ( Figure 1B-i ) is similar to NNLS , but replaces the additive Gaussian measurement noise model with a multinomial noise model which has the desired scaling . However , neither NNLS nor NNML is designed to address two key challenges: first , the presence of additional constituent populations in the heterogeneous sample whose corresponding reference profiles are not available; second , transcriptional variations between constituents and corresponding reference populations that arise due to microenvironmental or developmental factors . We addressed the first challenge using NNMLnp ( Figure 1B-ii ) . The model estimates the gene expression profile γ of a new , latent reference population to capture expression patterns in the heterogeneous samples that are not explained by the provided reference profiles . Simultaneously , the model estimates the proportions of individual reference populations in the heterogeneous samples . We developed PERT ( Figure 1B-iii ) to address the second challenge . The model estimates a genome-wide perturbation vector ρ where each element of ρ , ρg , reflects the fold difference in expression of gene g in the constituent profiles versus the reference profiles: ρg>1 indicates increased expression of gene g in constituent profiles compared to the reference profiles; ρg = 1 indicates no change; and ρg<1 indicates decreased expression . Simultaneously , the model estimates the proportions of individual reference populations in the heterogeneous samples ( Materials and Methods ) . To compare deconvolution accuracy ( R2 ) and averaged absolute differences between the linear regression and LDA-based probabilistic models , we used archival gene expression data of heterogeneous samples created by mixing RNA samples of Raji , Jurkat , IM-9 and THP-1 cell lines in known proportions [3] . Compositions of the RNA mixtures were deconvolved using NNLS and NNML with gene expression profiles of 54 , 613 Affymetrix probes . The model predicted cell proportions were benchmarked against the results from [3] ( Figure 2A ) , which were obtained using a NNLS model with an optimal number of 275 signature probes per cell line that were selected to maximize transcriptional distinction between the cell lines . The deconvolution accuracy achieved by NNML using the 54 , 613 probes ( Figure 2D ) was only 0 . 01 lower than that achieved by NNLS using the optimized signature probes ( Figure 2A ) , and the averaged absolute difference of NNML was 0 . 18% higher . For NNML using the optimized probes , the deconvolution accuracy ( Figure 2C ) was 0 . 08 lower than that of NNLS ( Figure 2A ) , and the averaged absolute difference was 1 . 55% higher . In contrast , deconvolution accuracy of NNLS using all the probes ( Figure 2B ) was 0 . 25 lower than that of NNLS using the optimized probes , and the averaged absolute difference was 5 . 02% higher . In this cell line analysis , the mixed profiles were derived from mixtures of RNA samples of 4 cell lines; there was no opportunity for microenvironmental or developmental factors to influence the gene expression of the reference and the constituent populations . Our analysis establishes a baseline that the LDA-based probabilistic model eliminates the need for cell line signature probes while performing deconvolution as accurately as the linear regression model with carefully optimized cell line signature probes , when the reference profiles match the constituent profiles of heterogeneous samples ( Figures S1 , S2 , S3 in Text S1 ) . Analysis of blood progenitor cell surface antigens is a widely used surrogate for cellular functional properties , despite widespread recognition that this parameter is dynamic , especially on culture-derived cells [13] . Assuming that functional properties of a cell population are encoded by its transcriptional program , we hypothesized that cells from different microenvironmental and developmental conditions exhibit varied transcriptional programs despite their identical presentation of cell surface antigens . To validate this hypothesis , we compared genome-wide transcriptome profiles of uncultured and culture-derived blood mature cells and progenitor cells . The experimental protocol is shown in Figure 3A . In brief , megakaryocytes and colony forming unit-monocytes ( CFU-M ) were sorted from fresh ( day-0 ) human umbilical cord blood . Enriched Lin- cells from the same umbilical cord blood samples were cultured as described in [14] . Megakaryocytes and CFU-M were harvested on day 4 using the same cell surface antigens and gating strategies as for day-0 samples ( Figure S4 in Text S1 ) . Gene expression profiles of the uncultured ( day-0 ) and culture-derived ( day-4 ) cells were obtained . As all the samples were prepared by following the same technical procedure , no batch removal analysis of gene expression data was performed . Figure 3B shows that robust multi-array average ( RMA ) [15] normalized gene expression profiles of the day-0 and day-4 samples segregated into “uncultured” and “cultured” clusters based on their Pearson's correlation coefficients , instead of “megakaryocyte” and “CFU-M” clusters as would be expected from a functional perspective . Gene set enrichment analysis ( GSEA ) [16] suggested that genes up-regulated in day-4 samples compared to day-0 samples were enriched in cell cycle related processes , and those down-regulated were enriched in immune and inflammatory responses ( Figure 3C , Table S1 ) . We anticipated that a “cell culture effect” had caused uncultured and culture-derived cells expressing the same lineage-associated surface antigens to exhibit different transcriptional programs . We then explored if PERT could capture and account for the cell culture effect . The model was applied to day-0 and day-4 megakaryocytes ( or CFU-M ) to estimate a genome-wide multiplicative perturbation vector , ρ , to capture gene-specific cell culture effects ( Table S2 ) . GSEA was applied to the genes whose expression levels had been perturbed by more than 2-fold ( ρg<0 . 5 or ρg>2 ) when comparing day-4 megakaryocytes with day-0 megakaryocytes , and day-4 CFU-M with day-0 CFU-M . We found that the GSEA results for megakaryocytes ( Table S3 ) and CFU-M ( Table S4 ) were similar . Overall , the day-4 samples exhibited higher expression of cell cycle , cell division , DNA and RNA metabolic processes and cell component assembly related genes ( Conditional hypergeometric test [17] , P<0 . 01 ) , and the day-4 samples exhibited a decrease in expression of immune system related genes ( Conditional hypergeometric test [17] , P<0 . 01 ) . These results were consistent with the results shown in Figure 3C and Table S1 , suggesting that PERT had captured the cell culture effects . The ρ vector from comparing day-4 with day-0 megakaryocytes ( or from comparing day-4 with day-0 CFU-M ) was then applied to the gene expression profiles of day-0 CFU-M ( or day-0 megakaryocytes ) to obtain perturbed gene expression profiles of day-0 CFU-M ( or day-0 megakaryocyte ) . As shown in Figure 3D ( or 3E ) , the perturbed gene expression profiles of day-0 CFU-M ( or day-0 megakaryocyte ) exhibited a stronger Pearson's correlation with that of day-4 CFU-M ( or day-4 megakaryocyte ) than the original gene expression profiles of day-0 CFU-M ( or day-0 megakaryocyte ) , confirming the success of PERT in estimating systematic effect of cell culture on reference profiles ( Figures S5 and S6 in Text S1 ) . Having established that expression deconvolution was accurate for samples where all constituent populations were known and that PERT could capture systematic transcriptional variations between uncultured populations and the cultured versions of those populations , we then used the four models — NNLS , NNML , NNMLnp and PERT — to deconvolve uncultured human mono-nucleated and Lin- umbilical cord blood samples ( Figure 4A ) where compositions are not pre-specified . Mixed profiles of mono-nucleated cells enriched from fresh human umbilical cord blood were first deconvolved to estimate the proportions of 11 developmentally and functionally distinct blood populations ( Table S5 and Text S1 ) using their reference profiles from [18] . As expected , because the two sets of samples were obtained by different labs , batch effects between the mixed profiles and the reference profiles were observed , and these were removed using the supervised normalization of microarray ( SNM ) method [19] . We benchmarked the model predicted cell proportions ( Figure 4B and Table S6 ) against those measured by flow cytometry ( Figure 4C and Table S6 ) using the same cell surface antigens originally used to recover the reference populations in [18] . The same analysis was performed for fresh human umbilical cord blood-derived Lin- cell samples ( Figures 4D and 4E , and Table S6 ) , which are known to have different compositions from mono-nucleated cell samples . The gene expression profile γ of the new reference population from NNMLnp and the perturbation vector ρ from PERT are given in Table S7 . Results of GSEA for genes whose perturbation factor ρg is <0 . 5 or >2 are in Table S8 . Notably , the deconvolved proportions of uncultured mono-nucleated cell samples and Lin- cell samples using NNML and that of NNMLnp were not substantially different ( P = 2 . 43×10−1 ) ( Figures 4F and 4G ) . For mono-nucleated cell samples , there was a large improvement in the deconvolution performance of PERT compared to the other three models in terms of both the deconvolution accuracy R2 and the averaged absolute differences ( Figures 4F and 4G ) . However , for Lin- cell samples , while the deconvolution accuracy R2 of NNLS and PERT were both high , the absolute differences of PERT were significantly lower than that of NNLS ( P = 5 . 00×10−3 ) . The Bayesian information criterion ( BIC ) indicated preferential applicability of PERT in deconvolving these uncultured heterogeneous samples ( Table 1 and Figure 4H ) . This analysis indicates that PERT recovered cell proportions of 11 reference populations with averaged absolute differences as low as 2% . In addition , PERT only required two biological samples of mono-nucleated cells and Lin- cells , and 4 to 10 biological profiles of individual reference populations , whereas flow cytometry required preparation of 41 aliquot samples ( including controls ) to measure the proportions of the same constituent populations as the deconvolution analysis in one mono-nucleated or Lin- cell sample . Having established that PERT could capture culture-associated changes in gene expression in relatively pure populations ( analysis of day-4 versus day-0 megakaryocytes and CFU-M ) and microenvironment-associated changes in heterogeneous samples ( analysis of uncultured mono-nucleated and Lin- cell samples ) , we next applied the model to analyze culture-derived heterogeneous samples from a hematopoietic stem and progenitor cell ( HSPC ) expansion culture . The experimental setup is described in detail elsewhere [20] . In brief , human umbilical cord blood Lin- cells were seeded in a suspension culture that had been optimized for HSPC expansion . After 4 days , Lin- cells were harvested , and then their genome-wide transcriptome expression was profiled ( Figure 5A ) . Proportions of the 11 blood cell lineages [18] were deconvolved ( Table S5 and Figure S8 in Text S1 ) . Model predictions ( Figure 5B and Table S6 ) were validated by the cell proportions assigned by flow cytometry ( Figure 5C and Table S6 ) . The deconvolution accuracy R2 of PERT was significantly higher than that of the other models ( Figure 5D ) , and the averaged absolute differences of PERT were lower as assessed by the Wilcoxon signed rank test ( P for PERT versus NNLS , PERT versus NNML , and PERT versus NNMLnp were 9 . 00×10−3 , 1 . 00×10−3 and 1 . 39×10−1 , respectively ) ( Figure 5E ) . In addition , the BIC ( Table 1 and Figure 5F ) indicates preferential applicability of PERT in this case . Intriguingly , compared with the results for uncultured samples for which deconvolution accuracy R2 and averaged absolute differences of NNML and NNMLnp were not significantly different , the predictions of NNMLnp were much more correlated ( R2 = 0 . 49 versus R2 = 0 . 06 ) with the cell proportions in the culture-derived samples than the NNML model , although the averaged absolute differences of the two models were similar . GSEA was performed for genes identified by PERT as being perturbed in the mixed profiles by more than 2-fold over the reference profiles ( Table S9 ) . Cultured-derived Lin- cells were found to upregulate genes enriched in cell cycle , metabolic and catabolic processes , and biosynthetic processes ( Conditional hypergeometric test [17] , P<0 . 01 ) ( Table S10 ) . Collectively , this analysis showed that PERT recovered cell proportions of culture-derived heterogeneous samples using the gene expression profiles of uncultured reference populations . PERT analysis revealed that transcriptome differences between uncultured and culture-derived cells of the same phenotypic identity were attributable to the increased expression of cell cycle process related genes by the culture-derived cells .
We have demonstrated that the transcriptional variations due to microenvironmental and developmental differences could not be addressed using existing batch effect models in gene expression deconvolution . We have introduced PERT , a new deconvolution method that allows for transcriptional variations between reference populations and constituent populations in heterogeneous samples of interest . Transcriptional programs of human cells fluctuate with circadian rhythms and vary among individuals [21] . Furthermore , procedures of blood collection , cell isolation and RNA extraction affect the expression of specific genes [22] . As reference profiles and mixed profiles are often collected by different labs , available reference profiles may not accurately represent the corresponding constituent populations composing the mixed profiles , even though they have the same cell surface markers . Gene expression differences between the reference profiles and the constituent profiles cannot be accounted for by the existing batch effect models because they assume that the reference and the constituent populations are the same , except for technical differences in data collection . Differences in performance of the four models for culture-derived samples may be explained by one of several factors that can complicate deconvolution . First , progenitor cells in culture can differentiate and give arise to intermediate cell types or populations that are not included in the reference populations . This could explain why NNMLnp captured seven times more compositional variation than NNML when they were used on culture-derived Lin- cells , but the two models produced similar results when they were used on uncultured samples . Second , culture-derived heterogeneous samples and reference samples which were directly isolated from patient samples had been exposed to different environments . Cell extrinsic factors cause genome-wide transcriptional variations [23] between the reference and constituent profiles . We found that these variations were not easily captured by modeling the presence of a new population in heterogeneous samples as is done by NNMLnp . In contrast , modeling these variations by a systematic genome-wide perturbation to the reference profiles as done by PERT was more successful . We anticipate that the improved performance of PERT in deconvolving heterogeneous samples over the other tested models herein is attributed to its more flexible and appropriate model assumptions . First , accumulating evidence has indicated the association between cell phenotypes and molecular networks consists of relatively small numbers of genes out of the whole genome [18] . Although components of cell phenotype-associated molecular networks can be used as cell signature genes for NNLS deconvolution , identification of those components is challenging , especially for a large number of cell types within the hematopoietic system because mature hematopoietic cells are generated from hematopoietic stem and progenitor cells through an amplifying differentiation hierarchy and the transcriptional profiles that distinguish different but related cell types is still very much an area of active investigation [18] , [24] . Second , definition of cell type signature genes is technically subjective . Third , although NNML eliminates the need to identify cell type signature genes , the model assumes that each constituent population is represented by one or more reference populations , and that the reference profiles are accurate estimates of the profiles of the constituent populations . However , reference profiles are rarely accurate estimates of the constituent profiles in practice due to the effects of environmental factors , technical factors and cell-cell interactions on gene expression that often occur in cell culture . While NNMLnp can help address the problem of an incomplete reference profile set , it cannot account for systematic variations in reference and constituent profiles . PERT is the first step towards addressing these transcriptional variations due to culture conditions . A future development of PERT could be to estimate a perturbation factor for each reference population to represent cell type specific culture effect , as opposed to the shared perturbation factor used here . Such a model would be similar to an expression deconvolution model in which both the reference populations and their proportions were unknown with a strong prior to guide the deconvolution and ensure identifiability . We suspect that such model would require more data to fit . Here we demonstrated success in applying in silico techniques to deconvolve compositions of heterogeneous samples using reference profiles collected under different conditions . As a large amount of resource and energy is required to generate a comprehensive data set of reference profiles , the ability to use available reference profiles to decompose heterogeneous samples potentially collected from different environmental conditions should dramatically extend the utility of archival gene expression datasets . Selection of a proper deconvolution model can be challenging in the situation where the nature or content of mixed samples is uncertain . In this work , we explored R2 , averaged absolute differences , and BIC as a means to select between NNLS , NNML , NNMLnp and PERT . Intriguingly , we found that PERT performed as well as , or better than the other models in all tested cases . The model has allowed us to recapitulate flow cytometry estimated cellular compositions of heterogeneous samples in a more efficient , unbiased manner . Our results demonstrated the importance of including prior knowledge of biological systems ( e . g . , existence of new cell populations , transcriptional variations between reference and constituent populations ) to achieve excellent deconvolution accuracy . We anticipate that PERT is not only relevant to the hematopoietic system , but is applicable to any heterogeneous biological system given prior knowledge about the gene expression profiles of reference populations .
In the following model description , variables are in italics , constants are in uppercase , and vectors are in bold . All deconvolution models herein make several common assumptions . They assume that the input consists of two sets of expression profiles . One set consists of D heterogeneous profiles corresponding to the gene expression profiles of D heterogeneous samples , where xd is a vector of length G and xd , g is the discretized total intensity measurement for gene g in sample d . The other set consists of K reference profiles corresponding to the gene expression profiles of K reference cell populations , where vk is a vector of length G and vk , g is the total intensity measurement for gene g in reference population k . The standard formulation for deconvolution is to model each heterogeneous profile xd as a linear combination of measurements of the reference populations , vk , weighted by mixture proportions θd: ( 1 ) We used log2 transformed gene expression data and the nnls ( ) function from the nnls package ( version 1 . 4 ) of R to estimate the optimal non-negative values of θd , k as previously described [9] . We then re-scaled the values θd , k such that Σkθd , k = 1 as done in [3] . There are several limitations with the NNLS model that we aimed to address in this work . First , NNLS requires cell type signature genes . However , identifying cell type-specific signature genes for different but related reference populations is challenging ( Text S1 ) . Second , as shown below , probabilistic representations of deconvolution can be naturally extended to estimate the profile of an additional ( unknown ) reference population , or to explicitly model the effects of cell culture on the gene expression profiles of cells . NNML is a probabilistic alternative to NNLS , which uses a different noise model that is less sensitive to the selection of cell type signature genes and also provides a basis upon which to address the estimation of an unknown reference population ( NNMLnp ) or cell culture effects ( PERT ) . NNML treats heterogeneous expression profiles as digital measurements of gene abundances in a sample: that is , xd , g represents a count of the number of times that gene g was found in sample d as measured in arbitrary units of intensity or read density . In other words , there are xd , g observations of a unit of intensity . We model each of those xd , g observations as coming from exactly one constituent population; xd , g is therefore the sum of contributions from each of the constituent cell populations present in the heterogeneous sample , and Nd = Σgxd , g is the total number of observations for sample d . In this work , the units are selected so that Nd is on the order of 107 . The goal of deconvolution is to estimate θd , k , the fraction of all observations in sample d attributable to reference population k , by identifying from which reference population each observation originates . In order to infer from which reference population each observation originates , we expand each heterogeneous expression profile from the compact vector xd into an alternative vector td of length Nd , where td , n ∈ {1 , … , G} represents the nth observation from sample d . Note that the vectors td and xd store the same information because Σn[td , n = g] = xd , g , where [td , n = g] is the indictor function that is 1 if td , n = g , and otherwise 0 . Representing heterogeneous profile d using the vector td allows us to simplify the deconvolution problem to inferring a vector zd of length Nd , where zd , n = k indicates that the observation td , n originated from reference population k . Inference of all zd , n variables allows straightforward estimation of θd , k; we can set θd , k = Σn[zd , n = k]/Nd . Also , because NNML treats heterogeneous expression profiles td , n as digital measurements , it is natural to treat each observation td , n as a draw from a discrete distribution , whose parameters characterize the expression profile of the sample d . We first converted each of the reference expression profiles vk into parameters of a discrete distribution βk , where βk , g = vk , g/Nk and Nk = Σgvk , g . For each observation td , n in heterogeneous sample d , conditioned on the knowledge of which constituent population it is from ( i . e . knowledge of zd , n ) , the likelihood of observing the specific gene td , n is defined by the appropriate reference distribution . NNML makes two limiting assumptions . First , it assumes that all constituent populations of each heterogeneous sample are represented by at least one discrete distribution βk from the provided reference profiles . Second , it assumes that each reference profile βk faithfully recapitulates the gene expression pattern of the corresponding cell type k in each heterogeneous sample . Under these assumptions , NNML estimates θd by maximizing the following complete log likelihood function using conjugate gradient descent until convergence of the likelihood function: ( 2 ) ( 3 ) ( 4 ) ( 5 ) The initial states of the hidden variables θd are all set to 1/K before optimization . See Program S2 for the NNML program . NNML deconvolution was performed on linear , untransformed gene expression data . NNMLnp is an extension of NNML . This model relaxes NNML's assumption that all constituent populations in each heterogeneous sample are represented in the provided reference sets βk . Namely , NNMLnp assumes that there exists a single cell population γ that is not in the reference set βk but that is present in at least one of the heterogeneous samples . NNMLnp is a slightly modified version of the ISOLATE [5] model that we reported previously . In order to prevent overfitting in the estimation of γ , we place a prior over γ such that γ is drawn from a Dirichlet distribution centred on a convex combination of the existing reference populations βk because we assume that , all else being equal , the new population will be related to the existing reference populations . The convex weights ω , as well as the strength of the prior κ , are estimated from the data . Finally , NNMLnp also puts a Dirichlet prior over each variable θd to prevent overfitting: that prior has mean α that is also estimated . Estimating the hidden variables and parameters ( γ , ω , κ , α and θd ) are optimized by ( block ) coordinate descent; the complete log likelihood function is cyclically optimized with respect to each set of hidden variables and parameters using conjugate gradient descent , until convergence of the likelihood function . The complete likelihood function is as follows ( variables θd , td , n , zd , n , and βk have the same meaning as for NNML ) : ( 6 ) ( 7 ) ( 8 ) ( 9 ) ( 10 ) ( 11 ) Initialization of model parameters is described in the Text S2 . The major difference between NNMLnp and ISOLATE is that the Dirichlet prior on the new population ( eq . 7 ) in NNMLnp is replaced with a product of Gamma priors in ISOLATE . See Program S2 for the NNMLnp program . NNMLnp deconvolution was performed on linear , untransformed gene expression data . In contrast to NNMLnp , PERT extends NNML by relaxing its other main assumption , namely , that the provided reference distributions βk faithfully represent the expression patterns of the actual constituent cell populations in each heterogeneous sample . PERT defines new constituent profiles γ1 through γK , where γk is based on the reference profile βk that has been adjusted for systematic differences due to cell culture effects , for example . These systematic changes in gene expression are assumed to act equally across all constituent cell populations , and are defined by multiplicative perturbation factors ρg . PERT uses a prior distribution over ρg , with a mean of one and strength of κ , to regularize ρg such that it introduces as few deviations as possible . Similar to NNMLnp , we introduce a prior over θd for regularization , where the mean of that prior , α , is also estimated . Estimating hidden variables and parameters ( ρg , κ , α and θd ) is done by cyclically optimizing the complete log likelihood function with respect to each hidden variable and parameter using conjugate gradient descent , until convergence of the likelihood function . The likelihood function is as follows ( variables θd , td , n , zd , n , and βk have the same meaning as for NNML ) : ( 12 ) ( 13 ) ( 14 ) ( 15 ) ( 16 ) ( 17 ) Initialization of model parameters is described in the Text S2 . See Program S3 for the PERT program . PERT deconvolution was performed on linear , untransformed gene expression data . NNML , NNMLnp and PERT were implemented in Matlab , and the programs were used to obtain the results herein . The Matlab programs were converted into Octave to allow them to be used with free software . The programs are found in the supporting information ( See instructions in Text S2 ) . Samples of human umbilical cord blood were obtained from Mount Sinai Hospital ( Toronto , ON , Canada ) and processed in accordance to guidelines approved by the University of Toronto . Mono-nucleated cells were obtained by lysing the erythrocytes . Lineage-depleted ( Lin- ) cells were isolated from mono-nucleated cells using the EasySep system ( Stemcell Technologies , Vancouver , BC , Canada ) according to the manufacture's protocol . Genome-wide expression of mono-nucleated cells and Lin- cells were profiled by isolating total RNA using Rneasy Mini kits ( Qiagen ) . RNA quality was tested on both NanoDrop ( ND-1000 ) and BioAnalyzer machines . cDNA samples were prepared using Nugen IVT kit , and split into 2 technical replicates . Hybridization was performed using Affymetrix Gene Chip HG-U133A2 . 0 arrays on the Affymetrix Gene Chip Scanner 3000 machine . CD34−CD33+CD13+ colony forming unit-monocytes ( CFU-M ) and CD34−CD41+CD61+CD45− megakaryocytes were sorted from pooled fresh human umbilical cord blood samples on BD FACS Aria ( CD34: PE; CD33: APC; CD13: PERCP; CD41: PE; CD61: FITC; CD45: APC . All antibodies were purchased from BD BioScience ) . Lin- cells were cultured as described in [14] . On day 4 , CFU-M and megakaryocytes were sorted . Total RNA of the four samples was isolated using RNeasy Micro kit ( Qiagen ) . RNA quality was tested on both NanoDrop ( ND-1000 ) and BioAnalyzer machines . cDNA samples were prepared using Ambion IVT kit . Hybridization was performed using Affymetrix HG-U133Plus2 arrays on the Affymetrix Gene Chip Scanner 3000 machine . Data of two biological replicates were collected . Compositions of mono-nucleated cells and Lin- cells were analyzed by flow cytometry on either BD FACS Canto Flow Cytometer or BD LSRFortessa . Data analysis was performed with BD FACSDiva Software version 5 . 0 . 1 . Normalized gene expression data ( Affymetrix Gene Chip HG-U133Plus2 ) of IM-9 , Jurkat , Raji , THP-1 cell lines , and mixtures of the four cell lines were downloaded from the Gene Expression Omnibus ( GSE11103; downloaded on 23rd August 2012 ) . Affymetrix CEL files ( Affymetrix Gene Chip HG_U133AAofAv2 ) of 21 human umbilical cord blood-derived pure populations ( Table S5 ) were obtained from the authors of [18] ( GSE24759 ) . Affymetrix CEL files ( Affymetrix Gene Chip HG-U133Plus2 ) of day-4 Lin- cells were obtained from the authors of [20] ( GSE16589 ) . Microarray data were analyzed in BioConductor using the affy package . For the analysis of CFU-M and megakaryocyte profiles , RMA [15] background adjusted , normalized profiles , without batch removal , were used because all the samples for this analysis were processed under the same technical setup . The processed data of CFU-M and megakaryocyte samples are found in Table S11 . For the deconvolution studies of uncultured and culture-derived samples , RMA [15] background adjusted , non-normalized reference and mixed profiles were post-processed by the supervised normalization of microarray ( SNM ) method [19] in order to normalize data while removing the batch effects between the two datasets . The processed data of uncultured and culture-derived samples are found in Table S12 and Table S13 , respectively . Hierarchical clustering shown in Figure 3 was obtained from log2 gene expression values using an average agglomeration method with a distance matrix of ( 1 - Pearson's correlation coefficients ) . GSEA was either done using the GSEA program ( v2 . 0 ) from the GSEA website using gene sets c5 . all . v3 . 0 . orig . gmt ( downloaded on Jan 23 , 2012 ) , or using the GSEAStat ( v2 . 20 . 0 ) and GSEABase ( v1 . 16 . 0 ) packages with the generic GOslim gene sets ( download from the GSEA website on Jan 21 , 2012 ) in the BioConductor . Unless otherwise stated , all P-values were calculated using the Wilcoxon signed rank test in R . Association test of Pearson's correlation was done in R using the cor . test ( ) function . Gene Expression Omnibus , GSE40831 . | The cellular composition of heterogeneous samples can be predicted from reference gene expression profiles that represent the homogeneous , constituent populations of the heterogeneous samples . However , existing methods fail when the reference profiles are not representative of the constituent populations . We developed PERT , a new probabilistic expression deconvolution method , to address this limitation . PERT was used to deconvolve the cellular composition of variably sourced and treated heterogeneous human blood samples . Our results indicate that even after batch correction is applied , cells presenting the same cell surface antigens display different transcriptional programs when they are uncultured versus culture-derived . Given gene expression profiles of culture-derived heterogeneous samples and profiles of uncultured reference populations , PERT was able to accurately recover proportions of the constituent populations composing the heterogeneous samples . We anticipate that PERT will be widely applicable to expression deconvolution strategies that use profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular phenotypic identity . | [
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] | 2012 | PERT: A Method for Expression Deconvolution of Human Blood Samples from Varied Microenvironmental and Developmental Conditions |
Identifying molecular connections between developmental processes and disease can lead to new hypotheses about health risks at all stages of life . Here we introduce a new approach to identifying significant connections between gene sets and disease genes , and apply it to several gene sets related to human development . To overcome the limits of incomplete and imperfect information linking genes to disease , we pool genes within disease subtrees in the MeSH taxonomy , and we demonstrate that such pooling improves the power and accuracy of our approach . Significance is assessed through permutation . We created a web-based visualization tool to facilitate multi-scale exploration of this large collection of significant connections ( http://gda . cs . tufts . edu/development ) . High-level analysis of the results reveals expected connections between tissue-specific developmental processes and diseases linked to those tissues , and widespread connections to developmental disorders and cancers . Yet interesting new hypotheses may be derived from examining the unexpected connections . We highlight and discuss the implications of three such connections , linking dementia with bone development , polycystic ovary syndrome with cardiovascular development , and retinopathy of prematurity with lung development . Our results provide additional evidence that plays a key role in the early pathogenesis of polycystic ovary syndrome . Our evidence also suggests that the VEGF pathway and downstream NFKB signaling may explain the complex relationship between bronchopulmonary dysplasia and retinopathy of prematurity , and may form a bridge between two currently-competing hypotheses about the molecular origins of bronchopulmonary dysplasia . Further data exploration and similar queries about other gene sets may generate a variety of new information about the molecular relationships between additional diseases .
The study of the health implications of developmental processes has now entered the genomic era . The recent sequencing of an entire fetal genome [1] has demonstrated the possibility of applying molecular methods to design novel prenatal diagnostics . The development of therapeutic approaches for personalized fetal treatment of developmental disorders is now on the horizon [2] . Genomic approaches are providing new insights into causes of and possible treatments for such widespread pediatric disorders as asthma [3] and autism [4] . A growing awareness that development may influence lifelong health risk [5] , [6] has led to closer examination of the molecular links between developmental processes and disease at multiple life stages . Despite considerable progress , our understanding of the molecular etiology of most complex diseases is still limited . Yet by combining weak signals from multiple genes , we may identify patterns that provide clinically significant insights into disease processes . We hypothesized that by examining the relationships between sets of genes related to specific developmental processes and reported disease genes , we could develop novel insights into developmental impacts on health . To test this hypothesis , we created a novel approach and tool to assess the overrepresentation of various developmental gene sets among groups of genes linked to specific diseases . Our approach derives its strength from combining signals of sets of genes and from pooling disease-gene links across disease subtypes using a hierarchical taxonomy of disease . We demonstrate that this pooling approach improves accuracy over a comparable enrichment-detection method without pooling . Our approach has the advantage of potentially generalizing incomplete disease gene data and overcoming variation in how genes are associated with specific disease terms , improving our ability to detect novel and interesting connections . We note that a similar principle - that of pooling many weak signals to provide a stronger one - has led to the creation of many highly effective “gene-set analysis” methods for expression data [7] , [8] and genome wide association data [9] . However , these approaches are inappropriate for assessing the overlap of disease-linked genes with genes involved in developmental pathways , because the members of our developmental gene sets cannot meaningfully be ranked by the strength of their participation in the set . Standard statistical enrichment methods such as the hypergeometric distribution might be more suitable , but their probabilities depend on inappropriate assumptions of gene independence [10] . Our approach avoids these problems . The choice of a disease taxonomy for this analysis is vitally important , yet most existing hierarchies lack the molecular focus inherent in the proposed analysis [11] . We chose the MeSH hierarchy of diseases ( category C ) because it is widely used , it is relatively compatible with our disease-gene databases , and it represents diseases multiple times within different parts of the tree , thus potentially including somewhat molecularly homogeneous groupings [12] . For example , type 1 diabetes mellitus appears multiple times in the taxonomy under categories corresponding to nutritional and metabolic diseases , endocrine disorders , and immune system diseases . The MeSH disease taxonomy can be represented as a “forest” of disease terms ( a collection of “trees , ” in the computational sense [13] ) , with 26 top-level categories ( Table S1 ) represented by “disease trees , ” and more specific disease terms located at increased tree depths . We derive our disease-gene links from two sources: OMIM , a curated collection of genes linked to human disease [14] , and the Genopedia data from the database of Human Genetic Epidemiology ( HuGE ) , whose disease-gene information is obtained primarily by computational literature curation , but includes manual review of both abstracts and index terms [15] . We then pool genes linked to descendants of a disease node in the MeSH trees , and we assess significance through permutation . Because of the current incomplete knowledge of gene-disease connections , enrichment of gene sets among genes linked to a specific disease node in the MeSH forest may not be detectable . By pooling gene links from related diseases , we are able to rescue some of these lost connections . For this study , we focus on identifying connections to genes involved in developmental processes . The gene sets chosen were based on Biological Process terms from the Gene Ontology ( GO ) , a hierarchically-organized collection of controlled-vocabulary functional annotation of genes and gene products [16] . However , given our interest specifically in developmental gene sets , we chose to use the gene sets from DFLAT , a prior collaboration of ours that aimed to expand human developmental annotation in the Gene Ontology framework [17] . Gene sets derived from the Gene Ontology that include the DFLAT annotation have been shown to improve the interpretability of gene expression data related to human development [18] , so they are a reasonable choice for the analysis described here . We refer to the developmental gene sets whose links to disease are being investigated as the query gene sets . Additional related work assesses significant enrichment of GO functional annotation terms in query gene sets using the directed-acyclic graph structure of the Gene Ontology . Such approaches adjust enrichment calculations by accounting for relationships between the genes at a given annotation node and those at the parent or child [19] , [20] . But these methods are concerned with a different problem - that of spurious enrichment at higher levels of the GO hierarchy . Instead , the hazard in our case is false negatives that occur because of the incomplete knowledge of disease genes and the variable levels of precision used to map known disease genes to the MeSH forest . We therefore focus here on query sets representing top-level developmental processes ( e . g . , “heart development” rather than “atrial cardiac muscle cell development” ) , because highly specific terms typically include very few genes , rendering gene-set analyses powerless . Future efforts will include drilling down into specific developmental pathways . Yet even at this high level , our analysis identifies both expected links and several unexpected ones , the latter leading to individual novel hypotheses about surprising molecular connections that may affect future disease research .
To identify significant connections between gene sets and disease , we used a novel method of assessing overlaps between disease genes and the designated query gene sets . We first created a computational representation of the MeSH disease taxonomy in which each node represents a MeSH disease concept . We extracted and combined gene-disease links from the HuGE Genopedia database and from OMIM , and mapped the resulting 119 , 400 gene-disease links to the MeSH forest ( see Methods ) . Taking advantage of the hierarchical representation of disease concepts in MeSH , we then created a version of the forest in which each disease node D contains any genes in the subtree rooted at D . For example , instead of identifying four lung development genes linked to neural tube defects , two to meningomyelocele , and three to spinal dysraphism , pooling them together identifies seven distinct lung development genes implicated in neural tube defects ( Figure 1 ) . For this study we considered nine DFLAT gene sets , broadly representing development in brain , bone , heart , kidney , liver , lung , nerve , blood vessels , and skin . We identified the overlaps between each of these gene sets and the disease genes at each node of our MeSH tree by counting the number of genes in both . ( Table S2 lists the query gene sets and their sizes . ) Assessing the significance of these overlaps must account for gene set sizes and multiple testing . However , such adjustment is non-trivial because of the complex dependencies between the tests . ( For example , any method that assumes the probability of enrichment at node D is independent of the probability of enrichment at D's parent or child is going to be wildly inaccurate . ) We therefore use a permutation test ( described in the Methods section ) to assess the significance of each observed overlap , given the number of genes in the query set and the disease-gene mappings in the MeSH forest . This test produces a p-value at each node estimating the probability of seeing an overlap of the observed size at that node by chance . Our hypothesis was that mapping disease genes to broader disease terms in the MeSH tree as described above would improve our power to detect actual enrichment by mitigating the effects of varying precision in gene annotation . However , it is also possible that pooling might lead to less-accurate results by incorrectly mapping genes to unrelated disease classes . Assessing which happens more frequently is challenging because the right answers are rarely known . Thus , to compare our pooling approach to a more traditional enrichment analysis , we performed the following experiment . The intuition behind this experiment is that disease classes that are correctly linked to the query gene set should be more likely to be supported by withheld data from the same query set . So we use support by withheld data as a rough way to approximate correctness . Our “pooling” approach computes the significance of the query gene set's enrichment at disease node D by pooling data from the genes in the subtree rooted at D . For fairness , we chose ( as the “traditional” method ) to assess significance of linkage using exactly the same random permutations of gene labels , but counting only the genes directly linked to disease node D ( rather than those linked to the node or any of its descendants ) . We note that the traditional method used here is really just a randomized approximation to the classical hypergeometric calculation , but one that maintains the correlation structure of genes between different diseases . We have separately computed the hypergeometric probabilities ( data not shown ) , and found them to give very similar overall results to those derived using permutation . Accordingly , we present just the permutation-based method , which is the most direct control for our pooling approach , in the comparison below . We withheld 100 randomly chosen links , each connecting a gene in the query gene set to a specific associated disease . We recomputed enrichment at each disease node without the withheld links , using both the pooling method and the traditional one . Counting then allows us to estimate the probability that a randomly-chosen node found to be more significant under the pooling approach than the traditional approach would be supported by a randomly withheld link , and , the probability that a node more significant by the traditional method would be . ( See Methods for further details . ) We repeated this experiment with a different set of 100 withheld links 100 times for each of the 9 developmental gene sets . Table 1 shows the average values of and for each of the development gene sets , and Figure 2 shows histograms of the distribution of - for all of the development gene sets . If is larger than , then the nodes that are more significant under the pooling approach tend to be more consistently supported by the withheld data , which is our proxy for correctness . In other words , when is larger , it suggests that the pooling method tends to make correct links appear more significant . For all nine query sets , we found that the averaged is greater than the averaged , suggesting that the pooling method is better able to identify true links between developmental gene sets and disease . While it is relatively easy to provide a list , for each developmental gene set , of MeSH terms whose gene set enrichment p-value is below some cutoff , interpreting those lists is complex . Because enrichment calculations are based on subtrees , there is important information available at different scales , ranging from high-level overviews of the MeSH disease forest to specific enriched gene-disease links , their significance scores , and the genes involved . For these results to lead to new discoveries , we must select from this large collection of significant links a few that are surprising yet plausible . Doing this requires a considerable amount of domain knowledge in molecular medicine . To facilitate data exploration by collaborators with such expertise , we developed a web-based tool that provides both an abstract and a detailed view of the associations ( available at http://gda . cs . tufts . edu/development ) . For a high-level overview , we visualize each disjoint hierarchy of disease terms ( i . e . , each tree of the MeSH disease forest ) in a simplified triangular form ( Figure 3 ) . Each significant disease association with the given gene set is represented as a dot in this triangle , whose color represents the degree of significance . This abstract view helps highlight the broad overall patterns of association between development gene sets and disease classes . Clicking on a particular disease subtree leads to a detailed tree view ( Figure 4 ) . The tree visualization is implemented using Cytoscape Web [21] . Color again corresponds to significance , with darker nodes indicating more significant enrichment of the developmental gene set in the disease genes associated with the subtree rooted at that node . For clarity , this view by default only displays disease nodes significantly associated with the query gene set ( and their ancestors in the chosen tree ) . However , users can adjust parameters to view the full tree if desired . Specific genes and p-values for individual links can be identified by selecting nodes in this view . The associated gene lists are easily selected and pasted into functional analysis tools for pathway identification . In the next two sections , we describe some results from our initial explorations using this tool . The first section provides a sanity-check by demonstrating that we find the broad patterns of connections that one would expect , while the next shows that we can use this approach and the tool described here to make novel but plausible discoveries with potential clinical impact . We first take a high-level view of all the results together . Generally speaking , one would expect to see connections between tissue-specific developmental gene sets and broad categories of diseases known to involve those particular tissues . For example , it seems likely that many cardiovascular disorders would be linked to a significant number of heart development genes . Figure 5 shows a heatmap of the relative “density” of disease terms significantly linked to each of the gene sets ( see Methods ) for several MeSH disease trees . We see high enrichment that essentially mirrors our expectations: bone development genes are over-represented in musculoskeletal disorders , brain development genes in nervous system disorders , heart development genes in cardiovascular disorders , etc . There are a few interesting exceptions . For example , the percentage of nervous system disorders significantly enriched for nerve development genes is relatively high , but not quite high as the percentage of musculoskeletal diseases enriched for nerve development genes . This seems to be in part an artifact of the large number of distinct nervous system disorders listed in MeSH category C despite having little or no molecular information , artificially decreasing the normalized density values ( the maximum density score in the C10 category is lower than the maximum score in any of the other MeSH disease trees shown in the figure ) . The root node of MeSH category C4 , “Neoplasms , ” is significantly associated ( ) with all of the developmental gene sets except for nerve and skin ( the two smallest of the gene sets and therefore the least likely to have significant overlaps ) . This observation reflects the fact that the regulation of cell growth and differentiation that comprise normal developmental processes are typically disrupted and dysregulated during the onset of malignancy [22] , [23] . A range of signaling proteins that play roles in directing both developmental processes and tumorigenesis are likely to blame for these interactions [24]–[26] . However , the specific signaling processes implicated in the different tumor types , as well as those known to be involved in developmental processes but not yet implicated in specific tumor types , may be of interest . Similarly , given that the query gene sets are all involved in developmental processes , it is not surprising that the C16 MeSH subtree , described as “Congenital , Hereditary , and Neonatal Diseases and Abnormalities , ” shows significant enrichment at the root node ( ) for all of the tested developmental gene sets . A wide range of molecular developmental processes are implicated in this MeSH category . The density measurement shown in Figure 5 provides a broader way of assessing a similar property . The density measure for the C16 tree is above average ( i . e . , the z-score normalized density metric is positive ) for each of the nine gene sets considered here . By confirming that we find expected and reasonable high-level results , the observations in this section provide evidence of the efficacy of our approach . Delving more closely into specific results , we identified several findings that seemed , at first glance , less predictable than those described above . Here we describe three such links . All of them identified surprising connections that , since our initial discovery of them using this approach , have been further supported by new publications . We have introduced a new approach that identifies significant overlap of gene sets with groups of related diseases in a hierarchical disease taxonomy . To evaluate this approach , we implemented a tool that allows users to explore connections between disease subtrees in MeSH and several developmental gene sets . Our observations in this analysis have helped identify surprising molecular connections between disparate processes . They have also more generally served to validate the approach of pooling incomplete information about disease genes across related disorders to strengthen our ability to identify such connections . With a growing interest in research into the developmental origins of adult disease , this resource should prove a valuable source of information for generating hypotheses about such connections at the molecular level . Our work has assumed only that query gene sets are lists of genes that share some common property [61] . However , for this study we have chosen query sets whose genes share common annotations in the Gene Ontology . An interesting future direction would be to consider the possibility of creating hierarchically-structured queries representing related query terms in the Gene Ontology's directed acyclic graph structure , while still looking for significant links to disease classes or subtrees in the MeSH forest . While our implementation relies on a particular set of disease-gene information and a small group of developmental gene sets , the power of the approach will be best exploited by the inclusion of a more comprehensive set of disease-associated genes . One key limitation of the current approach is due to the nature of the available data linking genes to diseases . OMIM is an excellent resource created largely by computer-assisted manual review of the literature [14] . However , it is limited in scope and is curated by locus rather than by disease , so that even identifying all genes related to , for example , type 2 diabetes , can be complicated . Conversely , the HuGE database , which provides the majority of the disease-gene data used in this project , derives most of its information from computational screening of PubMed ( along with some manual review ) [62] , [63] . This raises the possibility that , in addition to being incomplete , our gene-disease database may include a substantial number of false positives due not only to false-positive experimental results but also to inappropriate interpretation of the text . There is prior work on reducing the rate of false positives when mining such information from the literature [64] , and the HuGE database creators worked to assess and improve accuracy [63] , but any data set derived from computational literature analysis will always have this concern . On the other hand , the success of our initial analysis in identifying expected connections suggests that false positives are so far not interfering significantly with the use of this tool for discovery . Further improving the quality of the data and characterizing the impact of different types of noise on the results will be an important area to investigate in the future . Finally , we note that while there are many disease taxonomies that are widely used for different purposes , there is growing dissatisfaction with most of them , in part because of the lack of a molecular representation of disease relationships [11] . Analyses such as ours may , as the data improve over time , lead to better understanding of molecular disease relationships across the board . Such knowledge is an important prerequisite for developing a truly molecular taxonomy of disease . We therefore hope that this work may ultimately contribute to the development of a new , more molecular disease taxonomy that is well suited to support translational research in the genomic era .
We assembled a combined set of disease-gene links for 11 , 831 genes using 116 , 117 human gene-disease associations from the Genopedia compendium in the HuGE database of Human Genetic Epidemiology [62] and 4 , 813 gene-disease associations from the OMIM database [65] , both downloaded in November , 2013 . Genes from the Genopedia database were mapped to their corresponding disease concepts in the MeSH hierarchy of medical subject headings ( http://www . nlm . nih . gov/mesh/ ) , using the Unified Medical Language System ( UMLS ) [66] as a thesaurus to identify synonymous diseases . To find MeSH terms that best correspond to the OMIM phenotypes , we used the MEDIC merged disease vocabulary , an ongoing toxicogenomics effort to map OMIM disease terms into the MeSH disease hierarchy , downloaded from the Comparative Toxicogenomics Database [67] in November , 2013 . After removing one copy of the 1 , 530 duplicate associations found in both data sets , we were left with a total of 119 , 400 unique associations . We estimate the distribution of the expected number of shared genes between the query gene set and the genes associated with a disease under the null hypothesis that there is no meaningful relationship between the query gene set and the disease class . We do so by randomly choosing gene sets of the query-set size from among all the genes in our MeSH tree . This is equivalent to randomly permuting the labels of the genes in the data to determine whether or not they are in the query set . Such permutation leaves the gene-disease connections intact and maintains the complex correlation structure of genes between related diseases . Assuming that is the observed size of the real overlap at disease node ( i . e . , the number of genes in the query gene set that are linked to node ) , for each permuted query set we can then determine whether the number of genes at node in that random query set is larger than . We ran 10 , 000 permutations to compute a p-value at each node estimating the probability of seeing an overlap of the observed size at that node by chance . Density of enrichment was computed between the 9 query gene sets and the 26 top-level MeSH disease categories , each represented by its own tree . Because many diseases are represented multiple times at different places in each tree , we first created a listing of all the unique MeSH disease terms in each tree . If different instances of the same disease in the same tree had different p-values , they were averaged . We then compared the p-values to the chosen significance cutoff of 0 . 005 . The fraction of unique terms in the tree with lower significance was computed . This fraction represents the “density” of significant enrichment of the query gene set in the chosen MeSH category . To create the heatmap , we z-score normalized the densities across each row ( query gene set ) . To identify expected enrichment , we manually selected the 9 top-level MeSH disease categories thought to be most relevant to the 9 query gene sets ( or to many/all developmental gene sets , as in the case of C4 - neoplasms and C16 - congenital , hereditary , and neonatal diseases and disorders ) . We performed the following experiment to compare the accuracy of our proposed pooling approach to a comparable enrichment analysis using only the genes directly associated with a given disease term . To describe the experiment , we first introduce new terminology: Assume that we are discussing only a single , fixed query gene set . Let be the set of all gene-disease links in our combined database: gene is associated with disease . For any disease node in the MeSH forest , let be the permutation-based significance score for enrichment of the query gene set among genes in associated with that node using the traditional method ( only those genes directly linked to node ) . Similarly , let be the analogous score for node under the pooling approach . Then we will repeatedly randomly withhold some links from . Specifically , for the th random iteration , let be a randomly chosen set of 100 pairs from , such that is in the query gene set , and let We can then partition the disease nodes into those that are more significant under the pooling method ( in the th iteration ) and those that are more significant under the traditional method . Formally , let nodes , and let nodes . ( Note that in the many cases where , the nodes contribute to neither set . Many of these are either leaves , or nodes with no associated genes under either method . ) We say a node is supported by gene-disease link from if a node corresponding to appears in the subtree rooted at . We can then determine the probability that a node in the set or is supported by some link in . Let indicator function if node is supported by a link in , and 0 otherwise . Then the probability that a node in is supported by is defined asand is defined analogously , using Finally , we average over all random trials to compute the averages and that are reported in Table 1 . Figure 7 illustrates the process of calculating and with an example for the random trial . | Understanding the roles that genes involved in normal human development can play in disease processes is an important part of predicting disease risk and designing novel treatment approaches . In this study , we have identified classes of disease that are associated with a surprisingly large number of genes involved in any of several tissue-specific developmental processes . To do so , we developed a novel approach whose strength comes from pooling genetic information across related diseases , overcoming problems ordinarily posed by limited information about individual gene-disease relationships . We demonstrate the method's efficacy both by examining its ability to highlight connections between gene sets and disease classes that are known to be related , and by demonstrating that the approach recovers expected broad classes of connections , such as those between heart development and cardiovascular disorders . However , by examining unexpected connections in this data set , we are able to develop new understanding of some surprising disease relationships , such as the one between dementia and osteoporosis . Such connections may lead to a better overall understanding of the role of development in lifelong health , as well as to the design of new methods to treat a range of diseases . | [
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] | 2014 | Finding Novel Molecular Connections between Developmental Processes and Disease |
The last decade has witnessed important advances in our understanding of the genetics of pigmentation in European populations , but very little is known about the genes involved in skin pigmentation variation in East Asian populations . Here , we present the results of a study evaluating the association of 10 Single Nucleotide Polymorphisms ( SNPs ) located within 5 pigmentation candidate genes ( OCA2 , DCT , ADAM17 , ADAMTS20 , and TYRP1 ) with skin pigmentation measured quantitatively in a sample of individuals of East Asian ancestry living in Canada . We show that the non-synonymous polymorphism rs1800414 ( His615Arg ) located within the OCA2 gene is significantly associated with skin pigmentation in this sample . We replicated this result in an independent sample of Chinese individuals of Han ancestry . This polymorphism is characterized by a derived allele that is present at a high frequency in East Asian populations , but is absent in other population groups . In both samples , individuals with the derived G allele , which codes for the amino acid arginine , show lower melanin levels than those with the ancestral A allele , which codes for the amino acid histidine . An analysis of this non-synonymous polymorphism using several programs to predict potential functional effects provides additional support for the role of this SNP in skin pigmentation variation in East Asian populations . Our results are consistent with previous research indicating that evolution to lightly-pigmented skin occurred , at least in part , independently in Europe and East Asia .
The remarkable variation observed in skin , hair and iris pigmentation in human populations is the result of differences in the amount , type and distribution of the pigment melanin , which is synthesized by specialized cells known as melanocytes . Pigmentation is a complex trait , influenced by numerous genes and their interactions . The last decade has witnessed impressive advances in our understanding of the genetics of normal pigmentation variation , driven by functional studies , gene expression studies , studies in animal models , analyses of signatures of natural selection and candidate gene or genome-wide association studies . At least 11 genes are known to be associated with normal pigmentation variation: TYR , TYRP1 , OCA2/HERC2 , SLC45A2 , SLC24A5 , SLC24A4 , MC1R , ASIP , KITLG , IRF4 and TPCN2 [Reviewed in 1] , [2] . In European and related populations , a clear picture is emerging of the genetic and evolutionary processes associated with skin lightening . The most important genes involved are SLC24A5 , SLC45A2 and KITLG , which explain a large portion of the skin pigmentation differences observed between European and West African populations [3]–[6] . Other polymorphisms within the genes TYR , OCA2 , MC1R , ASIP and IRF4 are known to play a role in normal pigmentation variation in populations of European descent [7]–[13] . A recent genome-wide study reported that variants within the genes SLC24A5 , SLC45A2 and TYR are associated with skin pigmentation in a South Asian sample [14] . Interestingly , the genes SLC24A5 and SLC45A2 show an extremely unusual pattern of allele frequency distribution , with a derived allele near fixation in European populations , and the alternative ancestral allele fixed in other population groups [5] . These two genes show strong signatures of selection in European populations [15]–[19] , but not in other population groups . These observations indicate that evolution to lightly-pigmented skin happened , at least in part , independently in Europe and East Asia [5] , [16] . Unfortunately , while there have been important advances in our understanding of the genetics of pigmentation in European populations , very little is known about the genes involved in skin pigmentation variation in East Asian populations . There are also pigmentation candidate genes ( DCT , ADAM17 , ADAMTS20 , KITLG , TYRP1 and OCA2 ) that show signatures of selection in East Asians [16]–[21] , but formal association studies are required to confirm that these genes are involved in skin pigmentation variation . Studies of signatures of natural selection are extremely useful as a strategy to identify potential genes of interest , but it is critical to carry out further analysis in order to confirm that the signatures of selection are not false positives and to eliminate the possibility that positive selection was related to biological processes other than pigmentation [22] , [23] . For example , the ADAM17 gene has been implicated in many processes involved in cell-cell and cell-matrix interactions , including fertilization , muscle development and neurogenesis . Therefore , it is in principle possible that the signatures of selection observed in this gene are due to its role in these processes . Here , we present the results of a study evaluating the association of 10 Single Nucleotide Polymorphisms ( SNPs ) located within 5 pigmentation candidate genes ( OCA2 , DCT , ADAM17 , ADAMTS20 and TYRP1 ) with skin pigmentation measured quantitatively in a sample of individuals of East Asian ancestry living in Canada . We selected these loci based on previous studies that identified signatures of natural selection in East Asian populations , and prioritized a list of SNPs within these genes using the program SNPSelector . We show that the non-synonymous polymorphism rs1800414 located within the OCA2 gene is significantly associated with skin pigmentation in this East Asian sample . We replicated this result in an independent sample of Chinese individuals of Han ancestry . This polymorphism is characterized by a derived allele that is present at high frequency in East Asian populations , but is absent in other populations . In our sample , individuals with the derived G allele , which codes for the amino acid arginine , show lower melanin levels than those with the ancestral A allele , which codes for the amino acid histidine . An analysis of this non-synonymous polymorphism using several programs to predict potential functional effects ( see materials and methods ) provides additional support for the role of this SNP in skin pigmentation variation in East Asian populations .
We applied four tests of positive selection based on different statistics to the five genes analyzed in this study . For these tests , we used genomewide information available for the HapMap East Asian , European and African samples ( see Material and methods section ) . Table 1 shows the results of the four tests of natural selection in the HapMap sample . In accordance to previous reports [16]–[21] , we observed evidence of positive selection in East Asian populations for these pigmentation genes . The OCA2 gene shows numerous SNPs displaying high levels of differentiation in the East Asian sample with respect to the genomewide average ( LSBL tests ) , very negative Tajima's D values for two windows encompassing a portion of this gene and a reduction in genetic diversity . We also observed clusters of markers exhibiting high differentiation for the DCT gene , as well as evidence of a reduction of genetic diversity in the East Asian sample for this locus ( lnRH test ) . The ADAM17 gene is significant for the LSBL , lnRH and Tajima's D tests , and is also significant for the WGLRH test , indicating that ADAM17 has haplotypes characterized by derived alleles that have risen to very high frequencies and have longer than expected levels of Linkage Disequilibrium ( LD ) . The gene ADAMTS20 has extreme values for the LSBL and Tajima's D statistics . Finally , markers in the gene TYRP1 show high levels of genetic differentiation between East Asians and the other two HapMap populations measured by LSBL and this locus is encompassed by a significant extended haplotype region ( WGLRH test ) . Ten polymorphisms located within these five genes were genotyped in a sample of individuals of East Asian ancestry ( N = 122 ) . Table 2 reports the genotype and allele frequencies for each marker . No significant deviations from Hardy-Weinberg proportions were identified for any of the SNPs . We evaluated the patterns of LD between the markers located in each gene using an Expectation Maximization algorithm implemented in the program EMLD . LD was low between the markers located within the OCA2 gene ( rs7495174/rs1800414: r2 = 0 . 06; rs7495174/rs1545397: r2 = 0 . 05 and rs1800414/rs1545397: r2 = 0 . 25 ) . In contrast , there was perfect LD between the markers located within the DCT gene ( rs1407995/rs2031526: r2 = 1 ) . Finally , within the ADAMTS20 gene , there was almost perfect LD between the markers rs11182091 and rs11182085 ( r2>0 . 99 ) , but LD was substantially lower between rs11182091 and rs1510523 ( r2 = 0 . 30 ) and rs11182085 and rs1510523 ( r2 = 0 . 31 ) . We tested if there was evidence of association between the 10 SNPs and quantitative measures of constitutive pigmentation ( melanin index ) in the East Asian sample . The results of the linear regression analysis for each marker , including sex as a covariate are depicted in Table 3 . The rs1800414 polymorphism located within the OCA2 gene showed a significant association with skin pigmentation . Using an additive model , we estimated that each copy of the G allele decreases skin pigmentation by approximately 1 . 3 melanin units ( p = 0 . 002 ) and the rs1800414 polymorphism explains approximately 9% of the pigmentation variation observed in this sample . A model-free ( unconstricted ) analysis indicates that AG heterozygotes decrease skin pigmentation by 1 . 6 melanin units ( p = 0 . 046 ) and GG homozygotes by 2 . 6 melanin units ( p = 0 . 002 ) , with respect to AA homozygotes . The marker rs1800414 remains significant when using the conservative Bonferroni correction ( taking into account the intermarker LD patterns and assuming 8 independent tests , the p-value after correction is p = 0 . 016 ) . Figure 1 shows the distribution of melanin index value by rs1800414 genotype . No association was observed for the other 9 SNPs analyzed in this study . In order to confirm the results of this study , we genotyped the rs1800414 polymorphism in an independent sample of Chinese individuals of Han ancestry recruited in China ( N = 207 ) and measured with a similar instrument . The results of this analysis are reported in Table 4 . In agreement with our preliminary results , the linear regression analysis shows that rs1800414 has a significant effect on skin pigmentation , although the effect size is slightly lower than in our study . Under an additive model , each copy of the G allele decreases skin pigmentation by 0 . 85 melanin units ( p = 0 . 005 ) and explains around 4% of the variation observed in the sample . Under a model-free ( unconstrained ) model , the AG heterozygotes decrease skin pigmentation by approximately 1 melanin unit ( p = 0 . 085 ) and the GG homozygotes by 1 . 7 melanin units ( p = 0 . 005 ) , with respect to AA homozygotes .
We analyzed the association of 10 SNPs within 5 pigmentation candidate genes ( OCA2 , DCT , ADAM17 , ADAMTS20 and TYRP1 ) with skin melanin content measured quantitatively in an East Asian sample . Previous studies have indicated that these 5 genes show signatures of natural selection in East Asian populations [16]–[20] and our analysis of signatures of selection using data obtained with the Affymetrix 6 . 0 chip showed a remarkable agreement with these studies . The 10 SNPs selected for analysis showed high allele frequency differences between East Asian and non-Asian populations and 6 of them ( rs1800414 , rs7495174 , rs1182091 , rs1510523 , rs11182085 and rs2075509 ) also had high function , regulatory or phastcons scores in SNPSelector , which indicated that these SNPs could be of functional importance . We observed that one of the markers included in the study , the non-synonymous SNP rs1800414 ( His615Arg ) located within the OCA2 gene , was significantly associated with melanin index in our sample of Canadian individuals of East Asian ancestry ( p = 0 . 002 ) . An analysis in an independent sample of Chinese individuals of Han ancestry also showed that the His615Arg polymorphism has a significant effect on skin pigmentation ( p = 0 . 005 ) . Based on both samples , it can be estimated that each copy of the derived G allele ( coding for the amino acid Arginine ) , which is present at high frequency in East Asian populations , but absent in European and West African populations , decreases skin pigmentation by 0 . 85–1 . 3 melanin units . Additionally , the unconstrained statistical analysis shows that in terms of its effects on skin pigmentation , this polymorphism fits a codominant model of inheritance , rather than dominant or recessive models . Although significant , the phenotypic effect observed for rs1800414 is lower than the effect that has been reported for other polymorphisms previously associated with skin pigmentation . For example , studies in African American populations have shown that polymorphisms located within the pigmentation genes SLC24A5 , SLC45A2 and KITLG have an effect of more than 3 melanin units per allele copy [3] , [5] , [6] . However , direct comparison between studies is complicated by the different pigmentation characteristics of the samples . This is one of the first formal reports of association with skin pigmentation measured using reflectometry in East Asian populations . Our study indicates that the OCA2 gene was independently involved in the evolution of light pigmentation in Europe and East Asia , and in combination with previous findings for other genes ( SLC24A5 and SLC45A2 ) , strongly suggests that there was convergent evolution towards light pigmentation in Europe and East Asia . Previous studies reported that the OCA2/HERC2 gene showed distinct signatures of positive selection in Europe and East Asia [16] , [19] , [20] , [21] . Markers in the HERC2 gene are associated with blue eyes in European and related populations . In particular , the SNP rs12913832 segregates almost perfectly with blue-brown eye color [24]–[26] . This SNP is located within a highly conserved region that may act as a control region for OCA2 and a recent study reported that rs12913832 had a significant effect on the levels of OCA2 mRNA [25] , [27] . Lao et al . [19] reported that the OCA2 gene had significant Extended Haplotype Homozygosity ( EHH ) values in European and East Asian samples , but the core haplotypes were different in both populations . Yuasa et al . [28] noted that the rs1800414 G allele ( R615 ) is very frequent in East Asian populations , but rare or absent in African and Indo-European populations . Anno et al . [29] also showed that European and East Asian populations are characterized by different haplotypes at the OCA2 gene , with the East Asian haplotype harboring the variant rs1800414 G , which is the allele that is associated with light skin in our study . More recently , Donelly et al . [21] described that the rs1800414 allele is under selection in East Asia , and the blue eye allele BEH2 ( defined by rs12913832 ) is under selection in Europe and Southwest Asia . Therefore , it seems clear that there were independent selective processes acting on the OCA2 gene in Europe and East Asia , involving distinct haplotypes . Our sample comprises individuals of East Asian ancestry living in Toronto . The majority of the subjects have ancestry from China , South Korea and Japan ( N = 96 ) , but some individuals have ancestry from Southeast Asia ( Vietnam , Thailand and Phillipines , N = 26 ) . If there are large differences in frequency between East Asian populations for rs1800414 , our significant association results for this SNP could be confounded by population stratification . However , two observations indicate that this is not the case: 1/ There are no significant deviations from Hardy-Weinberg proportions for any of the markers included in our study ( Table 2 ) . The effect of allele frequency differences between East Asian populations would have been reflected in deviations from Hardy-Weinberg ( excess of homozygotes , Wahlund effect ) . In fact , there is a slight excess of heterozygotes for rs1800414 in our total sample , which is the opposite of what would be expected in the presence of stratification , 2/ The statistical analysis excluding the Southeast Asian subjects ( N = 96 ) is also significant and shows remarkable concordance with the results obtained using the full sample ( beta = −1 . 6 , p = 0 . 001 ) . In this respect , it is important to note that Yuasa [28] reported that there are no large frequency differences between five samples from China and Japan for the rs1800414 G allele ( 44 . 8%–63% ) . Similarly , we did not observe significant allele frequency differences between the Canadian East Asian sample and the Chinese sample that was used for replication ( p = 0 . 255 ) . Our statistical analysis was significant for rs1800414 , but not for the other SNPs genotyped in this sample , including 2 additional SNPs within the OCA2 gene . Given our relatively small sample size , our study was not adequately powered to identify loci with small effects . The rs1800414 polymorphism explains a substantial proportion of the skin pigmentation variation observed in the sample . A relevant question is if the observed effects are due to rs1800414 or to a causative SNP in LD with rs1800414 within the OCA2 gene . In this sense , there is strong evidence pointing to rs1800414 as the causative variant itself . In addition to SNPSelector , we used other tools to infer the functional effect of this polymorphism ( FastSNP , the SNP function portal , SIFT and Polyphen ) . All of these methods suggest that this non-synonymous rs1800414 SNP , which was first described by Lee et al . [30] , is functionally important . FastSNP indicates that the functional effect of this SNP may be mediated through the regulation of alternative splicing . The programs Polyphen and SIFT also point to a damaging effect of the A to G transition at rs1800414 . It would be extremely important to carry out gene expression studies of this polymorphism , similar to the research published for other variants known to be associated with skin pigmentation using primary cultures of human melanocytes [2] , [27] , [31] . Our study provides new evidence regarding the genetic and evolutionary processes driving the lightening of skin following the migration of anatomically modern humans from Africa to high latitude regions in Europe and East Asia . Evidence is growing that the reduction in melanin content took place , at least in part , independently in these two regions . We now know that the evolution of skin pigmentation has been quite complex: some genes were the target of positive selection only in one population group ( eg . SLC24A5 and SLC45A2 in Europe ) , whereas other genes were under selection independently in more than one group ( eg . OCA2 in Europe and East Asia ) . However , there are still many aspects of the evolution of skin pigmentation that remain unclear . Our picture of the genetics of normal pigmentation variation in non-European populations is still incomplete , and the evolutionary time frame remains to be elucidated . When did the evolution to light skin take place in Europe and East Asia ? It has been suggested , based on evidence collected for the SLC24A5 gene , that the evolution to light skin occurred in Europe long after the arrival of anatomically modern humans to this continent [32] , but it will be necessary to collect information on additional genes and from different geographic regions to gain a better understanding of the evolution of skin pigmentation in human populations .
Written informed consent was obtained from each participant , and the study was approved by the University of Toronto Health Sciences Research Ethics Board . Participants were recruited by the Molecular Anthropology Laboratory at the University of Toronto Mississauga ( UTM ) between 2007 and 2009 . Recruitment took place primarily through the use of advertisements on UTM campus , and online advertisements in the University of Toronto community . Geographic origin was assessed using questions regarding the participant's place of birth and the ancestry of their parents and maternal and paternal grandparents . In total , 122 East Asians were recruited . We took quantitative melanin measurements from each participant's inner arm using a narrow-band reflectometer ( DermaSpectrometer , Cortex Technology , Hadsund , Denmark ) . This instrument emits light at the green ( 568 nm ) and red ( 655 nm ) wavelengths of the visible spectrum and a photodetector measures the amount of light reflected by the skin . These measurements are used to estimate the melanin content in the skin , which is expressed as the Melanin Index ( M ) . In human populations , the melanin index ranges from the low 20s ( individuals with light skin ) to close to 100 ( individuals with dark skin ) . Throughout the text , when we refer to melanin units , we refer to the melanin index values obtained with the DermaSpectrometer . More information about this instrument is available in Shriver and Parra [33] . In order to capture the most accurate reading of constitutive skin pigmentation , these measurements were carried out during the winter . We used SNPSelector [http://snpselector . duhs . duke . edu/hqsnp36 . html] to prioritize a limited number of SNPs to genotype within each of the pigmentation candidate genes . SNPSelector is a SNP selection tool that provides information on population allele frequencies , linkage disequilibrium patterns , potential SNP function and patterns of SNP conservation . Our criteria for SNP selection was based on: 1/ high frequency differences between East Asian and non-Asian populations ( West Africa and Europe ) and 2/ potential functional effect , based on the function score , regulatory score or conservation score ( PhastCons score ) . The following SNPs were selected for genotyping: 1/ Gene OCA2: rs1800414 is a non-synonymous polymorphism with an allele present at high frequency in East Asian populations ( G allele , 59% ) but absent in non-Asian populations . This SNP also had very high function , regulatory and phastcons scores; rs1545397 is an intronic polymorphism showing dramatic allele frequency differences between East Asian and non-Asian populations ( >85% ) ; rs7495174 is an intronic variant showing high frequency differences between East Asian and non-Asian populations ( >45% ) and a high regulatory score ( CpG island ) , 2/ Gene DCT: rs1407995 and rs2031526 are intronic polymorphisms showing very high frequency differences between East Asian and non-Asian populations ( >60% ) , 3/Gene ADAM17: rs4328603 is an intronic SNP showing very high frequency differences between East Asian and non-Asian populations ( >60% ) , 4/ Gene ADAMTS20: rs11182091 is an intronic SNP with substantial frequency differences between East Asian and non-Asian populations ( >30% ) and a high regulatory score ( conserved transcription factor binding site ) ; rs1510523 is an intronic variant with high frequency differences between East Asian and non-Asian populations ( >40% ) and high regulatory and phastcons scores; rs11182085 is an intronic SNP with substantial frequency differences between East Asian and non-Asian populations ( >30% ) and high regulatory and phastcons scores , 5/ Gene TYRP1: rs2075509 is an intronic variant with high frequency differences between East Asian and non-Asian populations and high regulatory and phastcons scores . No markers were studied at the KITLG gene because no SNPs were identified with large frequency differences between East Asian and European populations . This is consistent with reports indicating that the signatures of selection observed in KITLG region are shared in Europeans and East Asians [6] . A sample of each participant's blood was collected in a 4-mL EDTA tube . DNA was extracted from the blood using the E . Z . N . A . Blood DNA Midi Kit ( Omega Bio-Tek , Georgia , United States ) . Genotyping was done by the company KBiosciences [http://www . kbioscience . co . uk/] using a KASPar assay that relies on competitive allele specific PCR and fluorescent detection . Eighty-nine genotypes were characterized in duplicate , and the concordance rate between the samples and the blind duplicates was 100% . Departures from Hardy-Weinberg proportions were evaluated using an exact test available at the website http://ihg2 . helmholtz-muenchen . de/cgi-bin/hw/hwa1 . pl . Linkage disequilibrium ( LD ) between the markers located within the same genes was estimated using the program EMLD ( University of Texas , Houston , TX ) . LD is reported as the r2 value . Association between the selected SNPs and Melanin Index was tested using linear regression . Sex was included as a covariate , as it has been found to be associated with skin pigmentation in previous studies [34] , [35] . Each of the 10 SNPs was tested independently using additive and unconstrained models . The regression analysis was carried out with the program SPSS ( version 17 . 0 , SPSS Inc . , 2008 ) . We used the program Quanto [http://hydra . usc . edu/gxe/] to estimate the statistical Power of our study using an additive model and a range of allele frequencies and allelic effects ( measured as the regression coefficient-beta ) . These estimates are based on the distribution of melanin levels observed in the East Asian sample ( mean melanin index = 31 , standard deviation = 3 ) and a sample size of 120 individuals . For markers with intermediate allele frequencies ( 35%–65% ) , our study has more than 90% power to detect effects higher than 1 . 3 melanin units ( type I error rate = 0 . 05 , two-sided test ) . The Power drops for markers with more extreme frequencies: for a marker with 20% frequency , the power to identify effects higher than 1 . 3 is 77 . 8% and for a marker with 10% frequency , it is 52 . 5% . For replication of the significant results of the initial analysis , the OCA2 His615Arg polymorphism was genotyped by sequencing in an independent sample from China . The sample comprised 207 individuals of Han ancestry that were recruited by Professor Li Jin at Fudan University . Skin pigmentation was measured in the inner upper arm with an instrument similar to that used to measure pigmentation in the Canadian East Asian samples ( DermaSpectrometer , Cortex Technology , Hadsund , Denmark ) . Informed consent was obtained from each participant , and the project approved by the research ethics board of the School of Life Sciences , Fudan University . Four different tests of selection were used to evaluate evidence of positive selection in the HapMap East Asian sample for the five genes analyzed in this study . They include the locus-specific branch length ( LSBL ) , the log of the ratio of heterozygosities ( lnRH ) , Tajima's D , and whole genome long range haplotype ( WGLRH ) test [36]–[39] . The results reported here are based on genome-wide data for the East Asian , European and West African HapMap samples obtained with the Affymetrix 6 . 0 chip , which includes approximately 1 million SNPs . The LSBL test evaluates if genetic markers within a genomic region show unusual levels of differentiation with respect to the genome average . This test apportions the genetic variation observed in East Asian , European and West African populations for each SNP , and identifies markers with high levels of genetic differentiation in the East Asian sample . The lnRH test highlights genomic regions with low levels of genetic diversity in the population of interest , in comparison with other population groups . This statistic was calculated for a two-way population comparison between East Asians and Europeans , and East Asians and West Africans , using an overlapping sliding window size of 100 , 000 base pairs ( bp ) and moving in 25 , 000 bp increments along a chromosome . Regions of the genome with negative Tajima's D values are also a hallmark of positive selection . However , negative values of D can result from demographic events as well , specifically the recovery from a population bottleneck . For this reason , it is important to compare local values of Tajima's D with the empirical levels observed in the genome . As for the lnRH analysis , Tajima's D was calculated for each population using an overlapping sliding window size of 100 , 000 bp with a 25 , 000 bp offset . The statistical significance for each of the LSBL , lnRH , and Tajima's D statistics was based on the genome-wide empirical distribution , using the formula PE ( x ) = ( number of loci>x ) / ( total number loci ) . The final test used to infer selection is the WGLRH test of Zhang et al . [37] . This test first calculates the Relative Extended Haplotype Homozygosity ( REHH ) for each core haplotype in the data set and identifies core haplotypes with longer than expected ranges of linkage disequilibrium ( LD ) given their frequency in the population . A gamma distribution is then estimated using maximum likelihood methods against which the REHH of each core haplotype is tested to determine if its respective p-value is suggestive of recent , positive selection . This test then considers the ancestral state of the alleles , determined by a closely related outgroup , to identify SNPs where the derived allele has risen to high frequencies ( >0 . 60 ) . For this data set , the ancestral state for all SNPs available in the chimpanzee sequence was retrieved using the UCSC genome browser . In total , the ancestral states for 846 , 032 SNPS on the autosomes and X chromosome were obtained . Lastly , the WGLRH test applies a false discovery rate approach to control for false positives and identifies significant extended haplotypes . The four statistics used in this analysis have been described in more detail in Bigham et al . [40] . We analyzed the OCA2 His615Arg polymorphism using the programs FastSNP ( http://fastsnp . ibms . sinica . edu . tw/pages/input_CandidateGeneSearch . jsp ) , the SNP function portal ( http://brainarray . mbni . med . umich . edu/Brainarray/Database/SearchSNP/snpfunc . aspx ) and SIFT ( http://sift . jcvi . org/ ) in order to predict potential functional effects . | Our knowledge of the genetic basis of normal pigmentation variation in human populations is quite incomplete . Recent studies have identified some of the genes responsible for the reduction in melanin content in European populations , but this is not the case for other population groups , such as East Asians . Here , we report that a genetic variant located within the gene OCA2 ( rs1800414 ) is associated with skin pigmentation in two samples of East Asian ancestry . The allele associated with lower melanin levels is found at high frequencies in East Asian populations , but is absent or at very low frequencies in other population groups . This is one of the first reports of association of genetic markers with quantitative measures of pigmentation in East Asian populations and it confirms previous evidence indicating that evolution towards light skin occurred , at least in part , independently in Europe and East Asia . The OCA2 gene has been under positive selection in Europe and East Asia , but different alleles have been selected in each region . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"evolutionary",
"biology/human",
"evolution",
"genetics",
"and",
"genomics/population",
"genetics"
] | 2010 | Association of the OCA2 Polymorphism His615Arg with Melanin Content in East Asian Populations: Further Evidence of Convergent Evolution of Skin Pigmentation |
Synaptic plasticity is thought to induce memory traces in the brain that are the foundation of learning . To ensure the stability of these traces in the presence of further learning , however , a regulation of plasticity appears beneficial . Here , we take up the recent suggestion that dendritic inhibition can switch plasticity of excitatory synapses on and off by gating backpropagating action potentials ( bAPs ) and calcium spikes , i . e . , by gating the coincidence signals required for Hebbian forms of plasticity . We analyze temporal and spatial constraints of such a gating and investigate whether it is possible to suppress bAPs without a simultaneous annihilation of the forward-directed information flow via excitatory postsynaptic potentials ( EPSPs ) . In a computational analysis of conductance-based multi-compartmental models , we demonstrate that a robust control of bAPs and calcium spikes is possible in an all-or-none manner , enabling a binary switch of coincidence signals and plasticity . The position of inhibitory synapses on the dendritic tree determines the spatial extent of the effect and allows a pathway-specific regulation of plasticity . With appropriate timing , EPSPs can still trigger somatic action potentials , although backpropagating signals are abolished . An annihilation of bAPs requires precisely timed inhibition , while the timing constraints are less stringent for distal calcium spikes . We further show that a wide-spread motif of local circuits—feedforward inhibition—is well suited to provide the temporal precision needed for the control of bAPs . Altogether , our model provides experimentally testable predictions and demonstrates that the inhibitory switch of plasticity can be a robust and attractive mechanism , hence assigning an additional function to the inhibitory elements of neuronal microcircuits beyond modulation of excitability .
To successfully interact with our environment , we need to adjust to new or changing conditions . It is widely accepted that this ability involves alterations of synaptic connections in the brain , so-called synaptic plasticity [1] . While synaptic plasticity fulfills key requirements for the incorporation of new knowledge and memories into neural circuits , it also introduces the risk of changing connections that are essential for previously stored information , a problem termed plasticity-stability dilemma [2 , 3] . Hence , a mechanism to selectively switch plasticity on or off would be useful . In this context , inhibition has been proposed as a means to regulate plasticity [4–6] . GABAergic interneurons that target the dendrites of pyramidal cells may control backpropagation of action potentials to excitatory synapses and hence the coincidence signal required for Hebbian forms of synaptic plasticity . Alternatively , such inhibition could affect the NMDA-receptor-mediated component of excitatory postsynaptic potentials ( EPSPs ) [5 , 7] . Indeed , recent experimental work has shown that inhibitory dendritic synapses can weaken the backpropagating action potential ( bAP ) in the dendrite of pyramidal cells so that calcium signals required for the plasticity of excitatory synapses are reduced [8] . These findings provide strong support for a crucial role of inhibition as a regulator of plasticity , in particular as direct stimulation of inhibitory neurons leads to a cancellation of bAPs in pyramidal cells . For an effective switch in plasticity , however , it needs to be ensured that while the dendritic inhibition cancels the bAP , the forward-directed EPSP ( that is meant to cause the bAP in the first place ) still reaches the soma and orthodromic information flow is preserved . Whether a separation of the effect of inhibition on EPSP and bAP is possible on physiological timescales is currently unclear and needs to be explored in view of the well-known efficiency of ‘on-path’ inhibition in impairing passive EPSPs in the dendrite [9] . We here address this question as well as identify the temporal and spatial constraints that are required to reliably modulate plasticity of excitatory synapses in pyramidal cells . To this end , mathematical modeling suggests itself , because it allows to systematically vary both the strength and dendritic location of inhibition and to monitor bAPs and calcium spikes in the whole dendrite with high temporal and spatial resolution . We hence adapted a multi-compartmental model of pyramidal cells to reproduce a number of key electrophysiological characteristics . These included a realistic decrease of the amplitude of bAPs along the dendritic tree [10] , characteristics of bAP-activated calcium spike ( BAC ) firing [11] , and the generation of distal calcium spikes at a critical frequency of somatic stimulation [12] . Using this model , we identify conditions under which a modulation of bAPs and calcium spikes is possible and does not impair the forward-directed information flow to the soma . We demonstrate that shunting inhibition gates bAPs and calcium spikes in an all-or-none manner , laying the foundation for a binary switch of plasticity of excitatory synapses . Further , we identify the timing constraints for an efficient inhibitory gating of bAPs and calcium spikes . Implementing an additive spike-timing-dependent plasticity ( STDP ) rule [13] , we find that plasticity of excitatory synapses is indeed switched as a consequence of the effects of inhibition on the coincidence signal . Depending on the site of inhibition , this effect can be more global or constrained to smaller dendritic compartments and hence also distinct pathways converging onto pyramidal neurons . We observe that timing constraints for the gating of bAPs are relatively strict in that inhibition has to arrive with a precision of ∼1 ms . Local gating of distal calcium spikes , however , can be achieved within a wider time window of several milliseconds . Additionally , calcium spikes are more sensitive to inhibition in that smaller conductances suffice to abolish them . Importantly , with appropriate timing of inhibition , forward-directed EPSPs can still drive somatic firing while Hebbian coincidence signals are canceled . Finally , we suggest that a common circuit motif of feedforward inhibition can ensure the appropriate timing required for the plasticity switch by inhibition . Our model study provides testable experimental predictions and strengthens the view that the functional role of interneurons includes a pathway-specific regulation of plasticity , in addition to the widely studied regulation of excitability and information flow in local networks [14] .
First , we sought to understand the effect of local dendritic inhibition on bAPs . To this end , we placed a shunting inhibitory input on the proximal apical dendrite , systematically varied its strength , and monitored the amplitude of a somatically-induced bAP along the apical dendrite . For weak inhibition , the bAP amplitude was reduced around the dendritic location of the inhibitory input , but recovered to its full amplitude as the bAP propagated down the dendrite . In contrast , strong inhibition barred the bAP from invading the dendrite ( Fig 2A ) . The transition from a fully intact bAP to a complete bAP failure occurred abruptly at a critical inhibitory conductance ( Fig 2B ) . This all-or-none behavior was observed for inhibition at different locations along the shaft of the apical dendrite , albeit with different critical amounts of inhibition ( Fig 2B ) . From the perspective of a synapse further up in the dendrite , the bAP was either fully intact or completely canceled . Fig 2 shows the case for a synapse on the oblique dendrite , which is taken as a representative for one excitatory pathway subject to bAP-dependent STDP throughout the paper . The same effect could , however , also be observed for propagation into the apical tuft . The amount of inhibition required to cancel a bAP appeared to be in a realistic range . For example , when inhibition was placed at 90 μm from the soma ( Fig 2A and 2B ) , the critical inhibitory conductance was slightly above 25 nS , corresponding to about 25 co-activated inhibitory synapses of 1 nS [24] . Such inhibition could be provided by about 2–3 co-activated interneurons , given that interneurons form up to a dozen contacts onto a single pyramidal cell [25] . We checked that the core observations of the effects of inhibition onto bAPs were not altered when shunting synaptic inputs were distributed along the dendrite ( S1 Fig ) . A moderate temporal jitter ( on the order of 1 ms ) in the activation of inhibitory synapses also did not abolish the all-or-none behavior , but when a critical amount of inhibition was reached too early , the neuron was prevented from spiking . Overall , however , timing of inhibition needed to be relatively precise , as we will outline in the following section , where we first present data on the compartment-specificity of the effects . Due to its relatively local effect , shunting inhibition can control dendritic signals in a manner that is distinct between compartments , such as basal and apical dendrites . We hence investigated the effect of inhibition in different compartments ( i . e . the basal , oblique and distal apical dendrites , as well as the soma/axon ) , on the bAPs and calcium spikes , in the same and other compartments . While many neurons exhibit bAPs , some neuron types ( such as cortical layer 5 and CA1 pyramidal neurons ) can additionally generate distal calcium spikes that trigger BAC firing . Because calcium spikes can also serve as plasticity signals [26] , we analyzed the effects of inhibition on bAPs and calcium spikes during this firing mode . To trigger calcium spikes in the distal dendrite , we here combined somatic current injection in our model with coincident depolarization in the distal dendrite . In the control situation without inhibition ( Fig 4A–a ) , a calcium spike was triggered in the distal dendrite and the resulting BAC-firing-induced bursts of APs could be observed in the other compartments . Calcium spikes ( and the accompanying burst of APs ) proved highly sensitive to inhibitory inputs in the distal part of the apical dendrite ( Fig 4A–bI ) . In contrast , the bAP was relatively robust to distal inhibition and readily invaded both the apical oblique and basal dendrites despite the presence of distal inhibition ( Fig 4A–bII/III ) . In the basal dendrite , inhibition was not sufficient to cancel all BAC firing related bAPs , because the single GABAA conductance decayed too fast , and hence could not exert an influence on later bAPs ( Fig 4A–dIII ) . bAPs in the basal dendrite could , however , be controlled by inhibition that outlasted the burst . Such inhibition could be mediated by inhibitory synapses of the same dynamics but an activation that is distributed in time ( as it could be provided by bursting interneurons ) . Along these lines , four temporally spread out inhibitory inputs were able to cancel the bAPs in the basal dendrite ( Fig 4A–eIII ) . The quantitative dependence of this effect on frequency and number of such repetitive inhibitory inputs is shown in S2 Fig . Altogether , we have seen that , in principle , inhibition can cancel coincidence signals in a manner that is selective between compartments . To demonstrate the robustness of our results with respect to morphological and physiological detail , we replicated the main findings in an anatomically reconstructed L5 neuron model with different physiology [23] ( Fig 5 ) . To demonstrate that cancelation of the coincidence signals indeed results in the anticipated changes to synaptic plasticity , we next subjected the model cell to a classical plasticity paradigm . Contemporary biophysical and phenomenological models of STDP [13 , 27–29] depend on postsynaptic variables such as depolarization or calcium concentration , which are in turn shaped by bAPs and calcium spikes . Because the latter can be abolished by properly timed and placed inhibition , we suggest that cancellation of these signals via inhibition will lead to a noticeable change in the predicted learning rule ( or abolish plasticity altogether ) . To demonstrate this , we simulated a typical STDP pairing protocol ( see , for example , [30 , 31] ) . Somatic current injection was paired with excitatory synaptic activation on either basal , oblique or distal dendrites with a time delay Δt . Excitatory synaptic plasticity was Hebbian with a positive weight change for positive timings between the activation of the synapse and the arrival of a depolarizing postsynaptic potential and a negative weight change for negative timings ( for details see Methods ) . We implemented the simple additive rule by [13] , leading to the classical asymmetric STDP window ( Fig 6 ) . While the learning windows for oblique and basal synapses resembled the classical one ( Fig 6II/III ) , we found the distal learning window to be more symmetric ( Fig 6I ) . The latter results from the assumption that at distal synapses a calcium spike serves as the signal for plasticity . Because a calcium spike required the coincidence of bAP and EPSP , a calcium spike could only occur ( see Methods ) after presynaptic activation , such that the synaptic change was positive unless the relative timing of bAP and EPSP did not lead to a calcium spike ( in which case it was zero ) . As expected , inhibitory cancellation of bAPs and calcium spikes in the dendrite resulted in flat learning windows , corresponding to zero synaptic change ( compare Figs 3A , 4A and 6 ) . Because of the all-or-none effect of inhibition on dendritic signals , we predict a switch-like effect on learning for any plasticity rule relying on the coincidence of pre- and postsynaptic spiking . Above , we have shown that the modulation of the bAP requires inhibition to fall into a small time window closely tied to the initiation of the somatic action potential in the pyramidal neuron . The question arises whether such timing is realistic and how it can be achieved . As a proof of principle , we here show that the common local circuit motif of feedforward inhibition [32] is a good candidate to provide the appropriate timing . In feedforward inhibition an excitatory signal is passed on to a pyramidal neuron along two parallel pathways: one direct excitatory pathway to the pyramidal cell and one indirect pathway , where the signal first excites an inhibitory neuron that sends its output on to the pyramidal cell ( see Fig 7A , black parts of the schematic representation ) . From the perspective of the pyramidal neuron , excitation in this circuit arrives first to be followed by a delayed inhibition [33] . Embedding our pyramidal cell model into this circuit we found that inhibition arriving 2 ms after the EPSP was suited to control the bAP signal ( Fig 7B , Δt = 2 ms ) . This delay matched experimentally measured inhibition delays well [33 , 34] and was suited to control backpropagation without affecting forward-directed signal flow responsible for the initiation of postsynaptic somatic spikes . Interestingly , interneuron dynamics play a role for the timing requirements . Dependent on interneuron type , spike latencies and firing frequencies can vary . The size of the temporal window where inhibition can cancel bAPs ( without canceling EPSPs ) tends to be smaller for proximal inhibitory synapses than more distal synapses , as outlined above . One may hence speculate that the dynamics of interneurons innervating proximal parts of the dendritic tree should be sufficiently fast . To highlight the role of interneuron dynamics , we probed the circuit with two interneurons differing in their spiking dynamics . We used the interneuron model from the previous paragraph and compared its performance in the circuit to that of a second interneuron with slower dynamics . The latter was implemented by artificially slowing down the opening and closing rates of the interneuron’s sodium channels . In this example , only the interneuron with a short spike latency met the tight timing requirements for bAP modulation at a more proximal synaptic location ( S6A Fig ) . The timing provided by the slower interneuron did not suffice to cancel the bAP proximally . In contrast , both ( faster and slower ) interneurons were able to cancel the bAP ( and accompanying calcium spike ) when their synapses were located at more distal parts of the dendritic tree ( S6B Fig ) .
The central hypothesis that inhibition can control synaptic plasticity has been discussed in the experimental and theoretical literature [5–7 , 35–37] . Its feasibility and functional relevance relate to three observations . First , different compartments within a neuron often receive excitatory input from distinct synaptic pathways [15] , such that a compartment-specific regulation of plasticity could be functionally advantageous . Second , different compartments are targeted by different inhibitory interneuron classes [25 , 38] , so that Hebbian coincidence signals could be regulated locally . Third , dendrites support different plasticity-related coincidence signals , namely bAPs [39 , 40] and calcium spikes [11 , 18 , 26] , which are sensitive to inhibitory control [4 , 11 , 41–43] . Besides a switch in plasticity , inhibition has been shown to contribute to the shape of temporal requirements for plasticity in different parts of the dendrite [44–46] . Moreover , the induction and coincidence requirements of plasticity have been described to change with development , potentially through an increase in inhibition [47–49] . Strong experimental evidence in support of the ability of inhibition to modulate plasticity via gating of bAPs was recently provided by Müllner et al . ( 2015 ) [8] . They showed that one important step in this mechanism—the suppression of somatically elicited bAPs by dendritic inhibitory synapses with high temporal precision—is indeed possible . For a robust switch of plasticity , however , several points remain to be shown: ( 1 ) gating of coincidence signals can be exerted in a controlled and systematic manner , ( 2 ) forward information flow via EPSPs can be maintained , and ( 3 ) plasticity itself is altered . Our analysis on the basis of mathematical models with physiologically constrained properties demonstrates that all three points can be fulfilled . In particular , we find that shunting inhibition is sufficient to cancel bAPs and calcium spikes while preserving the ability of EPSPs to elicit somatic APs . Timing of inhibition needs to be precise , but is not unrealistic ( ∼1 ms for bAPs and >5 ms for calcium spikes ) . Our study makes several testable predictions . In particular , the all-or-none nature of bAP modulation enabling the binary switch as well as the compartment specificity could be tested in experiments . To investigate the latter , classical paired recording paradigms for synaptic plasticity could be extended by optogenetic stimulation of different interneuron classes . Particularly promising candidates are the above mentioned SOM or PV interneurons that target perisomatic and distal dendritic regions of pyramidal cells , respectively [25 , 38] . Moreover , perisomatic versus distal modulation of dendritic coincidence signals poses different timing requirements on inhibition . The proximity of inhibition to the soma constrains the modulation window , because the times of passage of the forward-directed EPSP and the backward-directed bAP ( at the location of the inhibitory synapse ) are very close . Therefore , inhibitory synapses ought to have a certain distance to the soma to be well suited to control the bAP and plasticity without canceling somatic spiking . Our results suggest that at a distance of ∼100 μm , which is relatively proximal for apical dendrites , the timing window becomes wide enough to enable inhibitory control ( S4 Fig ) . We predict that the regulation of bAPs caused by more proximal excitation in the pyramidal neuron may be better achieved by interneurons with short latencies , such as fast-spiking interneurons . We found that distal inhibition of the calcium spike did not require very precise timing and tolerated longer delays to the onset of shunting inhibition . In the context of the here discussed mechanism of plasticity regulation , it would be functionally useful if proximal and distal inhibition were accomplished by interneurons of different dynamics . This proposition is in line with the fact that the many interneuron types connected to pyramidal cells differ in their spiking dynamics as well as their dendritic target location in pyramidal cells . Regulation of calcium spikes in the distal dendrite , on the other hand , need not be provided by local circuits , but could be mediated by longer-range connections including multiple synaptic transmissions . One may speculate that this time scale is beneficial when incorporating top-down information that tends to arrive in superficial layers [50] . From the perspective of the pyramidal neuron , we find that , a short rise time of synaptic inhibition ( like that typical for GABAA-mediated inhibition ( ∼0 . 5 ms ) ) is crucial for the mechanism to operate effectively . In contrast , the temporal extent of inhibition ( determined by the temporal extent of inhibitory input as well as the decay time constant of inhibition ) is less important . These variables are likely to be more relevant in setting a lower frequency limit to excitatory signals because they could interfere with a following EPSP if inhibition lasted for too long . For GABAA-typical decay time constants on the order of 5 ms ( as used here ) , such limits to the frequency of EPSPs are sufficiently large ( >100 Hz ) . We note that GABAA-mediated inhibitory postsynaptic potentials ( IPSPs ) have been described to differ between proximal and distal sites [20] . The difference is mainly in half-width and decay time constant , less in rise time . We hence used the same time constants for proximal and distal GABAA-mediated currents . Our results suggest that , in contrast to the other compartments , in basal dendrites several volleys of interneuron input may be needed to suppress all bAPs . In this scenario , a burst of bAPs invades the dendrite in the BAC firing mode . For this compartment , an innervation by bursting interneurons may hence be advantageous and several bursting interneuron types , including double bouquet cells [51] and bistratified cells [52–54] , have been described . Our prediction on the timing dependence of the modulation of bAPs quantitatively agrees with the experimental study by [8] . They found that calcium transients , evoked by a train of bAPs , are maximally inhibited with a spike timing ( between interneuron and pyramidal cell ) on the order of < 5 ms . This time scale is compatible with our predictions for the required timing of bAP modulation ( see Fig 3B/C ) . Also space constants of inhibition , observed to be on the order of 23–28 μm by Müllner and colleagues , are comparable to our results ( see Fig 2 ) . As our study showed , inhibition has to fall into a narrow time window to gate APs without simultaneously canceling EPSPs that are meant to drive the postsynaptic cell , potentially casting some doubt on the robustness of the mechanism of an inhibition-mediated plasticity switch . Feedforward inhibition , however , seems a good candidate to guarantee the appropriate timing , in particular , as in such a circuit inhibition follows excitation within a relatively fixed time interval . Feedforward inhibition is a common circuit motif [55 , 56] that has , for example , been implicated in keeping a balance of excitation and inhibition and to open time windows for precise firing events [33 , 34 , 57] . While we do not suggest that it is the only mechanism that can provide suitable timing for the plasticity switch discussed here , it can satisfy both temporal requirements for the cancellation of bAPs: the delay between forward-directed excitation and the shunting inhibition , as well as the temporal precision on the order of a millisecond . In the circuit model ( Fig 7A , black part of the schematic ) , a delay on the order of 2–3 ms between onset of the excitatory EPSP and the onset of the inhibitory IPSP in the pyramidal neuron was required to cancel the bAP triggered by the EPSP . This delay has to be accounted for by the processing in the inhibitory neuron itself and comprises the time the EPSP in the interneuron needed to reach this neuron’s soma , spike generation in this cell , propagation of the action potential along the axon , and the inhibitory synaptic transmission between interneuron and pyramidal cell . The timescale of 2–3 ms is plausible for these processes and agrees with the range reported in experiments [33 , 34] . Characteristics of the interneuron allow for some flexibility of this delay [58] . For example , a fast spike generation ( as in fast-spiking interneurons , due to lower firing threshold and/or stronger excitation on interneurons [59 , 60] ) plays an important role in keeping the delay short . Additionally , dendritic propagation is slower than axonal propagation , so that the relative length of these cables influences the required delay ( which may potentially be correlated to the somatic location of the interneuron , assuming a regular , bipolar morphology where the soma lies in between dendrites and axon ) . For example , a more proximal excitation in the pyramidal neuron requires a shorter delay of inhibition . The integration time in the pyramidal neuron depends on the location , and distribution of synaptic excitatory inputs , next to being negatively proportional to the number , strength and rise time of the synaptic conductances . In a feedforward inhibitory circuit , inhibition , by default , comes along with excitation . This means that an additional source is required to switch off the inhibitory influence ( see Fig 7A , gray part of the schematic ) . Per se , when the interneuron is active , plasticity of excitatory pyramidal synapses is switched off . In turn , silencing the interneuron up-regulates pyramidal cell plasticity . This design indicates a disinhibitory regulation of Hebbian plasticity , which is in line with recent findings for behavioral learning [61 , 62] . Interestingly , advances in unraveling the connectivity profile of different interneuron classes suggest that the cortical mircocircuitry seems to be well suited for a disinhibitory and compartment-specific regulation [24 , 36 , 63–65] . In particular , vaso-intestinal peptide ( VIP ) expressing interneurons and other supragranular interneuron classes have been proposed to modulate the activity of somatostatin ( SOM ) - and parvalbumin ( PV ) - positive cells—inhibitory interneurons with distinct postsynaptic targets within the pyramidal dendrite—in a way that is consistent with a rapid redistribution of inhibition between perisomatic and distal apical dendrites of pyramidal cells [24 , 66] . We note that distal disinhibition in pyramidal cells is special because it can significantly and reversibly increase the occurrence of calcium spikes and somatic bursts [67] . Calcium-induced bursting has been proposed as a mechanism for the association of inputs arriving through different pathways [11] . Because synaptic plasticity is often more efficiently induced by pairing presynaptic inputs with postsynaptic bursts , it is tempting to speculate that calcium spikes can induce a form of global synaptic plasticity within a neuron . Consequently , plasticity in the entire dendritic tree could be regulated at a single spot through local disinhibition . Note that such a form of plasticity regulation does not exclude , but complements our main hypothesis , because proximal inhibition could contribute to impair the arrival of bursts of backpropagating APs at basal and oblique synapses ( see Results on basal signaling ) . However , our prediction that basal plasticity regulation requires more complex inhibitory innervation in the presence of a calcium spike , possibly indicates that calcium-dependent burst firing is in place to overcome the effects of inhibition by increasing the likelihood of backpropagating action potentials passing the barrage . STDP is an important and commonly observed mechanism underlying many forms of learning [68] . Regulation of STDP ( a potential mechanism is considered in this study ) is hence highly relevant . However , there are other , non-Hebbian forms of synaptic plasticity which are independent of postsynaptic spiking . For example , plasticity can be directly triggered by spikes of dendritic origin that arise locally from cooperative or strong synaptic activation [26] . The proposed pathway-specific switch does not apply to these types of plasticity . Still , theoretical and experimental studies have shown that dendritic spikes can be affected by inhibition on a local ( spine- or branch-specific ) or global level [69–71] , possibly providing a mechanism to control timing-independent plasticity . Especially NMDA spikes cannot be excluded as a target for inhibitory modulation . Because they present a more local phenomenon that carries limited information about somatic spiking , an investigation thereof is beyond the scope of the present study . One kind of synaptic plasticity which is unlikely to be regulated by inhibition of dendritic events at all is presynaptically induced and expressed LTD [72] . In this study , we provide computational evidence that the known physiological characteristics of pyramidal cells are sufficient to exert a binary control of plasticity while preserving excitatory , forward-directed information flow . Despite the fact that the identified timing requirements for this mechanism may at first seem tight , the proposed local circuit motif of feedforward inhibition seems well suited to provide inhibition at appropriate times . Our modeling work substantiates the point of view that inhibition is likely to play a crucial role for Hebbian plasticity of excitatory synapses in a manner that can be specific to individual pathways of the local network [5] . Recent advances of optogenetic methods allow to shed further light on the computational relevance of interneuron diversity [50 , 63 , 64 , 73] and could—by targeting specific interneuron types—help to reveal whether ( and if so which ) neurons can regulate plasticity in local circuits .
To study the impact of inhibition on dendritic spikes , we recorded the membrane voltage in the basal dendrite ( 75 μm ) , the oblique dendrite ( 370 μm ) and the apical tuft ( 650 μm ) . Additionally , we monitored the calcium current in the apical tuft at the same spot . Spike amplitudes were measured as the maximum voltage deviation from rest ( a membrane voltage reaching 10 mV would be a spike of amplitude 10 mV- ( -73 mV ) = 83 mV ) . bAPs were normalized to the non-inhibited amplitude of the first bAP . Calcium spikes were quantified by the integral of the calcium current ( to clearly disambiguate between calcium and sodium components of the voltage trace at the same spot ) , normalized to the non-inhibited calcium current . A somatic spike was detected when its amplitude reached a threshold of 80 mV . Synaptic plasticity was implemented with an additive spike-timing dependent plasticity rule as in [13]: Δ w = { - A - exp ( Δ t τ - ) if Δ t ≤ 0 A + exp ( - Δ t τ + ) if Δ t > 0 where Δt is the difference between pre- and postsynaptic spike time: tpost-tpre . A postsynaptic spike was counted when the membrane voltage at the synapse reached a threshold of -20 mV . For apical tuft plasticity , a postsynaptic spike was counted when the calcium concentration reached a threshold of 0 . 5 mM . We used a different model for distal plasticity , assuming that the calcium spike governs calcium dynamics and thus plasticity in the distal dendrite ( for an illustration of our argument , see S5 Fig ) . Parameters were: potentiation factor A+ = 0 . 001 , depression factor A− = 0 . 00106 , potentiation time constant τ+ and depression time constant τ− were both 20 ms . Hard bounds set to 0 and 0 . 0001 μS were imposed on synaptic weights . Somatic current injection was paired with dendritic synapse activation 100 times with a frequency of 1 Hz with varying Δt . The feedforward inhibitory circuit contained the introduced multi-compartment pyramidal neuron model and a model of a fast-spiking interneuron ( IN ) . The latter , a single-compartment model with Hodgkin-Huxley-type sodium and potassium conductances , was taken from [85] ( ModelDB accession: 3817 ) . The interneuron with slower spike initiation was adapted from the fast-spiking interneurons by changing the sodium dynamics . The opening rate α was changed by a factor of 0 . 1 , the closing rate β by a factor of 0 . 2 . Both , the interneuron and the pyramidal neuron received EPSPs ( NEURON Exp2Syn with rise and decay time constants of 0 . 5 ms and 2 ms , respectively , and a reversal potential of 0 mV ) from an excitatory source ( EX ) at time t0 . The excitatory synapse onto the interneuron had a maximum conductance of 300 nS . To mimic inputs from oblique dendrites , eight synapses were distributed onto the apical trunk of the pyramidal neuron between 140 and 420 μm [86] with a total maximum conductance of 20 nS . The inhibitory synapse from the fast-spiking interneuron had the same properties as all shunting synapses modeled in this study ( τrise = 0 . 5 ms; τdecay = 5 ms; reversal potential -73 mV ) . From [23] , we took the neocortical L5 pyramidal cell model constrained by both BAC firing and perisomatic step current firing with an anatomically reconstructed morphology ( ModelDB accession: 139653 ) . We did not change any parameters . To investigate the all-or-none effect , the model was stimulated with somatic current injection as in [23] . We added inhibitory synapses ( NEURON Exp2Syn synapses; rise and decay time constants 0 . 5 and 5 ms , respectively ) to the model and placed them ∼ 100 μm ( proximal ) and ∼617 μm ( distal ) from the soma . The onset of inhibition was 2 . 5 ms after the onset of the somatic pulse if not varied . Voltage traces were measured in the soma , the oblique branch and an apical tuft branch . In Fig 5E and 5F , inhibition had maximum conductances of 100 nS ( proximal ) and 200 nS ( distal ) . | We must constantly learn in order to meet the demands of a dynamically changing environment . The basis of learning is believed to be synaptic plasticity , i . e . the potential of neuronal connections to change . Depending on context , however , it may be either useful to learn and modify connections or , alternatively , to keep an established network structure stable to maintain what has been already learned ( also referred to as the plasticity-stability dilemma ) . The ability to switch synaptic plasticity on and off in a flexible way hence constitutes an attractive feature of neuronal processing . Here , we analyze a cellular mechanism based on the inhibition-mediated gating of coincidence signals required for Hebbian forms of excitatory synaptic plasticity . While experimental evidence in support of individual steps involved in this mechanism is accumulating , it is as of now unclear whether this mechanism can indeed operate robustly under physiologically realistic parameters of pyramidal cells , in particular , without impairing information flow in these cells altogether . Computational modeling allows us to demonstrate that this is indeed possible if inhibition is well timed ( on the order of 1 ms ) . Moreover , we show that a specific design of the local circuit can ensure the necessary timing . | [
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] | 2016 | Inhibition as a Binary Switch for Excitatory Plasticity in Pyramidal Neurons |
The coordination of subcellular processes during adaptation to environmental change is a key feature of biological systems . Starvation of essential nutrients slows cell cycling and ultimately causes G1 arrest , and nitrogen starvation delays G2/M progression . Here , we show that budding yeast cells can be efficiently returned to the G1 phase under starvation conditions in an autophagy-dependent manner . Starvation attenuates TORC1 activity , causing a G2/M delay in a Swe1-dependent checkpoint mechanism , and starvation-induced autophagy assists in the recovery from a G2/M delay by supplying amino acids required for cell growth . Persistent delay of the cell cycle by a deficiency in autophagy causes aberrant nuclear division without sufficient cell growth , leading to an increased frequency in aneuploidy after refeeding the nitrogen source . Our data establish the role of autophagy in genome stability through modulation of cell division under conditions that repress cell growth .
Autophagy is a protein degradation pathway that is conserved from yeast to mammals . Previous studies have reported the functions and molecular mechanisms of autophagy , including the identification of autophagy-related genes [1] , [2] , the characterization of the molecular mechanisms of each autophagy stage [3] , [4] , and the detection of selective autophagy [5] , [6] . Autophagy is induced in response to nutrient starvation . In addition , homeostatic autophagy occurs at a low level in mammalian cells under nutrient-rich conditions [7] , [8] , and is regulated in a cell cycle-dependent manner [9] , [10] . Furthermore , autophagy is induced during developmental stages [11] . In budding yeast , cell division and cell growth are precisely regulated by an intrinsic mechanism , and are tightly modulated by nutrient conditions . Target of rapamycin ( TOR ) is a phosphatidylinositol kinase-related Ser/Thr kinase and a critical regulator of cell growth , which senses the nutrient conditions [12] . TOR forms two distinct complexes , TOR complex 1 ( TORC1 ) and TOR complex 2 ( TORC2 ) [13] . Yeast TORC1 consists of either of the two yeast TOR homologs , Tor1 or Tor2 , together with co-factors Kog1 , Lst8 , and Tco89 , which are sensitive to inhibition by rapamycin . Conversely , yeast TORC2 , which is produced from Tor2 , Avo1–3 , and Lst8 , is not sensitive to rapamycin . While TORC2 regulates spatial aspects of cell growth , such as the organization of the actin cytoskeleton , TORC1 regulates temporal aspects of cell growth , including protein synthesis , gene transcription , ribosome biogenesis , amino acid uptake , and induction of autophagy [13]–[16] . Under nutrient-rich conditions , TORC1 is active and inhibits the induction of autophagy through inhibitory phosphorylation of Atg13 [17] . In contrast , TORC1 is inactive during starvation , thereby inducing autophagy [18] . Inhibition of TORC1 by rapamycin or by nutrient starvation leads to cell cycle arrest in the G1 phase [19] , demonstrating that TORC1 plays a crucial role linking cell growth and cell cycle progression . In addition to its role in the regulation of cell cycle progression in the G1 phase , we recently found that TORC1 is involved in the G2/M transition in budding yeast [20] . Reduced TORC1 activity , caused by nutrient starvation or by temperature-sensitive mutation in KOG1 , which is an essential component of TORC1 , leads to cell cycle arrest at G2/M . Two recent reports have shown that nutrient starvation also blocks the onset of mitosis in mammalian cells and the fission yeast Schizosaccharomyces pombe [21] , [22] . The regulation is achieved by TORC1-mediated activation of Cdc2/cyclin B in mammalian cells [21] , whereas it is suggested to be mediated by TORC2 as well as Sty1 MAPK signaling in the fission yeast [22] , [23] . Although the underlying mechanisms may differ among species , the transient suppression of mitotic entry in response to nutrient starvation is conserved throughout evolution . However , it is well-known that cells arrest in the G1 phase and enter G0 during starvation [24] . Thus , the mechanism as to how G2/M-delayed cells progress through the cell cycle to return to the G1 phase remains unclear . In this study , we elucidated the molecular mechanism underlying cell cycle progression under nutrient-limited conditions . We show that the cell cycle delay at G2/M is rescued in an autophagy-dependent manner . Regulation of the cell cycle by autophagy during starvation is believed to be involved in genome integrity by coupling cell growth with cell division .
TORC1 is thought to be a nutrient sensor that controls the cell cycle and autophagy directly [12] , [18] . We first examined fluctuation in TORC1 activity under starvation conditions by an immunoblot of Atg13 , a TORC1 substrate , and RT-qPCR of genes whose expression is a product of TORC1 function . TORC1 directly phosphorylates Atg13 [17] , thus , inactivation of TORC1 causes dephosphorylation of Atg13 . In addition , TORC1 induces the transcription of RPS26A , RPL9A , and NOG1 but suppresses that of MEP2 and GAP1 [16] , [25] , [26] . When exponentially growing wild-type ( WT ) cells were released into nitrogen-depleted medium , TORC1 activity was immediately decreased; Atg13 became dephosphorylated ( Figure 1A ) , resulting in a concomitant decrease in the expression of RPS26A , RPL9A , and NOG1 and an increase in the expression of MEP2 and GAP1 ( Figure 1B ) . We found that phosphorylation of Atg13 gradually recovered after 2–18 h in nitrogen-depleted medium ( Figure 1A ) , suggesting that TORC1 activity was restored in cells . The expressions of RPS26A , RPL9A , and NOG1 were increased , but those of MEP2 and GAP1 remained at high levels 18 h after release into nitrogen-depleted medium ( Figure 1B ) . The lack of a correlation between the recovery of Atg13 phosphorylation and the levels of MEP2 and GAP1 suggests that TORC1 activity did not completely recover during this process . Next , we investigated whether autophagy is involved in the recovery of TORC1 , and assessed the fluctuation in TORC1 activity in autophagy-deficient Δatg2 cells . When Δatg2 cells were placed in nitrogen-depleted medium , TORC1 activity decreased , similar to WT cells up to 4 h ( Figure 1A and 1B ) . Interestingly , TORC1 activity was not restored in Δatg2 cells 18 h after being released into the starvation medium ( Figure 1A and 1B , left panel ) . Re-phosphorylation of Atg13 and increased expression of RPS26A and RPL9A under starvation conditions required other autophagy-related genes , such as ATG1 and ATG7 , which are essential for autophagosome formation , and PEP4 encoding vacuolar proteinase A , which is responsible for autophagic degradation of proteins accompanied by recycling of amino acids ( Figure 1C and 1D , left panel ) . In contrast , recovery of TORC1 activity was not affected by deletion of ATG11 , which is essential in selective autophagy and dispensable for starvation-induced autophagy ( Figure 1C ) . Therefore , these results suggest that the partial recovery of TORC1 activity is induced in a non-selective and starvation-induced autophagy-dependent manner . The cell cycle is arrested in the G1 phase under starvation conditions [19] , and we previously reported that TORC1 inactivation caused by rapamycin treatment and nitrogen starvation induces a G2/M delay [20] . Therefore , we postulated that an unknown mechanism may exist that regulates cell cycle re-progression from G2/M under nutrient starvation . We carefully examined the cell cycle profiles of nitrogen-starved cells , particularly the relationship between cell cycle progression and TORC1 activity . First , WT and Δatg2 cells were synchronized in the G1 phase by treatment with α-factor , and released into SCD medium . When the majority of cells progressed to the S phase , they were released into nitrogen-depleted medium . During this time course , cells were collected at intervals and the DNA content of cells at each time point was examined by FACS analysis . As previously reported , WT and Δatg2 cells remained arrested at 2C DNA content for 2–4 h after α-factor release , demonstrating that cell cycle progression was delayed at G2/M ( Figure 2A ) . In WT cells , the delay in cell cycle progression was overcome after 5 h , and most cells reached the G1 phase after 25 h ( Figure 2A ) . In contrast , a signification portion of Δatg2 cells , as well as Δatg1 cells , remained arrested at 2C DNA content after 25 h ( Figure 2A and Figure S2 ) . The difference in the cell cycle profiles between WT and atg2 mutant cells was confirmed by Clb2 levels; G2/M cyclin Clb2 decreased 4–5 h after α-factor release in WT cells , indicating that the cells entered mitosis , whereas Clb2 was consistently present in Δatg2 cells ( Figure 2B ) . These results show that autophagy contributes to re-progression at G2/M during starvation . Next , we investigated whether re-activation of TORC1 is correlated with re-progression at G2/M . We analyzed time-dependent changes in gene expression downstream of TORC1 . In WT cells , transcription of RPS26A and RPL9A decreased in response to starvation ( 2 h ) , but was gradually restored after 5–25 h ( Figure 2C ) . Mitotic entry correlated with the re-activation of TORC1 ( Figure 2A and 2C ) . Conversely , transcription of RPS26A and RPL9A remained low in Δatg2 cells , and the cells remained arrested at G2/M ( Figure 2A and 2C ) . These results show again that TORC1 is partially restored in an autophagy-dependent manner in nitrogen-starved cells , and suggest that a correlation between the partial recovery of TORC1 activity and cell cycle re-progression at G2/M . To further address the mechanism of autophagy-dependent cell cycle re-progression during starvation , we next focused on the role of amino acid pools . Autophagy contributes to the maintenance of amino acid pools in yeast; during the first two hours of nitrogen starvation , the intracellular amino acid level decreases rapidly , and is then partially recovered in an autophagy-dependent manner [27] , [28] . Since the amino acids produced by enhanced autophagy are utilized for new protein synthesis [27] , [28] , we investigated whether specific amino acids produced by autophagy contribute to cell cycle re-progression after a G2/M delay . The Δatg2 strain AMY250 possesses his3 , trp1 , and ura3 mutations causing auxotrophies for histidine , tryptophan , and uracil , respectively . We postulated that these auxotrophic mutations would affect the intracellular pools of the corresponding amino acids especially upon starvation . As hypothesized , the G2/M-delayed Δatg2 mutant returned to G1 phase similarly to wild-type ( WT ) cells when tryptophan was added to the nitrogen-starved medium ( Figure 3A ) . In contrast , the addition of histidine did not affect the suppression of the prolonged G2/M delay in the Δatg2 mutant , nor did the addition of glutamine , the level of which is believed to be involved in TORC1 activity in yeast [29] ( Figure 3A ) . Recent studies have shown that TORC1 activity is regulated by the availability of some amino acid species including leucine; this is dependent on the editing function of aminoacyl-tRNA transferase [30] , [31] . Using another Δatg2 mutant ( AMY296 ) , which is congenic to AMY250 with adenine and leucine auxotrophies , we further examined the effect of amino acid supplementation to a starvation medium on autophagy-deficient cells . As shown in Figure 3B , the addition of tryptophan alone was insufficient for the cell cycle progression of AMY296 . However , the simultaneous addition of tryptophan and leucine efficiently rendered the cells to a G1 arrest . These results show that cell cycle perturbations caused by a deficiency in autophagy may be suppressed by the addition of amino acids specific to the strains , suggesting that autophagy contributes to cell cycle progression by allocating some , but not all , amino acids that are present in a limited intracellular amount . Next , we examined whether the addition of specific amino acids would upregulate the starvation-repressed TORC1 activity . Interestingly , while the addition of amino acids mimicked cell cycle progression in the autophagy-proficient cells , it was not associated with an increase in transcription of TORC1 downstream RPS26A and RPL9A ( Figure 3C and 3D ) . This result raises the possibility that TORC1 recovery is not essential for cell cycle re-progression after a G2/M delay , although TORC1 re-activation correlates with re-progression . To examine the causal relationship between the recovery of TORC1 activity and cell cycle re-progression , we examined cell cycle progression during starvation when TORC1 activity was inhibited by the addition of rapamycin . As reported previously [20] , rapamycin treatment transiently delayed cell cycle progression at G2/M in nutrient-rich SCD medium , and most cells returned to G1 irrespective of their autophagic competency ( Figure 4A ) . In contrast , rapamycin-treated WT cells showed a G2/M delay and proceeded to G1 when rapamycin was added to the nitrogen-starved medium ( Figure 4A ) . TORC1 activity , monitored by RPS26A expression , was persistently lower in rapamycin-treated cells than that in those without rapamycin treatment during starvation ( Figure 4B ) , supporting the hypothesis that TORC1 recovery is dispensable for cell cycle re-progression after a G2/M delay . Cell cycle progression to G1 phase in rapamycin-treated starved cells was significantly faster than in those that without rapamycin treatment ( Figure 4A ) . However , the effect of rapamycin was not observed in Δatg2 cells , in which the cell cycle remained arrested in the G2/M transition in nitrogen-starved medium , even with the addition of rapamycin ( Figure 4A ) . Again , these results show that autophagy is critical for cell cycle progression from G2/M to G1 under starvation conditions , and suggest the involvement of autophagy-dependent supply of amino acid pools in the accelerated cell cycle progression by rapamycin treatment . We further examined the role of TORC1 activity in cell cycle re-progression using a temperature-sensitive KOG1mutant ( kog1-105 ) [20] , encoding an essential component of TORC1 . Since the kog1-105 mutant showed a G2/M arrest phenotype at the restrictive temperature , the experiment was performed at 25°C , a permissive temperature at which the mutant is able to proliferate . When cells were cultured under nitrogen-starved conditions , the G2/M delay was prolonged in kog1-105 cells compared to WT cells ( Figure S3A ) . During the course of the experiment , TORC1 activity monitored by RPS26A expression was slightly lower in the kog1-105 mutant than in WT cells , both of which were exposed to nutrient-rich condition and nitrogen-starved conditions ( Figure S3B ) . Moreover , the delay in cell cycle re-progression in the kog1-105 cells was relieved by the addition of rapamycin ( Figure S3C ) . This result shows that further inhibition of TORC1 activity in kog1-105 cells by rapamycin is sufficient for cell cycle re-progression at G2/M , suggesting that the delay in cell cycle re-progression of the kog1-105 mutant is not likely due to the reduction in global TORC1 activity . Rather , kog1-105 may cause a defect in specific pathways downstream of TORC1 , and the residual activity of TORC1 in the kog1-105 mutant , which can be inhibited by rapamycin , may prevent cell cycle recovery after a G2/M delay . We investigated how amino acids produced by autophagy contribute to re-progression of the cell cycle from a G2/M delay under starvation conditions . We first observed the morphology of WT and Δatg2 cells cultured under starvation conditions . As shown in Figure 5 , the daughter cell of Δatg2 cells was smaller than that of WT cells 4 h after α-factor release , suggesting that amino acids produced by autophagy are important for sufficient cell growth during nutrient starvation . The status of the bud ( daughter cell ) and timing of nuclear division are strictly regulated by the Swe1-dependent checkpoint mechanism in budding yeast [32]–[35] . Therefore , we further analyzed cell division using Δswe1 and Δatg2 Δswe1 cells by DAPI staining to examine the potential relationship between the checkpoint mechanism and autophagy . As shown in Figure 6A and 6B , loss of the Swe1 function in both WT and Δatg2 cells caused an increase in premature mitosis at early time points ( 2–3 h after α-factor release; type 4 in Figure 7A ) . Interestingly , some of the Δswe1 cells arrested at 2C DNA content underwent normal cell division and returned to the G1 phase after 25 h ( Figure S4 ) , suggesting that premature mitosis caused by Swe1 dysfunction was rescued . This result is likely due to the spindle orientation checkpoint [36] , which can correct an abnormal nuclear position at later time points . Since the Swe1-dependent checkpoint delays entry into mitosis by regulating Swe1 degradation [37] , we examined the amount of Swe1 in response to nitrogen starvation . During normal cell cycling under nutrient-rich conditions , Swe1 was accumulated 40 min after α-factor release , and degraded after 1 . 5 h ( Figure 6C ) . Under starvation conditions , Swe1 was more stable , and degraded only after 3 h ( Figure 6C ) . These results support the hypothesis that the starvation-induced delay of mitosis is mediated by Swe1 . This notion is consistent with our previous result that TORC1 regulates G2/M progression through budding yeast polo-like kinase Cdc5 , a negative regulator of Swe1 in the initiation of mitosis [20] . In Δatg2 cells , degradation of Swe1 occurred normally in nutrient-rich medium , and was delayed under starvation conditions ( Figure 6C ) . Indeed , in Δatg2 cells , cells that underwent premature mitosis were scarcely observed 2 h after α-factor release ( Figure 6A and 6B ) . Degradation of Swe1 was delayed in Δatg2 cells , and a portion of Swe1 still remained 5 h after α-factor release ( Figure 6C ) , showing that autophagy is important for Swe1 degradation in nutrient-starved cells . However , we noted that Swe1 decreased gradually in Δatg2 cells under starvation conditions . Consistently , after 2–3 h , approximately half of the Δatg2 cells were arrested before mitosis , but at later time points , the majority of the cells appeared to enter mitosis , as indicated by a reduction in the population of cells with one nucleus ( type 2 in Figure 7A ) . Thus , autophagy is important for efficient recovery from the Swe1-dependent checkpoint under starvation conditions , although autophagy-deficient cells might , at least partly , execute nuclear division after a prolonged cell cycle delay . Nonetheless , cells cannot complete cell division without autophagy under starvation conditions because , unlike Δswe1 cells , the majority of Δatg2 Δswe1 cells remained arrested at 2C DNA content even 25 h after α-factor release ( Figure S4 ) . Indeed , cells that passed nuclear division but not cytokinesis ( type 3 in Figure 7A ) also accumulated transiently in WT cells 2–4 h after α-factor release , and such cells were consistently observed even at later time points in Δatg2 cells ( Figure 7A ) . To examine whether autophagy is involved in cell cycle progression after nuclear division , we used an anti-microtubule drug nocodazole to synchronize cell cycle at metaphase . WT cells were first synchronized in the G1 phase by treatment with α-factor , released into SCD medium , and then transferred into nitrogen-depleted medium containing nocodazole . After 3 h when the majority of cells was arrested at metaphase , cells were collected and re-released into SD-N medium containing 1 mM PMSF that specifically inhibited autophagic degradation under nutrient-starved conditions [38] . As shown in Figure 7B–7D , nocodazole-arrested cells showed a delay in completion of cytokinesis when autophagy was inhibited by the addition of PMSF . Therefore , autophagy contributes not only to nuclear division , but also to cytokinesis , or cell separation , under nutrient-starved conditions . Finally , we investigated the physiological significance of autophagy-dependent cell cycle re-progression from G2/M to G1 during starvation conditions . Δatg2 cells cultured in nitrogen-starved conditions for 24 h showed an increased frequency of aberrant mitosis , in which two nuclei were present in a mother cell ( type 4 in Figure 7A; Figure 6A and 6B ) . This was likely due to the leaky recovery from the Swe1-dependent mitotic delay . Moreover , when cells were replenished with a nitrogen source and cultured for 2 h , cells with unusually high DNA content ( 3C DNA content ) appeared in Δatg2 and Δatg1 mutants but not in WT cells ( Figure 2A and Figure S2 ) . In addition , analysis by a genetic system using intrachromosomal recombination [39] revealed that Δatg2 cells cultured in starvation medium displayed an increased frequency of aneuploidy ( 2 . 3-fold and 6 . 6-fold higher than that of WT cells after starvation for 24 and 48 h , respectively; Figure 8 ) . These results indicate that cell cycle arrest in the G1 phase under starvation conditions is critical for the normal progression of mitosis after restoration of nutrient conditions , and highlight the importance of autophagy-dependent cell cycle re-progression in genome stability .
Although nutrient starvation reduces TORC1 activity and subsequently induces cell cycle arrest in the G1 phase [19] , cell cycle progression at the G2/M boundary was blocked under starvation conditions [20]–[23] . Autophagy has been implicated in cellular physiologies under starvation conditions , and the data presented herein uncover another aspect of autophagic functions , namely , its contribution to cell cycle regulation and genome integrity in nutrient-starved yeast cells through the regulation of mitosis progression . Nuclear division and cytokinesis are two critical events during the cell cycle , both of which require protein synthesis [40] . Here we showed that , in addition to nuclear division , cytokinesis is another step of the cell cycle with limitations , whose entry is blocked or slowed by nutrient starvation , and that starvation-induced autophagy is required to overcome the cell cycle delay at both steps ( Figure 6 and Figure 7 ) . Previous reports have revealed that amino acids produced by autophagy are used for protein synthesis [27] , [28] . In the present study , we showed that the defects in cell cycle re-progression caused by a lack of autophagy was suppressed by the addition of particular amino acids ( Figure 3 ) , and that autophagy promoted the growth of daughter cells in starvation medium ( Figure 5 ) . These results clearly show that the amino acid supply through autophagy contributes to sufficient cell growth during nutrient starvation . We found that this phenotype was associated with auxotrophies to specific amino acids , including leucine and tryptophan , but not histidine or nucleobases , including uracil and adenine ( Figure 3 ) . The composition of the amino acid pool in budding yeast cells is influenced by nutrient conditions; cells using NH4+ as the sole nitrogen source accumulate glutamic acid and arginine , but contain amino acids such as tyrosine , leucine , tryptophan , and phenylalanine at low levels [41] . Thus , we assume that , at least under our experimental conditions , leucine and tryptophan are limiting amino acids whose pools cannot support protein synthesis without autophagy upon nitrogen starvation . It will be interesting to examine the potential difference in amino acid requirements for the suppression of autophagy deficiency by varying the composition of amino acid pools using alternate nitrogen sources . In cultured human cells , the cellular pools of glutamine and glutamic acid are maintained at high levels , whereas those of tryptophan , cysteine , and arginine are maintained at low levels [42] . It is important to note that the latter three are essential amino acids that cannot be synthesized de novo in humans; thus , autophagy may be involved in maintaining the pools of these amino acids in human cells . A recent paper reported that TORC1 activity was partially reactivated in an autophagy-dependent manner in ongoing starvation conditions , which played a role in the attenuation of autophagy [43] . Consistent with their findings , we demonstrated that starvation-induced autophagy leads to partial re-activation of TORC1 activity and that the timing of the re-activation correlates with that of cell cycle re-progression at G2/M ( Figure 2 ) . Although a reduction in TORC1 activity appears to contribute to the transient cell cycle delay at G2/M , we showed that the cell cycle delay was relieved under TORC1-repressed conditions ( Figure 4 ) . These results argue against the positive role of TORC1 re-activation during a rescue from the delay . Although TORC1 activity is involved in translation [19] , previous studies have shown that the synthesis of specific proteins continues under TORC1-inhibited conditions [44] . Thus , even in case that TORC1 activity is not re-activated , protein synthesis supported by the autophagy-mediated amino acid pools may be sufficient for starvation-adapted cells to complete a final round of the cell cycle . Although the inhibition of TORC1 by rapamycin did not abolish cell cycle re-progression from a G2/M delay , the temperature-sensitive kog1-105 mutation did affect this process ( Figure 4 and Figure S3 ) . There are three possible models to explain the cell cycle-specific phenotype of kog1-105 cells . In the first model , kog1-105 cells may be defective in only a part of multiple TORC1 functions , and the function affected by kog1-105 is necessary for cell cycle progression after a G2/M delay . In the second model , TORC1 may be significantly affected in kog1-105 and the level of TORC1 activity in the mutant is below that caused by nutrient starvation or rapamycin treatment in WT cells . The last model , based on the possibility that kog1-105 is a gain-of-function mutation , suggests that Kog1-105 protein fulfills a function in addition to the TORC1 function , which is not performed by the normal Kog1 protein . Our results showing that rapamycin suppresses the kog1-105 defect contradict the latter two models; therefore , it is likely that the kog1-105 mutation blocks only a portion of the pathways downstream of TORC1 . This scenario is consistent with our previous findings that kog1-105 is not defective in the progression of G1 and does not induce autophagy under nutrient-rich conditions at a restrictive temperature [20] . We also found that the kog1-105 mutation affected the interaction between Kog1 and TORC1 [20] . Raptor , the mammalian ortholog of Kog1 , contributes to the inhibition of mTOR activity upon nutrient depletion through stabilization of the mTOR-Raptor complex [45] . Although it is unknown whether Kog1 inhibits TORC1 under starvation conditions , a decreased interaction with TORC1 could contribute to the phenotypes observed in kog1-105 , which are distinct from those caused by rapamycin treatment . There are several examples of rapamycin-insensitive , TORC1-dependent processes in mammalian cells [46] . Although the presence of such processes is elusive in yeast , it is possible that rapamycin-insensitive TORC1 activity is involved in the recovery from a G2/M delay . However , under these circumstances , it is difficult to explain why the addition of rapamycin to nitrogen-depleted medium leads to an early recovery from a cell cycle delay ( Figure 4A ) . It is interesting to note that the addition of rapamycin to fission yeast cells leads to early mitotic onset in nutrient-rich medium , resembling the cell cycle behavior caused by a reduction in the quality of the nutrient source [47] . In budding yeast , cellular responses to rapamycin are not identical to those induced by nitrogen starvation [48] , [49]; rapamycin treatment rapidly activates the quality-sensitive nitrogen discrimination pathway , which is distinct from the nitrogen starvation pathway , to facilitate use of poor nitrogen sources . Such a difference may induce rapid adaptation to nitrogen starvation in rapamycin-treated cells . Indeed , the addition of rapamycin facilitates adaptation to an environment containing a low-quality nitrogen source in budding yeast [50] . We tested the involvement of the nitrogen discrimination pathway in the rapamycin-induced early cell cycle recovery by deleting GLN3 , which encodes the key transcriptional regulator in the nitrogen discrimination pathway , and found that rapamycin treatment accelerated recovery from the G2/M delay even in Δgln3 mutants ( data not shown ) . Therefore , we can at least conclude that rapamycin-induced transcription through Gln3 is not essential for this phenotype . It is known that TORC1 regulates a variety of cellular events including transcription , translation , and post-translation [51] . Note that autophagy is essential for rapamycin-mediated early cell cycle recovery ( Figure 4A ) and that protein synthesis supported by the amino acid pool appears to be involved in this mechanism . Further studies are required to specify the pathway responsible for the early recovery of cell cycle by rapamycin . In addition , it would be interesting to determine physiological conditions that induce the early cell cycle recovery phenotype in a similar manner to that caused by rapamycin treatment; such approaches will help clarify the significance of cell cycle regulation in response to acute inhibition of TORC1 activity . We have shown that the G2/M specific function of TORC1 is mediated by the polo-like kinase Cdc5 [20] , an upstream regulator of Swe1 [52] . Swe1 is involved in a checkpoint mechanism to ensure accuracy of cell division by monitoring daughter cells [32]–[35] . When this checkpoint mechanism is inhibited by the swe1 mutation , or overexpression of its negative regulators , HSL1 and HSL7 , nuclear division occurs prematurely , even if the growth of the bud is suppressed by a mutation in CDC24 [53] . We observed a similar premature mitosis of Δswe1 cells under nitrogen starvation ( Figure 5A and 5C ) , indicating that the Swe1-dependent checkpoint , probably activated by insufficient bud growth , contributes to the G2/M delay phenotype induced by starvation . We found that a deficiency in SWE1 also increased the fraction of cells that cannot return to the G1 phase normally ( Figure S4 ) , indicating that both the Swe1-dependent cell cycle delay and the autophagy-dependent recovery are critical for the integrity of mitosis . Thus , timely regulation of cell cycle progression is of significance under starvation conditions . Although autophagy is required for the growth of daughter cells during starvation conditions ( Figure 5 ) , in our experimental conditions , more than 80% of the autophagy-deficient cells ultimately proceeded to nuclear division after delay at the G2/M boundary . This may be because cells can produce a limited pool of amino acids that are independent of autophagy that support maturation of the bud to overcome the Swe1-dependent checkpoint . Otherwise , long-term starvation might cause an imbalance in the amount of proteins regulating M phase entry , thereby initiating nuclear division , since the Swe1-dependent checkpoint does not appear to monitor bud size per se , but detects an accumulation of mitotic cyclins in the bud [54] . In either case , cell division is not completed before cytokinesis without autophagy . Cytokinesis may serve as an additional control gate for the fulfillment of daughter cell maturation under nutrient-limited conditions . Cell cycle-dependent regulation of constitutive autophagy has been shown in mammalian cells [7]–[10] . However , this study established the direct involvement of autophagy in cell cycle regulation under starvation conditions; autophagy ensures the accomplishment of the final round of cell cycle progression to nutritionally starved cells . Without autophagy , prolonged treatment with nitrogen starvation caused perturbation of the cell cycle , including premature mitosis , and caused an increased frequency of aneuploidy in budding yeast ( Figure 6A , 6B and Figure 8 ) . Thus , in addition to the developmental significance of returning to the G1 phase under starvation conditions ( i . e . , meiotic division is only initiated from the G1 phase in diploid yeast cells ) , our results indicate that returning to the G1 phase is critical for maintaining genome integrity after restoration of the nutrient condition from starvation . Notably , a previous study reported that compromised autophagy promotes genomic instability , such as increased DNA damage , gene amplification , and aneuploidy in mammalian cells [55] , consistent with the tumor-suppressive activity of autophagy that was previously reported [56]–[59] . Our results demonstrate that autophagy allows a final round of cell cycle progression in budding yeast cells by supplying amino acids during nutrient starvation . This type of regulation can be considered part of the feedback system that maintains the minimum cell volume after division . Asymmetric division , the manner of cell division in the budding yeast , would require sufficient de novo protein synthesis for producing a daughter cell different from the mother , and autophagy may support this under nutrient-starved conditions . It will be interesting to determine whether autophagy-dependent regulation is implicated in genetically determined asymmetric division in mammalian development , such as differentiation and maintenance of stem cells . Further studies will shed light on the physiological roles of autophagy , including cell cycle regulation and development .
The yeast strains used in this study are listed in Table S1 . Unless otherwise noted , all strains were derived from W303-1A and W303-1B . DBY4962 was a gift from Dr . Botstein ( Princeton University , USA ) [39] . Standard genetic techniques including growth media , cell growth conditions , and transformations were performed as described previously [60] , [61] . The plasmid , pRS316-GFP-ATG8 , was a gift from Dr . Suzuki ( University of Tokyo , Japan ) . It harbors the ATG8 sequence with an N-terminal GFP tag in pRS316 [62] . The GFP-ATG8 fragment was integrated into pRS314 to construct pRS314-GFP-ATG8 . YEp352-ATG13 , pRS313-KOG1 , and pRS313-kog1-105 were described previously [20] . Samples corresponding to 0 . 2 OD600 ( anti-GFP , anti-Pgk1 , PAP , and anti-myc antibodies ) or 0 . 1 OD600 ( anti-Atg13 antibody ) units of cells were separated by SDS-PAGE followed by Western blotting and immunodecoration . Signal detection was performed using an enhanced chemiluminescent ( ECL ) detection system ( GE Healthcare ) or Immunostar Zeta ( Wako ) . The anti-Atg13 antibody was a gift from Dr . Ohsumi ( Tokyo Institute of Technology , Japan ) . Anti-GFP ( NACALAI TESQUE ) , anti-Pgk1 ( Invitrogen ) , PAP ( Sigma Aldrich ) , and anti-myc ( Cell Signaling Technology ) antibodies were used to detect GFP , Pgk1 , TAP , and myc , respectively . RNA samples were prepared by the hot-phenol assay using phenol∶chloroform ( 5∶1; pH 4 . 7 ) ( Sigma Aldrich ) and the Qiagen RNeasy Mini Kit ( Qiagen ) . The reverse transcriptase reaction was performed using the ReverTra Ace qPCR RT Kit ( TOYOBO ) or the ReverTra Ace qPCR RT Master Mix with gDNA Remover ( TOYOBO ) . Real-time PCR was performed using SYBR Green Realtime PCR Master Mix Plus ( TOYOBO ) in duplicate . The mRNA levels of RPS26A , RPL9A , MEP2 , GAP1 , and NOG1 were quantified using corresponding primer sets purchased from OPERON . TUB1 and ACT1 were used as controls for quantification . Cell synchronization in G1 was obtained by treating exponentially growing MATa cells with 4 ng/mL α-factor mating pheromone for ∼2 h . G1-arrested cells were washed with distilled water twice and conditioned medium once , and released into fresh SCD medium . After 0 . 75 h , synchronized cells were washed with distilled water three times , released into fresh SD-N and SCD medium with or without 200 ng/mL rapamycin ( Sigma Aldrich ) and 6 . 7 ng/mL α-factor , and collected at the indicated times . For cell synchronization at metaphase , G1-synchronized cells were washed with distilled water three times , released into fresh SD-N medium containing 5 µg/mL nocodazole ( Sigma Aldrich ) after 0 . 75 h from α-factor release . After 3 h , synchronous cultures arrested at metaphase by nocodazole were collected and re-released into SD-N medium with or without 1 mM PMSF ( Wako ) , and collected at the indicated times . For FACS analysis , cells were fixed with 70% ethanol at 4°C overnight . Then the cells were washed and suspended in 50 mM sodium citrate , treated with 250 µg/mL RNase A at 50°C for 1 h , and then treated with 1 mg/mL proteinase K at 50°C for 1 h . The resuspended cells were stained with 16 µg/mL propidium iodide at room temperature for 30 min . The DNA content of cells was measured on a Beckman-Coulter flow cytometer . Nuclear DNA was visualized by the addition of 1 µg/mL Hoechst 33342 or 50 ng/mL DAPI after cells were fixed with 3% formaldehyde at 4°C overnight . For staining by DAPI , cells fixed with formaldehyde were washed with PBS twice , and suspended in PBS with 0 . 1% Triton-X100 . After 1 h , cells were washed with PBS 5 times , and suspended in PBS . Cells were observed using an inverted microscope ( Delta Vision or Leica DMI 4000B ) . Images were captured using image acquisition and analysis software . Quantification of aneuploidy formation was performed using the system developed by Chan and Botstein [39] . This assay is based on a genetic system that detects yeast cells with extra copies of a genetically marked chromosome . To monitor the frequency of aneuploidy , appropriately diluted cell cultures were plated onto yeast extract peptone dextrose ( YEPD ) plates and selective medium lacking both leucine and uracil . The number of colonies was scored after incubation for 4 days at 30°C . Data sets of diameter ratios were analyzed as described previously [63] . First , the data were subjected to the Kruskal-Wallis rank sum test , with P<0 . 05 considered significant . Then , the significance of differences between WT and Δatg2 cells was determined using Sokal and Rohlf's test of nonparametric multiple comparisons by STP [64] . Equal-sized subsets were obtained from the larger data subsets using a random selection algorithm . | A nutrient stress such as nitrogen depletion induces pleiotropic responses in eukaryotic cells . For example , nutrient starvation slows cell cycling and ultimately causes G1 arrest . In addition , it is known that nitrogen starvation delays G2/M progression . However , the mechanism as to how G2/M-delayed cells progress through the cell cycle to return to the G1 phase remains unclear . Cells subjected to a nutrient stress induce autophagy , a bulk degradation system within lysosomes/vacuoles , to reconstitute cellular components . In this study , we show that an autophagy-dependent supply of amino acid pools is critical for completion of cell cycle under starvation conditions in the budding yeast Saccharomyces cerevisiae . Autophagy deficiency causes a defect in cell growth and leads to abnormal mitosis associated with a higher incidence of aneuploidy . Thus , our data establish the role of autophagy in genome stability through modulation of cell division under conditions that repress cell growth , which provides a possible mechanism of tumor suppression by autophagy shown in mammalian cells . | [
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] | 2013 | The Role of Autophagy in Genome Stability through Suppression of Abnormal Mitosis under Starvation |
Clearance of apoptotic cells by engulfment plays an important role in the homeostasis and development of multicellular organisms . Despite the fact that the recognition of apoptotic cells by engulfment receptors is critical in inducing the engulfment process , the molecular mechanisms are still poorly understood . Here , we characterize a novel cell corpse engulfment pathway mediated by the integrin α subunit PAT-2 in Caenorhabditis elegans and show that it specifically functions in muscle-mediated engulfment during embryogenesis . Inactivation of pat-2 results in a defect in apoptotic cell internalization . The PAT-2 extracellular region binds to the surface of apoptotic cells in vivo , and the intracellular region may mediate signaling for engulfment . We identify essential roles of small GTPase CDC-42 and its activator UIG-1 , a guanine-nucleotide exchange factor , in PAT-2–mediated cell corpse removal . PAT-2 and CDC-42 both function in muscle cells for apoptotic cell removal and are co-localized in growing muscle pseudopods around apoptotic cells . Our data suggest that PAT-2 functions through UIG-1 for CDC-42 activation , which in turn leads to cytoskeletal rearrangement and apoptotic cell internalization by muscle cells . Moreover , in contrast to PAT-2 , the other integrin α subunit INA-1 and the engulfment receptor CED-1 , which signal through the conserved signaling molecules CED-5 ( DOCK180 ) /CED-12 ( ELMO ) or CED-6 ( GULP ) respectively , preferentially act in epithelial cells to mediate cell corpse removal during mid-embryogenesis . Our results show that different engulfing cells utilize distinct repertoires of receptors for engulfment at the whole organism level .
During the development of multicellular organisms , cells that are unnecessary , damaged , or harmful undergo programmed cell death ( apoptosis ) [1] . Apoptotic cells are recognized and subsequently internalized by engulfing cells [2] , [3] . Improper engulfment of apoptotic cells has been linked to diseases: too little engulfment may cause inflammation , autoimmune diseases , and cancers [4]–[6] , whereas too much engulfment has been implicated in degenerative diseases [7]–[9] . In flies and mammals , engulfment of apoptotic cells is mediated by “professional” phagocytes , such as mobile macrophages and dendritic cells , or by “amateur” phagocytes , such as muscle cells , epithelial cells , and endothelial cells [10]–[14] . Several mammalian receptors involved in apoptotic cell engulfment have been identified and characterized . Receptors such as BAI1 [15] , stabilin-1 [16] , stabilin-2 [17] , TIM-1 [18] , TIM-3 [19] , TIM-4 [18] , integrins [20] , [21] , and receptor tyrosine kinase Mer [22] bind to the “eat me” signal , externalized phosphatidylserine ( PS ) [23] , [24] , on the surface of apoptotic cells either directly [25] , [26] or through bridging molecules [27] , [28] . BAI1 , integrins , and Mer then signal through the conserved DOCK180/ELMO1/RAC GTPase signaling module to promote the internalization of apoptotic cells [15] , [29]–[31] , whereas stabilin-1 and stabilin-2 do so through the intracellular adaptor GULP [16] . Other “eat me” signal and receptor pairs for engulfment have been reported . For example , lectin receptors bind to altered sugars on apoptotic cells [32] , scavenger receptors to oxidized LDL-like moieties [33] , and CD14 to ICAM3 [34] . The in vivo role of most of these receptors in the clearance of apoptotic cells and the tissues in which they act at the whole organism level have not been defined . During the development of a C . elegans adult hermaphrodite , 1090 somatic cells are generated , 131 of which undergo apoptosis [35]–[37] . The apoptotic cells are removed by their neighboring cells [35] , [38] . Cell types such as hypodermal cells ( which constitute the external epithelium ) , pharyngeal muscle cells , and intestinal cells have been shown to function as engulfing cells [37] , [38] . Three partially redundant pathways that regulate the engulfment process have been identified . The first pathway is mediated by two cell-surface proteins CED-1 ( mammalian homologue MEGF10 ) and CED-7 ( ABCA1 ) [39] , [40] . CED-1 binds to an apoptotic cell through secreted molecule TTR-52 ( transthyretin ) and transduces the engulfment signal through the adaptor protein CED-6 ( GULP ) and DYN-1 ( dynamin ) to promote the engulfment and degradation of apoptotic cells [41]–[43] . The second pathway is regulated by at least three engulfment receptors , phosphatidylserine receptor PSR-1 [44] , Frizzled MOM-5 [45] , and integrin INA-1/PAT-3 [46] , all of which signal through the adaptor protein CED-2 ( CRKII ) and the bipartite GEF complex CED-5 ( DOCK180 ) /CED-12 ( ELMO ) for CED-10 ( RAC1 ) GTPase activation [47]–[52] . Phosphoinositide phosphatase MTM-1 ( myotubularin ) negatively regulates this pathway by inhibiting the recruitment of CED-12 to the plasma membrane [53] , [54] . These two engulfment pathways may converge at CED-10 GTPase , which promotes the actin-based cytoskeleton rearrangement required for phagocytosis of apoptotic cells in engulfing cells [55] . CED-10 activity is negatively regulated by GTPase activating protein SRGP-1 during the engulfment process [56] . Compared to these two major pathways , little is known about the third pathway , which is negatively regulated by the cytoskeletal regulator ABL-1 ( Abl ) , which inhibits the engulfment of apoptotic cells by inhibiting ABI-1 ( Abl-interacting protein ) and acts independently of CED-10 [57] . Integrins are transmembrane αβ heterodimers that make connections to the extracellular matrix and cytoskeleton and activate several signaling pathways required for multiple cellular processes , including cell adhesion , cell migration , and cell survival [58] , [59] . C . elegans has two integrin α subunits , INA-1 and PAT-2 , and a single β subunit , PAT-3 [60]–[62] . Integrin PAT-2/PAT-3 is a component of muscle attachment complexes and is essential for sarcomere assembly [63] , [65] and also acts to direct muscle arm extension [66] and distal tip cell migration [67] . We have recently shown that integrin INA-1/PAT-3 functions as an engulfment receptor for apoptotic cells [46] . Intriguingly , the pat-3 knockout mutant has a stronger defect in cell corpse engulfment than the ina-1 mutant [46] , raising the possibility that pat-2 may also mediate the removal of apoptotic cells . In this study , we examined and characterized the role of pat-2 in cell corpse engulfment and showed that it functions in the muscle-mediated internalization of apoptotic cells and acts through a pathway distinct from the previously known pathways .
pat-2 ( st567 ) mutants [64] and worms treated with pat-2 RNAi are embryonic lethal and show a phenotype of paralyzed arrest at the two-fold stage ( Pat ) , as PAT-2 plays an essential role in body wall muscle assembly and function during embryogenesis [63]–[65] . We tested the involvement of pat-2 in apoptosis by counting the number of apoptotic cells at the comma and 1 . 5-fold stages , the two stages at which the majority of embryonic apoptosis occurs [37] and pat-2 mutant embryos are still developing normally , and found that both pat-2 ( st567 ) and pat-2 ( RNAi ) embryos had a Ced ( cell death abnormal ) phenotype with increased numbers of apoptotic cells ( Table 1 ) . The Ced phenotype of the pat-2 ( st567 ) mutant was rescued by the transgene Ppat-2pat-2::gfp , in which the pat-2::gfp translational fusion construct is expressed under the control of the endogenous pat-2 promoter Ppat-2 ( Table 2 ) , confirming that the Ced phenotype of the pat-2 ( st567 ) mutant was specifically caused by pat-2 loss of function . The Ppat-2pat-2::gfp transgene also rescued the Pat phenotype of the pat-2 ( st567 ) mutant ( Table 3 ) . ced-3 ( n717 ) and ced-4 ( n1162 ) , strong mutations in the pro-apoptotic genes ced-3 and ced-4 that block almost all programmed cell death [68] , suppressed the phenotype of an increased number of cell corpses in pat-2 ( st567 ) or pat-2 ( RNAi ) embryos ( Table 1 ) , showing that the extra cell corpses observed in the pat-2 mutants were generated by programmed cell death . In contrast , the Pat phenotype of the pat-2 ( st567 ) or pat-2 ( RNAi ) mutants was not suppressed by either the ced-3 or ced-4 mutation ( Table 1 ) . The fact that the Pat and Ced phenotypes can be uncoupled shows they are probably due to the loss of different pat-2 functions . To determine the cause of the Ced phenotype of the pat-2 mutant , we performed a time-lapse differential interference contrast ( DIC ) microscopy analysis of cell corpses in the wild-type and pat-2 ( st567 ) mutant during embryogenesis prior to the 2-fold stage . We found that , although the timing and number of cell death events were similar in the two types of embryo ( Figure 1A ) , the length of time that the cell corpses persisted was significantly different ( Figure 1B ) . In the wild-type , approximately 97% of cell corpses disappeared within 40 minutes and no cell corpses persisted longer than 60 minutes , whereas , in the pat-2 embryo , more than 50% of the cell corpses persisted longer than 40 minutes and about 25% longer than 100 minutes . These results demonstrate that pat-2 functions in the clearance of cell corpses . In addition to PAT-2 , C . elegans has another integrin α subunit , INA-1 , which forms a complex with the single integrin β subunit , PAT-3 , on the cell surface [62] and is also required for the clearance of embryonic cell corpses [46] . RNAi inactivation of either ina-1 or pat-3 results in increased numbers of apoptotic cells at the comma and 1 . 5-fold stages [46] ( Table 1 ) . The pat-2 ( st567 ) mutation further increased the number of cell corpses in the ina-1 ( RNAi ) mutant , but not in the pat-3 mutant ( Table 1 ) . These results support the notions that PAT-2 , like INA-1 , acts together with PAT-3 during cell corpse clearance and that the two integrins , PAT-2/PAT-3 and INA-1/PAT-3 , function in a partially redundant fashion during the clearance process . We next determined whether pat-2 functioned together with previously identified genes to promote cell corpse removal . The two major pathways that regulate cell corpse removal are mediated , respectively , by ced-1 , ced-6 , and ced-7 or ced-2 , ced-5 , and ced-12 [2] . We therefore generated and analyzed double mutants containing either the pat-2 ( st567 ) or pat-2 ( RNAi ) mutation and strong loss-of-function or null alleles of the engulfment ced genes for the two pathways . Interestingly , the pat-2 ( st567 ) or pat-2 ( RNAi ) mutation further enhanced the engulfment defect in mutants defective in either pathway ( Table 4 ) . This suggests two possibilities . First , pat-2 could act in both pathways , with the combination of a pat-2 mutation and a defect in either pathway having an additive effect . Alternatively , pat-2 could act in a separate pathway that is partially redundant with these two pathways . To distinguish between these two possibilities , we tested whether the pat-2 ( st567 ) or pat-2 ( RNAi ) mutation could increase the engulfment defect in double mutants between the two pathways , such as the ced-1 ( e1735 ) ; ced-5 ( n1812 ) and ced-12 ( tp2 ) ; ced-7 ( n1892 ) double mutants . In the first case in which pat-2 would act in both pathways , we would expect that pat-2 ( st567 ) or pat-2 ( RNAi ) would not enhance the engulfment defect of the double mutants . However , we found that the pat-2 ( st567 ) or pat-2 ( RNAi ) mutation significantly increased the number of cell corpses in both double mutants ( Table 4 ) . We therefore conclude that pat-2 probably functions in a pathway distinct from these two pathways to promote the engulfment of cell corpses . A recent study has shown that abi-1 ( abl-1 interactor 1 ) acts in parallel to the two major engulfment pathways during cell corpse removal [57] . Our analysis showed that pat-2 ( RNAi ) or pat-3 ( st564 ) enhanced the engulfment defect in the abi-1 ( null ) or abi-1 ( RNAi ) embryo ( Table 4 ) , showing that pat-2 and pat-3 function independently of abi-1 to mediate the removal of apoptotic cells . We next examined the localization of PAT-2 using the Ppat-2pat-2::gfp or Ppat-2pat-2::mcherry transgene , which rescued the Ced and Pat phenotypes of the pat-2 mutant ( Table 3 and data not shown ) . PAT-2::GFP or PAT-2::mCherry was found to be expressed in body wall muscles and hypodermal cells during embryogenesis ( Figure 2A , 2B and Figure S2A ) . Notably , a strong GFP signal was observed along the surface of apoptotic cells adjacent to muscle cells ( Figure 2A and 2E ) , whereas a relatively weak GFP signal was seen around apoptotic cells engulfed by hypodermal cells ( Figure 2B and 2F ) . Our analysis of embryos expressing the transgene Ppat-2nls::gfp , in which GFP was expressed under the control of Ppat-2 and was predominantly localized to the nucleus , indicated that Ppat-2 expression was absent from cell corpses during embryogenesis ( Figure 2I , 2K , Figure S1 and Text S1 ) . Thus , the PAT-2::GFP signal surrounding apoptotic cells likely originated from the surface of the engulfing cells . The localization of PAT-2::GFP around the surface of apoptotic cells near body wall muscles was not affected by the ced-1;ced-5 double mutation ( Figure S3 ) , consistent with our genetic data showing that pat-2 acts in parallel to ced-1 and ced-5 to mediate cell corpse engulfment . When PAT-2::mCherry and PAT-3::GFP were co-expressed under the control of their respective endogenous promoters , PAT-2::mCherry and PAT-3::GFP were co-localized on apoptotic cells and to the dense bodies ( Z-disks ) and M-lines of muscle cells ( Figure S2A ) , in agreement with the idea that PAT-2 and PAT-3 form a complex . In contrast to flies and mammals , C . elegans does not have macrophage-like motile phagocytes; instead , apoptotic cells are removed by their neighboring cells [36] , [38] . Hypodermal cells , pharyngeal muscle cells , and intestinal cells have been shown to function as engulfing cells [37] , [38] . Since pat-2 was expressed in both hypodermal cells and body wall muscles , we tested whether it functioned in muscle cells and/or hypodermal cells for the removal of apoptotic cells . To this end , we expressed pat-2 cDNA under the control of the Pajm-1 or Punc-54 promoter and examined the ability of each transgene to rescue the Ced phenotype of the pat-2 mutant . Pajm-1 is expressed in all epithelia including hypodermal cells [69] , whereas Punc-54 is expressed in body wall muscles [70] . We found that expression of pat-2 by Punc-54 , but not by Pajm-1 , fully rescued the Ced phenotype of the pat-2 ( st567 ) embryo ( Table 2 ) . Punc-54 did not appear to express in apoptotic cells ( Figure S4A ) . These results support the notion that pat-2 acts in muscle cells to mediate cell corpse removal during embryogenesis . We next co-expressed PAT-2::mCherry and PAT-2::GFP by the Ppat-2 and Punc-54 promoters , respectively , to monitor the PAT-2-mediated and muscle-mediated engulfment processes simultaneously in the 1 . 5-fold embryos . Approximately 22 . 7% and 18 . 1% of the cell corpses were enclosed by the PAT-2::mCherry and PAT-2::GFP circles , respectively , and most of the mCherry and GFP circles were co-localized , suggesting that most , if not all , PAT-2-mediated engulfment involves muscle cells ( Table S1 ) . CED-1 and INA-1/PAT-3 are engulfment receptors that are expressed in multiple cell types , including hypodermis and body wall muscles [40] , [46] , but act in pathways in parallel to that involving PAT-2 ( Table 1 and Table 4 ) . We therefore tested whether CED-1 and INA-1 acted in specific cell types to mediate the engulfment of apoptotic cells . Expression of ced-1 in hypodermal cells using the Pajm-1 promoter fully rescued the ced-1 engulfment defect , whereas expression of ced-1 in body wall muscles using the Punc-54 promoter did not ( Table 2 ) . Similarly , expression of ina-1 in hypodermal cells , but not muscle cells , rescued the ina-1 engulfment defect ( Table 2 ) . These data suggest that CED-1 and INA-1 preferentially act in hypodermal cells , at least during the comma and 1 . 5-fold stages . This observation , together with our result that PAT-2 predominantly functions in muscle cells , indicates that different engulfing cells may utilize different engulfment receptors to mediate cell corpse removal . The mesodermal MSpppaaa cell is generated in the head region about 250 minutes after the first cleavage of a zygote [37] . After circumferential migration to the dorsal midline , the MSpppaaa cell is located near the anterior dorsal muscle cells . Approximately 400 minutes after its generation , the MSpppaaa cell undergoes apoptosis at the four-fold stage [37] . To examine whether the apoptotic MSpppaaa cell was removed by a muscle cell , we expressed membrane-bound monomeric red fluorescent protein ( mRFP ) in muscle cells using Punc-54 . MSpppaaa cell corpses were found inside the adjacent muscle cells ( Figure 2J and 2K ) , showing that MSpppaaa cell corpses were engulfed by muscle cells . To confirm this result , we tagged PAT-2 with mCherry and GFP and co-expressed PAT-2::mCherry in body wall muscles using Punc-54 and PAT-2::GFP in hypodermal cells using Pajm-1 in wild-type embryos . The PAT-2::mCherry , but not PAT-2::GFP , signal was observed around apoptotic MSpppaaa cells ( Figure S5 ) , confirming that apoptotic MSpppaaa cells are engulfed by muscle cells , but not hypodermal cells . The basement membrane between muscle and hypodermis may limit the access of hypodermal cells to the apoptotic MSpppaaa cell for engulfment . We next examined whether the clearance of apoptotic MSpppaaa cells is defective in pat-2 embryos by scoring apoptotic MSpppaaa cells in wild-type and pat-2 embryos at specific embryonic stages . The two stages during which the pharyngeal grinder has just formed and the pharynx is pumping were chosen . The MSpppaaa cell undergoes apoptosis during pharyngeal grinder formation . Pharyngeal pumping begins about 1 . 5 hours after grinder formation is complete . At the time when pharyngeal grinder formation had just finished , only 19 . 6% of wild-type embryos contained the MSpppaaa cell corpse and none remained at the time when pharyngeal pumping occurred ( Table 5 ) . However , 74 . 6% of pat-2 embryos contained the MSpppaaa cell corpse at the time when pharyngeal grinder formation had just finished , and 52 . 4% still contained a corpse when pharyngeal pumping had started ( Table 5 ) . This shows that pat-2 is important for the removal of apoptotic MSpppaaa cells . In addition , the muscle-specific expression of pat-2 by Punc-54 significantly reduced the percentage of pat-2 mutant embryos containing the MSpppaaa cell corpse at the time when pharyngeal grinder formation had just finished or pharyngeal pumping had started ( Table 5 ) . In contrast , the hypodermal cell-specific expression of pat-2 by Pajm-1 failed to do so ( Table 5 ) . These results show that pat-2 functions in muscle cells to mediate the removal of the MSpppaaa cell corpse . Three cells C1 , C2 and C3 , called as in reference [42] , are generated in the ventral side of embryos at approximately 300–350 min after first cleavage and subsequently undergo apoptosis . Their cell corpses are engulfed by the ventral hypodermal cells ABplaapppp , ABpraapppa and ABplaapppa , respectively [42] . The duration time of these apoptotic cells appeared normal in the pat-2 mutants ( Table S2 ) . This result further supports the notion that pat-2 preferentially acts in muscle cells but not hypodermal cells for cell-corpse removal . In contrast to pat-2 , ced-1 has been previously shown to be important for the hypodermal cell-mediated removal of C1 , C2 and C3 cell corpses [42] . In the ced-1 mutants , the duration time of MSpppaaa cell corpses was slightly longer than that of the wild-types: approximately 19% of MSpppaaa cell corpses still persisted in the ced-1 mutants during pharyngeal pumping , whereas none remained in the wild-type embryos at this stage ( Table 5 ) . Thus , ced-1 also plays a minor role in the muscle-mediated removal of apoptotic MSpppaaa cells in late embryogenesis . The persistence of cell corpses in the pat-2 mutant could be due to a defect in either the internalization or the degradation of the corpse . We used the Punc-54myri::mrfp translational reporter to express the MYRI::mRFP fusion protein on the surface of muscle cells and followed the membrane processes of a muscle cell around an apoptotic cell using the time-lapse fluorescence microscopy analysis . The comma stage was chosen because the pat-2 embryos at this stage show an increased number of cell corpses and the embryos at this stage do not move around . In wild-type embryos , MYRI::mRFP fusion protein appeared to localize to the growing pseudopods , which eventually formed a circle around an apoptotic cell upon the completion of the internalization process ( Figure 3A ) . The MYRI::mRFP circle formed around an apoptotic cell in approximately 6 minutes . However , in the pat-2 ( st567 ) mutant embryos , the MYRI::mRFP circle formation took approximately 21 minutes to complete , more than three times longer than that in the wild-type , indicating that the internalization process was compromised . We further examined the internalization of MSpppaaa cell corpses in the pat-2 mutant using the Punc-54myri::mrfp transgene . As shown in Table 5 and Figure 3B , in wild-type embryos , all apoptotic MSpppaaa cells showed the MYRI::mRFP circle at the time when grinder formation had just finished and their cell corpses were cleared at the time of pharyngeal pumping . However , in pat-2 mutants , no apoptotic MSpppaaa cells had the MYRI::mRFP circle at the time when grinder formation had just finished , and nearly half of MSpppaaa cell corpses still persisted and showed no MYRI::mRFP circle at the time of pharyngeal pumping ( Table 5 ) . These data indicate that pat-2 is required for the internalization of an apoptotic MSpppaaa cell by a muscle cell . We then examined whether PAT-2 recognized apoptotic cells to trigger their internalization . To this end , we generated the transgene Phsppat-2 ( ex ) ::mcherry , in which the coding sequence for the PAT-2 extracellular domain with a signal sequence [PAT-2 ( ex ) ] was fused to that of mCherry under the control of the heat-shock promoter Phsp . The transgene was then introduced into the wild-type and ced-1 ( e1735 ) ; ced-5 ( n1812 ) double mutant embryos . The ced-1 ( e1735 ) ; ced-5 ( n1812 ) double mutant embryos contain many persistent apoptotic cells , resulting in a greater chance of seeing PAT-2 ( ex ) ::mCherry binding , especially during late embryogenesis when very few cell corpses are present in the wild-type embryos . We found that secreted PAT-2 ( ex ) ::mCherry clustered on the surface of apoptotic cells ( Figure 2M ) , albeit with a weaker fluorescence intensity compared to that of PAT-2::GFP ( Figure 2A ) . Approximately 15 . 2% of apoptotic cells displayed the PAT-2 ( ex ) ::mCherry circle in the ced-1 ( e1735 ) ; ced-5 ( n1812 ) double mutant embryos at the 4-fold stage . A similar percentage ( 16 . 6% ) was observed in the wild-type embryos at the 1 . 5-fold stage ( Table S1 ) . Thus , PAT-2 ( ex ) recognizes and binds to the surface of apoptotic cells . However , we do not know if binding is direct or indirect . We further examined whether PAT-2 ( ex ) ::mCherry bound to specific apoptotic cells . Approximately 20% of the MSpppaaa cell corpses had a PAT-2 ( ex ) ::mCherry circle ( Table S1 and Figure S6A ) , but none of C1 , C2 and C3 cell corpses , which are engulfed by hypodermal cells , had a PAT-2 ( ex ) ::mCherry circle ( Table S1 and Figure S6C and S6D ) . This result indicates that PAT-2 ( ex ) binds to specific apoptotic cells and may explain why PAT-2 is required for the removal of MSpppaaa ( Table 5 ) , but not C1 , C2 or C3 , cell corpses ( Table S2 ) and why only a subset , but not all , of apoptotic cells are labeled with PAT-2 ( ex ) ::mCherry ( Figure 2L and 2M ) . Furthermore , we used the Punc-54 promoter to express PAT-2 ( ex ) ::mCherry and observed PAT-2 ( ex ) ::mCherry clustering around apoptotic cells including the apoptotic MSpppaaa cell ( Figure S4B ) , despite the fact that the PAT-2 ( ex ) ::mCherry signal was not as strong as that expressed by the Phsp promoter . This result shows that PAT-2 ( ex ) ::mCherry expressed in and secreted from muscle cells recognizes , and binds to , the surface of apoptotic cells . Embryos with PAT-2 ( ex ) ::mCherry overexpression by either Phsp or Punc-54 resulted in increased numbers of apoptotic cells at the comma and 1 . 5-fold stages ( Table 3 ) and delayed the removal of MSpppaaa cell corpses ( Figure S6A ) , indicating that PAT-2 ( ex ) ::mCherry overexpression interferes with the normal process of cell corpse engulfment . The INA-1 extracellular domain , termed INA-1 ( N ) as in reference [46] , has been shown to recognize apoptotic cells [46] . To test whether PAT-2 and INA-1 may recognize the same apoptotic cells , we co-expressed PAT-2 ( ex ) ::mCherry and INA-1 ( N ) ::GFP in embryos using the transgenes Phsppat-2 ( ex ) ::mcherry and Phsp ina-1 ( N ) ::gfp . We found that PAT-2 ( ex ) ::mCherry was co-localized with INA-1 ( N ) ::GFP on some apoptotic cells ( Figure S6E ) . Thus , the extracellular domains of PAT-2 and INA-1 can recognize identical apoptotic cells , although they preferentially function in different cell types for apoptotic cell removal . As shown above , PAT-2 binds to apoptotic cells and may serve simply to tether the corpse to an engulfing cell or also initiate a signaling pathway for engulfment . To distinguish between these two possibilities , we tested whether the cytoplasmic domain of PAT-2 was essential for signaling by deleting it and examining the ability of truncated PAT-2Δcyto to rescue pat-2 ( st567 ) mutants . We generated pat-2 ( st567 ) ; Ppat-2pat-2Δcyto::gfp transgenic animals that expressed the PAT-2Δcyto::GFP fusion protein under the control of the pat-2 promoter . To our surprise , PAT-2Δcyto::GFP fully rescued the Pat phenotype of the pat-2 ( st567 ) mutant , but failed to rescue the Ced phenotype ( Table 3 ) . The pat-2 ( st567 ) ; Ppat-2pat-2Δcyto::gfp transgenic embryos contained increased numbers of cell corpses ( Table 3 ) at comma and 1 . 5-fold stages , and some of the cell corpses persisted longer than those of the wild-type embryos ( Figure 1B ) , showing an engulfment defect . The non-Pat phenotype of the transgenic embryos allowed us to count cell corpses at and beyond the 2-fold stage , which is impossible for homozygous pat-2 ( st567 ) embryos because of developmental arrest . The pat-2 ( st567 ) embryos carrying the Ppat-2 pat-2Δcyto::gfp transgene also had increased numbers of cell corpses at the 2- , 3- and 4-fold stages ( Table S3 ) . Thus , pat-2 is required for the engulfment of apoptotic cells throughout embryogenesis . In addition , MSpppaaa cell corpses also persisted longer in the pat-2 ( st567 ) ; Ppat-2 pat-2Δcyto::gfp transgenic embryos than those in wild-type embryos . Approximately 61% of the MSpppaaa cell corpses still persisted at the stage of pharyngeal pumping , while none remained in the wild-type embryos at this stage ( Table 5 ) . PAT-2Δcyto::GFP was localized to the surfaces , dense bodies and M-lines of muscle cells in the wild-type and pat-2 mutants ( Figure S7 ) , similar to PAT-2::GFP or PAT-2:: mCherry ( Figure S2 , S7 and data not shown ) , suggesting that the cytoplasmic domain is not required for PAT-2 localization . We then used the PAT-2Δcyto::GFP signal to monitor the internalization of MSpppaaa cell corpses by muscle cells . In the wild-type embryos , approximately 66 . 6% of MSpppaaa cell corpses were enclosed by the PAT-2Δcyto::GFP circle at the time when grinder formation had just finished , whereas no MSpppaaa cell corpses were enclosed by the PAT-2Δcyto::GFP circle in the pat-2 ( st567 ) embryos at this stage ( Table 5 and Figure S8 ) . Thus , the internalization of MSpppaaa cell corpses is defective in the pat-2 ( st567 ) ; Ppat-2pat-2Δcyto::gfp embryos . This result and the aforementioned data together indicate that the PAT-2 cytoplasmic domain is required for the muscle-mediated engulfment of apoptotic cells , but is dispensable for its subcellular localization in muscle cells and function during muscle development . Expression of the PAT-2Δcyto::GFP fusion protein in the wild-type embryos resulted in increased numbers of cell corpses at the comma and 1 . 5-fold stages comparable to those seen in pat-2 mutants , but failed to induce the Pat phenotype ( Table 3 ) . In addition , PAT-2Δcyto::GFP also prolonged the duration time of the MSpppaaa cell corpses and interfered with the internalization of the MSpppaaa cell corpses ( Table 5 ) . For example , in the wild-type embryos expressing PAT-2Δcyto::GFP , 39% of the MSpppaaa cell corpses persisted at the time when grinder formation had just finished , and 66 . 6% of these cell corpses were internalized by engulfing muscle cells . However , in the control wild-type embryos , only 16 . 6% of the MSpppaaa cell corpses were present at this stage , and all the remaining cell corpses were internalized by engulfing muscle cells ( Table 5 ) . This result reinforces the specific function of the PAT-2 cytoplasmic domain in the engulfment of apoptotic cells . PAT-2Δcyto::GFP may compete with the endogenous PAT-2 for binding of PAT-3 or apoptotic cells , but fail to initiate signaling for the internalization of cell corpses . During the engulfment of apoptotic cells , cytoskeletal rearrangement occurs as an engulfing cell extends pseudopods around an apoptotic cell [71] . Rho-family GTPases are important regulators of the actin cytoskeleton [72] . Although CED-10 ( RAC1 ) GTPase is required for cell corpse engulfment [55] , [72] , our data showing that a ced-10 mutation enhanced the engulfment defect in the pat-2 ( RNAi ) mutant ( Table 4 ) and that ced-10 overexpression failed to rescue the engulfment defect of the pat-2 ( RNAi ) mutant ( Table 6 ) suggest that pat-2 may act independently of ced-10 in promoting the cytoskeletal rearrangement required for the internalization process . We then examined whether actin filament assembly occurred during the muscle-mediated internalization of apoptotic cells . MOESIN has been used in Drosophila and C . elegans to specifically mark the filamentous form of actin [73] , [74] . Analysis of embryos expressing Punc-54moesin::gfp in which MOESIN::GFP was expressed in muscle cells using Punc-54 showed a MOESIN::GFP circle around apoptotic cells ( Figure 2C ) , demonstrating that filamentous actin assembly occurs as an engulfing muscle cell extends pseudopods along the surface of an apoptotic cell . A previous study showed that the Rho-family GTPase CDC-42 and UIG-1 , a guanine nucleotide exchange factor ( GEF ) specific for CDC-42 , function downstream of PAT-2/PAT-3 signaling for muscle assembly [75] . We next tested the involvement of uig-1 and cdc-42 in PAT-2-mediated cell corpse engulfment . The cdc-42 ( gk388 ) allele has a deletion that eliminates part of the 5′ regulatory sequence , the entire first exon , and part of the first intron of the cdc-42 gene ( C . elegans Gene Knockout Consortium ) , while the uig-1 ( ok884 ) mutation deletes the region coding for the DH/PH domain , which has the GEF activity [76] . Both alleles are likely to be null . We found that uig-1 or cdc-42 embryos contained increased numbers of cell corpses at the comma and 1 . 5-fold stages ( Table 4 ) , similar to those observed in the pat-2 mutants . A four-dimensional DIC analysis of cell corpse persistence in the cdc-42 ( gk388 ) embryos revealed a cell-corpse engulfment defect . In the wild-type , 97% of cell corpses disappeared within 40 minutes and none lasted longer than 60 minutes ( Figure 1C ) . However , in the cdc-42 ( gk388 ) mutants , only 40% of the cell corpses disappeared within 40 minutes and almost 40% persisted longer than 60 minutes ( Figure 1C ) . We next tested whether cdc-42 acted in the same pathway as pat-2 to promote apoptotic cell engulfment . We found that neither cdc-42 nor uig-1 increased the number of cell corpses in pat-2 mutants at the comma and 1 . 5-fold stages ( Table 4 ) , suggesting that cdc-42 and uig-1 both act in the same genetic pathway as pat-2 . Like PAT-2::GFP , the GFP::CDC-42 fusion protein expressed using the transgene Punc-54gfp::cdc-42 was localized in muscle pseudopods around apoptotic cells ( Figure 2D ) . In addition , the transgene Punc-54gfp::cdc-42 rescued the engulfment defect of the cdc-42 ( gk388 ) mutants , whereas Pajm-1gfp::cdc-42 , which expressed GFP::CDC-42 in hypodermal cells , did not ( Table 6 ) . This result suggests that cdc-42 acts in muscle cells to mediate the engulfment of apoptotic cells . This muscle-specific function of cdc-42 is further supported by the observation that loss of cdc-42 results in a defect in the engulfment of apoptotic MSpppaaa cells ( Table 5 ) , but not C1 , C2 or C3 cells ( Table S2 ) . When Punc-54gfp::cdc-42 and Ppat-2pat-2::mcherry were co-expressed , GFP::CDC-42 was co-localized with PAT-2::mCherry along the surface of apoptotic MSpppaaa cells ( Figure 2N–2Q ) . Furthermore , the Punc-54gfp::cdc-42 transgene rescued the engulfment defect of the pat-2 ( RNAi ) embryos ( Table 6 ) . These results support the model that cdc-42 functions downstream of pat-2 in the muscle-mediated engulfment of apoptotic cells . Our genetic data suggested that cdc-42 acts with pat-2 in the same pathway , whereas ced-10 functions in a partially redundant manner with pat-2 to mediate cell corpse engulfment ( Table 4 ) . We then tested whether the functions of ced-10 and cdc-42 in engulfment were exchangeable when ubiquitously overexpressed . We found that overexpression of ced-10 or constitutively active ced-10 V12 by the heat-shock promoter Phsp , which rescued the ced-10 ( n1993 ) or ced-10 ( tm597 ) mutant ( [50] , Table 6 ) , failed to rescue the engulfment defect of the pat-2 ( RNAi ) embryos ( Table 6 ) . Reciprocally , overexpression of cdc-42 by Phsp , which rescued the pat-2 mutant ( Table 6 ) , did not rescue the engulfment defect of the ced-10 ( n3246 ) mutants ( Table 6 ) . Moreover , overexpression of cdc-42 by the Phsp or Pajm-1 promoter also failed to rescue the defective engulfment of the ced-1 ( e1735 ) or ina-1 ( gm144 ) mutants , respectively ( Table 6 ) . These data suggest that the mechanisms by which cdc-42 and ced-10 mediate the actin-based cytoskeletal rearrangement during cell-corpse engulfment are distinct and that their functions are not exchangeable . As shown in Figure S6E , the INA-1 and PAT-2 extracellular domains can recognize the same apoptotic cells . We found that overexpression of pat-2 by the Pajm-1 promoter in hypodermal cells partially rescued the defective engulfment of the ina-1 mutants and , reciprocally , overexpression of ina-1 by the Punc-54 promoter in muscle cells also partially rescued the engulfment defect of the pat-2 mutants ( Table 2 ) . This result indicates that ina-1 and pat-2 can partially substitute for each other in cell corpse engulfment . Interestingly , overexpression of ina-1 by the Punc-54 promoter , which partially rescued the engulfment defect of the pat-2 mutants , did not rescue that of the cdc-42 ( gk388 ) mutants ( Table 6 ) suggests that cdc-42 may be necessary for ina-1 overexpression-induced engulfment in muscle cells . If so , ina-1 overexpression in muscle cells may promiscuously activate cdc-42 signaling , which , in turn , leads to the engulfment of apoptotic cells . Nevertheless , ina-1 overexpression does not efficiently activate the phagocytosis machinery for cell corpse removal in muscle cells since only partial rescue of the pat-2 engulfment defect by ina-1 overexpression was observed . Similarly , overexpression of pat-2 dose not efficiently induce the phagocytosis machinery in hypodermal cells . This may , in part , explain why pat-2 and ina-1 preferentially function in different cell types for cell corpse removal , despite that they are expressed in both cell types . In contrast , overexpression of ced-1 under the control of the Punc-54 promoter failed to rescue the defective engulfment of the pat-2 mutants ( Table 2 ) . In a reciprocal experiment , overexpression of pat-2 by the Pajm-1 promoter also failed to rescue the defective engulfment of the ced-1 mutants ( Table 2 ) . These results indicate that ced-1 and pat-2 functions are distinct and not exchangeable in cell corpse engulfment .
The engulfment of apoptotic cells requires the recognition and subsequent internalization of apoptotic cells by the engulfing cells . Here , we showed that , in C . elegans , the integrin α subunit PAT-2 functions in both the recognition and internalization steps . pat-2 loss of function resulted in an increased number of embryonic cell corpses due to a defect in cell corpse removal . Our data showed that PAT-2 bound to apoptotic cells through its extracellular domain and initiated downstream signaling via its cytoplasmic domain . We characterized the pat-2 signaling pathway and identified a previously unassigned function of cdc-42 and uig-1 in cell corpse engulfment . We further showed that PAT-2 predominantly functioned in muscle cells to mediate the engulfment process . We propose that binding of PAT-2 to an apoptotic cell results in the recruitment of UIG-1 and the subsequent activation of CDC-42 GTPase , which , in turn , regulates cytoskeletal rearrangement as a muscle cell extends pseudopods around an apoptotic cell . The finding that truncated PAT-2 lacking the cytoplasmic domain ( PAT-2Δcyto::GFP ) fully rescued the Pat phenotype of the pat-2 mutants , but failed to rescue the engulfment defect ( Table 3 ) argues against the possibility that the engulfment defect of the pat-2 mutant is a secondary effect caused by abnormality of muscle assembly or organization during embryogenesis . PAT-2 is co-localized with PAT-3 to the dense bodies and M-lines ( Figure S2A ) , which are platforms serving as anchoring sites for signaling/adapter proteins in muscle attachment and organization [65] . Because the deletion of the cytoplasmic domain of PAT-2 affects neither its localization pattern ( Figure S7 ) nor its function in muscle development ( Table 3 ) , muscle development probably requires only the transmembrane and extracellular domains , which are likely sufficient for the interaction of PAT-2 with PAT-3 and/or the extracellular matrix . Thus , the PAT-2/PAT-3 integrin likely mediates the intracellular signaling and/or adaptor protein binding through the cytoplasmic domain of PAT-3 , but not that of PAT-2 , for muscle development . In contrast , the requirement of the cytoplasmic domain of PAT-2 for cell-corpse removal suggests that this domain is important for signaling and/or serves as an anchorage site for adapter proteins during cell-corpse internalization . The pat-2 ( st567 ) mutants show a weak engulfment phenotype compared with that of the ced-1 mutants during embryogenesis ( Table 2 ) . One possible explanation for the weak engulfment defect is that pat-2 predominantly functions in muscle cells , while only a small fraction of cell corpses ( e . g . ∼20% of cell corpses in the 1 . 5-fold stage embryos , as shown in Table S1 ) are removed by muscle cells . Secondly , other engulfment receptor ( s ) may act redundantly with pat-2 in muscle-mediated engulfment . For instance , both PAT-2 and CED-1 are involved in the muscle-mediated internalization of MSpppaaa cells , despite that PAT-2 plays a bigger role than CED-1 ( Table 5 ) . The PAT-2 extracellular region bound to apoptotic cells when PAT-2 ( ex ) was fused to mCherry and overexpressed using the heat shock promoter ( Figure 2M ) . Because exposed PS is detected on the surface of apoptotic MSpppaaa cells ( Figure S1B ) , which are then removed by the PAT-2-mediated engulfment process ( Table 5 ) , PAT-2 might recognize exposed PS on apoptotic MSpppaaa cells . Mammalian integrins αvβ3 and αvβ5 have been shown to bind to apoptotic cells via the secreted bridging molecule MFG-E8 [25] , [48] . In addition , integrin αvβ3 binds synergistically with the cell-surface protein CD36 to apoptotic cells through the bridging molecule thrombospondin , an extracellular matrix glycoprotein [50] . MFG-E8 binds to integrin αv through its RGD domain . On the basis of amino acid sequence , PAT-2 is more closely related to the RGD-binding integrins than to the laminin-binding integrins . However , C . elegans does not appear to have an MFG-E8 homolog . A screen for RGD-containing molecules may be helpful in testing the involvement of RGD-containing molecules in the binding of PAT-2 ( ex ) ::mCherry to apoptotic cells . Like PAT-2 , CED-1 and INA-1 are expressed in muscle and hypodermal cells [40] , [46] . It is intriguing that different receptors are preferentially used in different cell types when they are all present in these cells . This may be , in part , because some receptors preferentially bind to a subset , but not all , apoptotic cells . For example , PAT-2 ( ex ) ::mCherry binds to MSpppaaa , but not C1 , C2 or C3 , cell corpses ( Figure S6A , S6C and S6D ) and PAT-2 is required for the removal of MSpppaaa , but not C1 , C2 or C3 , cell corpses . In contrast , INA-1 and CED-1 receptors recognize apoptotic C1 , C2 and C3 cells and mediate the engulfment of these cells [42] , [46] . However , some cell corpses can be recognized by multiple receptors . For example , PAT-2 ( ex ) ::mCherry and INA-1 ( N ) ::GFP are co-localized on some apoptotic cells ( Figure S6E ) . Therefore , additional factor ( s ) other than the receptor-cell corpse interaction determines the cell-type specificity of engulfment receptors . The observation that CDC-42 preferentially functions in muscle cells but not hypodermal cells for engulfment suggests that the downstream molecule ( s ) are important for the cell-type specificity of engulfment receptors . CED-10 and CDC-42 are important for the actin-based cytoskeleton rearrangement [72] , which occurs during engulfment of apoptotic cells ( [55] , Figure 2C ) . Hypodermal cells and muscle cells appear to have different requirement for CED-10 and CDC-42 in cell corpse removal , although the two GTP ases are expressed in both cell types [76] . Hypodermal cells and muscle cells may utilize different actin-based phagocytosis mechanisms or employ different ways to activate the phagocytosis machinery for cell corpse removal . We showed that the muscle-mediated internalization of cell corpses took apporximately 6 mintues ( Figure 3A ) . Similarly , the internalization of the C3 cell corpse by the hypodermal cell ABplaapppp took about 6 minutes [42] , [46] . However , previous studies by Wang et al . and Zou et al . [43] , [53] showed that the time for the CED-1::GFP-mediated internalization of apoptotic cells may take about 18 or 25 minutes , respectively , although the identities of the engulfing cells are unclear . Therefore , the time necessary for the entire internalization process to occur from initiation to completion appears different for different engulfing cells . Nonetheless , at least some engulfment processes mediated by the muscle cell and hypodermal cell proceeds with similar kinetics . Recently , conditional deletion of integrin αv in the mouse immune system revealed that this protein is essential for the engulfment of apoptotic cells by gut-associated macrophages and dendritic cells [4] . In addition , mice lacking MFG-E8 , which mediates apoptotic cell clearance through integrin αv , are defective in the removal of apoptotic B cells by tingible body macrophages in the spleen germinal centers [77] . However , little was previously known about whether integrin α or other engulfment receptors were involved in apoptotic cell removal mediated by amateur phagocytes , such as muscle cells . C . elegans provides a good system for studying the amateur phagocyte-mediated engulfment of apoptotic cells , as it does not have professional phagocytes . Our data showed that PAT-2 acts in muscle cells and transduces the engulfment signal through a novel signaling pathway for apoptotic cell removal . Recently , a mouse lacking ELMO1 showed a defect in Sertoli cell-mediated engulfment of apoptotic germ cells , but no engulfment defect was detected in macrophages or fibroblasts [31] . This result , together with our data , suggest that , like professional phagocytes [78] , amateur phagocytes in different tissues utilize different sets of engulfment receptors and signaling molecules for apoptotic cell engulfment at the whole organism level .
The N2 Bristol strain was used as the reference wild-type strain . All strains were maintained on nematode growth medium ( NGM ) plates with Escherichia coli OP50 as the food source at 20°C unless otherwise noted [79] . The following mutations were used: linkage group I ( LGI ) , ced-1 ( e1735 ) , ced-12 ( n3261 , tp2 ) ; LGII , cdc-42 ( gk388 ) , mIn1[mIs14 dpy-10 ( e128 ) ]; LGIII , abi-1 ( ok640 ) , ced-4 ( n1162 ) , ced-6 ( n1813 ) , ced-7 ( n1892 , n1996 ) , unc-79 ( e1068 ) , pat-2 ( st567 ) , dpy-17 ( e164 ) , pat-3 ( st564 ) , qC1 dpy-19 ( e1259 ) glp-1 ( q339 ) ; LGIV , ced-2 ( n1994 ) , ced-3 ( n717 ) , ced-5 ( n1812 ) , ced-10 ( n1993 , n3246 , tm597 ) ; LGIV , uig-1 ( ok884 ) . dpy-17 ( e164 ) was used to balance unc-79 ( e1068 ) pat-2 ( st567 ) . The homozygous unc-79 ( e1068 ) pat-2 ( st567 ) mutants were also maintained using the extrachromosomal Ppat-2pat-2::gfp or Ppat-2pat-2::mcherry transgene . In either of the transgenic lines , the unc-79 ( e1068 ) pat-2 ( st567 ) mutant embryos that lost the transgene were both Pat and Ced . The numbers of cell corpses in the unc-79 ( e1068 ) mutant at the comma and 1 . 5-fold stages were similar to those of the wild-type at the same stage , indicating that unc-79 ( e1068 ) does not affect apoptosis . qC1 dpy-19 ( e1259 ) glp-1 ( q339 ) was used to balance pat-3 ( st564 ) . mIn1 was used to balance cdc-42 ( gk388 ) . The pat-2 ( st567 ) allele has a G441D alternation in the extracellular domain . The information of the transgenic strains used in this work is listed in Table S4 . Three vectors were used to generate constructs expressing fluorescent proteins , the gfp vector pPD95 . 75 , the mcherry vector pYW806 , and the mrfp vector pYW897 . pYW806 and pYW897 were respectively generated by replacing gfp in pPD95 . 75 with mcherry or mrfp via the KpnI/EcoRI sites . To generate Ppat-2pat-2::gfp ( pYW903 ) or Ppat-2pat-2::mCherry ( pYW950 ) , the 10 kb fragment containing the 4 kb upstream sequence and the full-length pat-2 coding region without the stop codon was PCR-amplified from the cosmid F54F2 ( Sanger Institute , Cambridge , United Kingdom ) using the primers TCCCCCCGGGTTTATGACTCACAGAC and GGGTACCGATGCATTTGTC CGTGACGT , and cloned into pPD95 . 75 or pYW806 , respectively , via the KpnI site . The full-length pat-2 cDNA construct ( pYW901 ) was generated by inserting into yk616b4 ( Dr . Yuji Kohara ) via the PstI site a 0 . 6 kb pat-2 cDNA which was amplified by RT-PCR using the primers AACTGCAGATGCGAGAGGGTAGTTTTCC and GATTCTTCTTTCCTGGAACTGCAGC . To generate Ppat-2nls::gfp ( pYW949 ) or Ppat-2gfp ( pYW903 ) , the 4 kb upstream sequence of pat-2 was first amplified by PCR from the cosmid F54F2 using the primers TCCCCCCGGGTTTATGACTCACAGAC and TCCCCCCGGGATCTACTGG AAATTTG and inserted into pPD95 . 67 or pPD95 . 75 , respectively . The Punc-54gfp ( pYW899 ) or Punc-54mcherry ( pYW900 ) construct was generated by inserting the 1 kb HindIII/KpnI fragment of pPD30 . 38 containing Punc-54 into pPD95 . 75 or pYW806 , respectively . The Pajm-1gfp ( pYW902 ) was generated by inserting into pPD95 . 75 via the SalI/BamHI sites a 5 . 5 kb Pajm-1 fragment which was amplified by PCR from pBR980 [69] using the primers CGTCGACCGATTTGACCGTTCGATAAG and CGGATCCTCGTCGGTA GTACTCGTCC . Pced-1gfp ( pYW898 ) was generated by inserting into pPD95 . 75 via the SphI site a 5 kb Pced-1 fragment which was PCR-amplified from genomic DNA using the primers GGCATGCATACCTCCTGATATG GGGTGA and GCATGCTTGC GGCTGCAAAAAAACAGGG . Pced-1ced-1::gfp ( pYW904 ) was generated by three-piece ligation . The 6 kb PCR-amplified fragment from Pced-1ced-1::gfp [40] using the primers AGGTACCATGCGTCTCATTCTCCTTGTGC and GGTCGA CGTGATTGTTCAGATGA and the 2 . 4 kb fragment PCR-amplified from genomic DNA using the primers CGTCGACCTCTATTAGAAGAGCATGACG and TGGTACCGAGGTGTACAAATTGTCCTGAGC were inserted into pYW898 via the SalI and KpnI sites . Pced-1ced-1::gfp fully rescued the engulfment defect of the ced-1 ( e1735 ) mutant ( data not shown ) . To generate pYW901 containing pat-2 cDNA without the stop codon , pat-2 cDNA was PCR-amplified from the full-length pat-2 cDNA clone using the primers CGGTACCATGCGAGAGGGTAGTTTTCC and GGGTACCGATAGCATTTGTC CGTGACGT and inserted into the pGEM-T Easy vector ( Promega ) via the KpnI site . To generate Punc-54pat-2::gfp ( pYW913 ) or Punc-54pat-2::mCherry ( pYW916 ) , the 3 . 7 kb KpnI pat-2 fragment from pYW901 was inserted into pYW899 or pYW900 , respectively , via the KpnI site . To generate Punc-54ced-1::gfp ( pYW905 ) , the 8 . 5 kb KpnI ced-1 fragment from pYW904 was inserted into pYW899 via the KpnI site . To generate Punc-54 ced-1::mrfp ( pYW941 ) , a 8 . 6 kb SpeI/BamHI fragment , containing Punc-54 and the first 4 . 9 Kb of ced-1 , and a 3 . 6 kb BamHI/KpnI fragment , corresponding to the rest ced-1 sequence , of pYW905 were fused to a 1 . 5 kb KpnI/SpeI fragment , containing the mrfp sequence , of pYW897 by three-piece-ligation . To generate Punc-54moesin::gfp ( pYW940 ) , the 0 . 6 kb SmaI/NcoI fragment containing the moesin actin-binding sequence was inserted into pYW899 via the NheI/NcoI sites . The moesin plasmid was a gift from Dr . Fabio Piano [75] . To generate Punc-54 gfp::cdc-42 ( pYW906 ) , two plasmids gfp_Ntag_TA and cdc-42_Ntag_TA were generated first . To generate the gfp_Ntag_TA plasmid , PCR-amplified gfp , using oligonucleotides CGGTACCATGAGTAAAGGAG AAGAACT and GTCTAGATTTGTATAGTTCATCCATGCC as primers and pPD95 . 75 as template was inserted to the vector pGEM T-Easy . To generate the cdc-42_Ntag_TA plasmid , PCR-amplified cdc-42 from k1101h01 ( Y . Kohara ) using primers GTCTAGAATGCAGACGATCAAGTGCG and CGTTAACCTAGAGAATATTGCACTTCTTC was inserted into the pGEM T-Easy vector . The 1 kb HindIII/KpnI fragment of pPD30 . 38 containing Punc-54 , the KpnI /XbaI gfp fragment from the gfp_Ntag_TA plasmid , and the XbaI/EcoR1 cdc-42 fragment from the cdc-42_Ntag_TA plasmid were inserted to the pPD95 . 75 vector previously digested with HindIII and EcoRI in a four-piece-ligation reaction to generate pYW906 . To generate Pajm-1pat-2::gfp ( pYW948 ) or Pajm-1ced-1::gfp ( pYW914 ) , the 3 . 7 kb KpnI pat-2 sequence from pYW901 or the 8 . 5 kb KpnI ced-1 sequence from pYW904 was inserted , respectively , into pYW902 . To generate Pced-1pat-2::gfp ( pYW942 ) , the 3 . 7 kb KpnI pat-2 sequence from pYW901 was inserted into pYW898 via the KpnI site . To generate Ppat-2pat-2Δcyto::gfp ( pYW964 ) , pat-2Δcyto was PCR-amplified from pYW901 using the primers CGGTACCATGCGA GAGGGTAGTTTTCC and TTGGT ACCGTC CTATAGAATAATGCAA and cloned into pYW903 via the KpnI site . To generate Pemphtypepat-2 ( ex ) ::mcherry ( pYW917 ) , the DNA fragment corresponding to pat-2 ( ex ) was PCR-amplified from pYW901 using the primers CGGTACCATGCGAGAGG GTAGTTTTCC and CGGTACCAGATCTCTTCC TTCTTCAGA and cloned into pYW900 via the KpnI site . To generate Phsppat-2 ( ex ) ::mcherry ( pYW966 ) , the 4 . 2 kb NheI/PvuI fragment of the Punc-54 pat-2 ( ex ) ::mCherry plasmid containing pat-2 ( ex ) ::mCherry was inserted into pPD49 . 78 via the NheI/PvuI sites . To generate Phspgfp::cdc-42 ( pYW959 ) , the 1 . 5 kb KpnI/HpaI fragment of pYW906 corresponding to gfp::cdc-42 was inserted into pPD49 . 78 and pPD49 . 83 ( different tissue specificity ) via the KpnI/EcoRV sites . To generate the Punc-54ina-1::gfp plasmid , Punc-54 was PCR-amplified using pYW913 as template and oligonucleotides GCATCCGCCAAGCTTGTCTTCTTC and GGATCCGGTACCGT CGACGCTAC as primers and used to replace the Pced-1 region of Pced-1ina-1::gfp via the SphI/BamHI sites . To generate the Ppat-3pat-3::gfp ( pYW1091 ) plasmid , the 10 . 2 kb pat-3 genomic DNA was amplified by PCR and subsequently cloned into the pPD95 . 75 vector via the XmaI/KpnI sites . To generate the Punc-54myri::mrfp ( pYW1092 ) construct , the myri::mrfp cDNA of Punc-86myri::gfp ( from C . Bargmann ) was inserted into the Punc-54mrfp vector via the KpnI site . To generate the Pajm-1gfp::cdc-42 ( pYW1093 ) plasmid , the gfp region of the pPD95 . 75 plasmid was replaced by gfp::cdc-42 from the Phspgfp::cdc-42 construct via the KpnI site , and the resulting construct was then digested with BamHI and SalI and subsequently inserted with the BamHI and SalI fragment containing the Pajm-1 promoter from pYW902 . To generate the pat-2 RNAi clone , the 1 . 3 kb PstI/HindIII fragment of pat-2 cDNA was inserted into the pPD129 . 36 vector . The pat-3 RNAi plasmid was obtained from the J . Ahringer RNAi library . All RNAi experiments were carried out using a bacterial feeding protocol [80] . In brief , L4 larvae were laid out on control plates ( pPD129 . 36 ) or the indicated RNAi plates and cultured at 20°C for 48 h , then the F1 embryos were picked for phenotypic analysis . Transgenic animals were generated by microinjection of the indicated plasmid ( s ) ( 3–50 ng/µL ) , using the pRF4[rol-6 ( su1006 ) ] , pTG96[sur-5::gfp] or Psur-5rfp plasmids ( 50 ng/µL ) as coinjection markers [81] , [82] . The injection procedure was performed as described previously [8] . The resulting transgenes and genetic backgrounds of the strains were listed in Table S4 . Phspced-10V12 was injected with the coinjection marker pTG96[sur-5::gfp] to unc-79 ( e1068 ) pat-2 ( st567 ) /dpy-17 ( e164 ) , and no rescue of the Pat phenotype was observed . To score cell corpses , transgenic embryos carrying the transgene Phspced-10V12 and pTG96[sur-5::gfp] were scored and only the data for those which later exhibited the Pat phenotype were used . Embryos were mounted on a 4% agar pad in M9 buffer in the presence ( four-fold stage embryos ) or absence ( comma and 1 . 5-fold stage embryos ) of 30 mM sodium azide at 20°C . Cell corpses were analyzed using DIC microscopy , as previously described [46] . A Zeiss Axioplan 2 microscope equipped with a digital camera ( AxioCam; Carl Zeiss , Inc . ) and 4 . 7 AxioVision imaging software was used . To obtain the cell corpse data of homozygous pat-2 ( st567 ) embryos derived from the heterozygous unc-79 ( e1068 ) pat-2 ( st567 ) /dpy-17 ( e164 ) mothers , embryos at the indicated developmental stages were analyzed and only the data for those which later exhibited the Pat phenotype were used . To obtain the cell corpse data of homozygous pat-2 ( st567 ) embryos derived from unc-79 ( e1068 ) pat-2 ( st567 ) mothers carrying either Ppat-2pat-2::gfp or Ppat-2pat-2::mcherry transgene , non-fluorescent embryos at the indicated developmental stages were analyzed . To obtain the cell corpse data of homozygous cdc-42 ( gk388 ) embryos derived from the heterozygous cdc-42 ( gk388 ) /mIn1[mIs14 dpy-10 ( e128 ) ] mothers , GFP-negative embryos at the indicated developmental stages were analyzed . To analyze cell death events during embryogenesis , wild-type or mutant embryos were filmed during the period 200–460 min after the first cleavage and the time point at which each cell corpse appeared was noted and was reported relative to the first cell death for comparison between the wild-type and mutants . About 40–50 serial Z-sections were recorded at 0 . 4 µm intervals every 1 min . To measure the duration of cell corpses in the wild-type and mutants , cell corpses appearing between 360 to 410 min after the first cleavage during embryogenesis were followed , and about 50–60 serial Z-sections were recorded at 0 . 3 or 0 . 4 µm intervals every 1 min . To monitor the muscle-mediated internalization of apoptotic cells , the fluorescence images of wild-type or mutant embryos carrying the Punc-54myri::mrfp transgene were recorded using the DeltaVision microscope ( GE Healthcare company ) equipped with a digital camera ( Photometrics Cascade II 512 EMCCD ) at 1 ( for wild-type ) or 3 ( for mutant ) -minute intervals for about 120 minutes . For the heat shock rescue experiments , transgenic embryos were subjected to heat shock at 33°C for 30 min and transferred to 20°C to recover for 2 hours , then cell corpses in embryos at the indicated stages were counted . To test the binding of PAT-2 ( ex ) ::mCherry to apoptotic cells and its effect on cell-corpse engulfment when overexpressed , embryos carrying the transgene Phsppat-2 ( ex ) ::mcherry or embryos carrying the transgenes Phsppat-2 ( ex ) ::mcherry and Phspina-1 ( N ) ::gfp were subjected to heat shock at 33°C for 60 min and transferred to 20°C to recover for 4–5 hours , then embryos were examined using fluorescence microscopy ( for mCherry and/or INA-1 ( N ) ::GFP signal ) or DIC microscopy ( for cell corpses ) . To overexpress GFP::CDC-42 , CED-10V12 or Annexin V::mRFP by heat-shock , the embryos carrying the respective transgene were subjected to heat shock at 33°C for 30 min and transferred to 20°C to recover for 2 hours , then embryos were examined using fluorescence microscopy ( mCherry signal ) or DIC microscopy ( cell corpses ) . | When cells undergo apoptosis , their corpses are quickly recognized and phagocytosed by engulfing cells . Although many cell types , such as muscle cells and epithelial cells , possess the ability to remove apoptotic cells , little is known about the receptors and signaling pathways used for apoptotic cell uptake by these “amateur” phagocytes . We show that , in Caenorhabditis elegans , integrin PAT-2/PAT-3 functions as an engulfment receptor in muscle cells . The integrin α subunit PAT-2 mediates both the recognition and subsequent phagocytosis of apoptotic cells . PAT-2 signals through UIG-1 for CDC-42 activation , leading to the cytoskeletal reorganization as the engulfing muscle cell extends pseudopods around the apoptotic cell . Furthermore , in contrast to PAT-2 , the other integrin α subunit INA-1 and the engulfment receptor CED-1 , both of which appear to act upstream of small GTPase CED-10 ( RAC1 ) , predominantly function in epithelial cells to mediate cell corpse removal . Therefore , epithelial cells and muscle cells employ different engulfment receptors for apoptotic cell recognition , downstream signaling , and specific GTPase activation during apoptotic cell removal . | [
"Abstract",
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] | 2012 | Integrin α PAT-2/CDC-42 Signaling Is Required for Muscle-Mediated Clearance of Apoptotic Cells in Caenorhabditis elegans |
Lassa fever is a viral hemorrhagic fever endemic in West Africa . However , none of the hospitals in the endemic areas of Nigeria has the capacity to perform Lassa virus diagnostics . Case identification and management solely relies on non-specific clinical criteria . The Irrua Specialist Teaching Hospital ( ISTH ) in the central senatorial district of Edo State struggled with this challenge for many years . A laboratory for molecular diagnosis of Lassa fever , complying with basic standards of diagnostic PCR facilities , was established at ISTH in 2008 . During 2009 through 2010 , samples of 1 , 650 suspected cases were processed , of which 198 ( 12% ) tested positive by Lassa virus RT-PCR . No remarkable demographic differences were observed between PCR-positive and negative patients . The case fatality rate for Lassa fever was 31% . Nearly two thirds of confirmed cases attended the emergency departments of ISTH . The time window for therapeutic intervention was extremely short , as 50% of the fatal cases died within 2 days of hospitalization—often before ribavirin treatment could be commenced . Fatal Lassa fever cases were older ( p = 0 . 005 ) , had lower body temperature ( p<0 . 0001 ) , and had higher creatinine ( p<0 . 0001 ) and blood urea levels ( p<0 . 0001 ) than survivors . Lassa fever incidence in the hospital followed a seasonal pattern with a peak between November and March . Lassa virus sequences obtained from the patients originating from Edo State formed—within lineage II—a separate clade that could be further subdivided into three clusters . Lassa fever case management was improved at a tertiary health institution in Nigeria through establishment of a laboratory for routine diagnostics of Lassa virus . Data collected in two years of operation demonstrate that Lassa fever is a serious public health problem in Edo State and reveal new insights into the disease in hospitalized patients .
Lassa fever is a viral hemorrhagic fever that was first described in 1969 in the town of Lassa in the North-East of Nigeria [1] . It is endemic in the West African countries of Sierra Leone , Guinea , Liberia , and Nigeria ( [2] , [3] and references therein ) . Cases imported to Europe indicate that Lassa fever also occurs in Côte d'Ivoire and Mali [4] , [5] . The causative agent is Lassa virus , an RNA virus of the family Arenaviridae . Its natural host is the rodent Mastomys natalensis [6] , [7] , which lives in close contact to humans . Mastomys shed the virus in urine [8] and contamination of human food is a likely mode of transmission . The virus may be further transmitted from human to human , giving rise to mainly nosocomial epidemics with case fatality rates ( CFR ) of up to 65% [9]–[12] . However , most of the Lassa virus infections in the communities are probably mild [13] . Clinically , Lassa fever is extremely difficult to distinguish from other febrile illnesses seen in West African hospitals , at least in the initial phase [14] , [15] . Gastrointestinal symptoms , pharyngitis , and cough are frequent signs . Late complications include pleural and pericardial effusions , facial edema , bleeding , convulsion , and coma . In the terminal stage patients often go into shock , although bleeding itself is usually not of a magnitude to produce shock [10] , [14]–[22] . The only drug with a proven therapeutic effect in humans is the nucleoside analogue ribavirin . Drug efficacy decreases if treatment is commenced at day 7 or later [23] , making early diagnostics critical for survival . Lassa virus can be detected in blood at an early stage of illness . Death occurs about two weeks after onset of illness with fatal cases showing higher levels of viremia than those who survive . In survivors , virus is cleared from circulation about three weeks after onset of symptoms [24]–[26] . IgM and IgG antibodies are detectable only in a fraction of patients during the first days of illness , and patients with fatal Lassa fever may not develop antibodies at all [24] , [26] , [27] . Therefore , RT-PCR is a valuable tool for rapid and early diagnosis of Lassa fever [24] , [25] , [28] , [29] . So far , diagnostic testing of samples from Lassa fever patients has been performed almost exclusively outside of Africa . Only the laboratory at the hospital in Kenema , Sierra Leone , which has become operational since 2004 ( after civil war forced its closure in 1993 ) , is able to perform Lassa fever testing for patients [30] . In Nigeria , the situation improved with the implementation of Lassa virus PCR testing at a research laboratory of the University of Lagos , which facilitated retrospective laboratory confirmation of Lassa fever cases in various parts of the country [31] , [32] . However , none of the hospitals in the endemic areas of Nigeria has the capacity to perform Lassa virus tests . Case management is thus mainly based on non-specific clinical criteria [14] , [15] and in the worst cases , health care workers became infected while they treated patients without knowing they had Lassa fever [32] . The Irrua Specialist Teaching Hospital ( ISTH ) has faced these challenges for many years . ISTH serves as a referral hospital in Edo State , one of the many Nigerian States with evidence of Lassa fever [1] , [9] , [12] , [17] , [32]–[37] . In 2001 , ISTH was designated as a Centre of Excellence in the management of Lassa fever , along with two other federal tertiary health institutions . It set up awareness campaigns to sensitize hospital staff and the public to the severity of Lassa fever infection and need for treatment and prevention . Ribavirin was periodically supplied to the hospital by the Federal Ministry of Health and given to suspected cases . Prevalence and case fatality figures based on clinical suspicion and pilot laboratory investigations in 2003 and 2004 suggested a high incidence of Lassa fever in Edo State [31] , but the true magnitude of the problem remained obscure . In 2007 , the management of ISTH was dissatisfied with the level of response and attention given to Lassa fever and took bold steps to address the situation . Amongst these was the establishment of the Institute of Lassa Fever Research and Control ( ILFRC ) . The rationale for the institute was based on the need to build capacity to adequately respond to the epidemics observed in the region in terms of manpower development and training , laboratory diagnosis , and adequate case management as well as the dire need for focused research and advocacy . A collaborative effort was made to establish a laboratory for molecular diagnostics of Lassa fever , which was considered crucial for appropriate case and contact management , including early treatment and postexposure prophylaxis with ribavirin [23] , [38] , [39] . The diagnostic and research laboratory was built in 2008 and started operation in September 2008 . We describe here the establishment of a diagnostic service for Lassa fever and analyze the data recorded during two years of operation .
The study was classified as a service evaluation and granted exemption from ethical review by the Research and Ethics Committee of ISTH . Lassa fever PCR diagnostics , patient management , and public health measures are part of routine clinical practice at ISTH . The choice of treatment or diagnostics was that of the clinician and patient according to professional standards or patient preference . Neither PCR diagnostics nor treatment with ribavirin was experimental in nature . All data described in the manuscript stem from existing records that have been generated as part of the regular clinical practice . The service was evaluated 2 years after implementation . Service evaluation is exempt from ethical review according to the National Code of Health Research Ethics , National Health Research Ethics Committee , Federal Ministry of Health , Nigeria . The following case definition , taking into account published signs and symptoms of Lassa fever , was used as a guideline for identifying suspected cases and requesting molecular testing for Lassa virus ( from the diagnostic laboratory request form ) : However , as the laboratory provides routine diagnostic service for patients , there was flexibility in applying this case definition . Clinical experience and suspicion were taken into account as well . In addition , in the interest of time , Lassa virus testing was often performed before typhoid fever or malaria had been excluded . Samples were also sent from other parts of Nigeria for Lassa virus RT-PCR testing . Ribavirin treatment was usually commenced on clinical grounds before laboratory testing . If RT-PCR was negative , it was terminated . However , in cases with a strong clinical suspicion for Lassa fever , ribavirin treatment was continued even if the RT-PCR was negative . If the RT-PCR was positive , treatment was continued or commenced . If a patient was suspected of having Lassa fever in any of the clinical or outpatient departments of ISTH , staff of ILFRC collected an EDTA blood sample . The blood was centrifuged and virus RNA was purified from plasma by using the diatomaceous earth method as described [40] , [41] . In brief , 140 µl and 14 µl , respectively , of each plasma sample were mixed with 560 µl chaotropic lysis buffer AVL ( Qiagen ) containing 5 . 6 µg carrier RNA ( Qiagen , no . 19073 ) . AVL has been shown to inactivate enveloped RNA viruses [42] . The lysate was incubated at room temperature for 10 min . About 100 mg of diatomaceous earth ( Sigma , no . D3877 ) and subsequently 560 µl of ethanol was added to the lysate and the slurry was incubated with vigorous agitation for 10 min at room temperature in a shaker . The diatomaceous earth was pelleted by centrifugation , and the pellet was washed three times , first with 500 µl of buffer AW1 ( Qiagen , no . 19081 ) , second with 500 µl of buffer AW2 ( Qiagen , no . 19072 ) , and finally with 400 µl of acetone ( each washing step included vortexing with wash fluid , centrifugation for 2 minutes at maximum speed in a table top centrifuge , and removal of supernatant ) . The pellet was dried at 56°C for 20 minutes until the acetone was completely evaporated . To elute the RNA from the diatomaceous earth , the pellet was resuspended in 100 µl of water ( Aqua ad injectabilia ) , the slurry was vortexed , incubated 1 minute at room temperature , centrifuged at maximum speed , and the supernatant was transferred to a new tube . The RNA was immediately used for PCR . The Lassa virus RT-PCR targeting the GPC gene was performed using QIAGEN OneStep RT-PCR Kit reagents ( Qiagen , no . 210210 or 210212 ) as described [28] . The 25-µl assay contained 5 µl RNA , 0 . 6 µM primer 36E2 ( ACC GGG GAT CCT AGG CAT TT ) , 0 . 6 µM primer LVS-339-rev ( GTT CTT TGT GCA GGA MAG GGG CAT KGT CAT ) , 0 . 4 mM dNTP , 1× RT-PCR buffer , 1× Q-solution , and 1 µl enzyme mix . The reaction was performed in a Primus25advanced thermocycler ( PeqLab , Erlangen , Germany ) using the following temperature profile: 50°C for 30 min , 95°C for 15 min , followed by 45 cycles of 95°C for 30 s , 52°C for 30 s and 72°C for 30 s . All pre-PCR pipetting was performed with filter tips . PCR products were separated in a 1 . 5% agarose gel containing ethidium bromide and visualized by UV light . Gel images were recorded with a digital camera . As a positive control , inactivated culture supernatant of cells infected with Lassa virus strain CSF was used . All consumables , reagents , chemicals , kits , and plastic materials were purchased in Germany or the US and transferred to ISTH . PCR products generated in the diagnostics from September 2008 through February 2011 were stored at −20°C and sequenced retrospectively using primer 36E2 . The automated base calling was proof-read by visual inspection of the electropherograms . A representative set of 35 sequences has been sent to GenBank and assigned the accession nos . JN651366-JN651400 . Phylogenetic analysis included all novel GPC sequences ( n = 204 ) as well as Lassa virus sequences available from GenBank by May 2011 . The program jModelTest 0 . 1 . 1 [43] identified the general time-reversible model of sequence evolution with a gamma distribution of among-site nucleotide substitution rate variation ( GTR+gamma ) as the substitution model that best describes the data in the nucleotide sequence alignment of the partial GPC genes ( 284 taxa , 237 sites ) . The gamma+invariant sites model was not considered because it was not favored by jModelTest , and because the two parameters estimated under this model ( the gamma distribution shape parameter and the proportion of invariant sites ) are highly correlated and may be poorly estimated depending on the number of taxa [44] . Phylogenies were inferred by the Bayesian Markov Chain Monte Carlo method implemented in BEAST software [45] using the following parameters: GTR+gamma; 107 steps with sampling every 105th step; and two independent runs combined ( effective sampling size >100 for all parameters ) . To avoid over-parameterization – considering that the sequences were short – simple molecular clock and demographic models were chosen , that is strict clock with mean substitution rate fixed at 1 and constant population size . Demographic data as well as major symptoms at presentation were recorded on the request form that accompanied the blood sample . The department responsible for the patient provided clinical chemistry data generated in the Clinical Pathology Department of ISTH and data on treatment and outcome . Data were entered into a database ( Excel , Microsoft ) maintained at ILFRC . All patients from whom samples were processed in the Lassa fever diagnostics laboratory from January 2009 through December 2010 were included in the analysis . Each case was included only once; further testings on the same case were not considered . The data set was checked using plausibility criteria . A PCR result was corrected before analysis if the sequence of the PCR product indicated a false positive result , e . g . if the sequence corresponded to that of the positive control . Statistical comparison of unpaired groups was performed for continuous parameters with the Mann-Whitney test and for frequencies with two-tailed Fisher's Exact test . A critical p value of 0 . 01 was considered appropriate , given the large number of tests performed on the data set . The p value was further lowered to 0 . 001–0 . 0005 according to Bonferroni correction if multiple tests were conducted within one category ( e . g . within the category profession ) .
The laboratory was built in 2008 on the campus of ISTH . Equipment was provided by Bernhard-Nocht-Institute for Tropical Medicine ( BNI ) , Harvard University , and University of Ibadan after initiating collaborations with the hospital in 2007 . These partners also performed on-site trainings before and regularly during operation of the laboratory . In addition , staff of ILFRC was trained for 3 months in PCR technology at BNI in Hamburg and at Harvard University in Cambridge , Massachusetts . The laboratory started operation in September 2008 . It is divided into three zones to minimize PCR contamination: a “clean” area for all pre-PCR manipulations , a “grey” area for amplification , and a “dirty” area for post-PCR manipulations ( Figure 1A ) . The clean area features separate rooms for i ) sample inactivation , ii ) RNA extraction and PCR setup , and iii ) mastermix preparation . The workflow starts with sampling blood using a closed syringe system . The sample is processed the same day or , if it arrives late , it is stored at 4°C and processed the next day . The syringe container is opened within a plexiglas box in the inactivation room and the plasma is mixed with chaotropic buffer to inactivate the virus and prepare RNA , or aliquoted for storage at −20°C ( Figure 1B ) . From each plasma sample , 140 µl and 14 µl are inactivated and processed separately . The reason for testing two different volumes per sample is to avoid false negative results due to PCR inhibition , which appears to be a particular problem with samples from patients with severe hemorrhagic fever [46] . If the undiluted sample ( 140 µl ) would be false negative due to inhibition , the 1/10-volume sample ( 14 µl ) was expected to be positive due to the dilution of the inhibitor . In addition , running two PCRs in parallel on a patient sample enhances the reliability of the diagnostic process , as in most Lassa fever cases both samples were positive due to the high virus load ( see below ) . RNA is extracted from samples and a negative control by the diatomaceous earth method [40] in the RNA extraction room . This method is inexpensive , and has been demonstrated to recover RNA over a broad concentration range with high efficacy [47] . The RT-PCR mastermix is prepared in the mastermix room and transferred to the extraction room for setting up the reaction . The closed PCR vials are transferred to the PCR thermocycler in the “grey” area . After completion of the PCR run , the closed vials are transferred to the detection room in the “dirty” area for agarose gel electrophoresis and gel documentation . PCR results are technically evaluated and transmitted to the clinics on a report form ( Figure 1C ) . If either the undiluted or the 1/10-volume sample was positive , additional tests were performed to exclude PCR contamination . Gel pictures were transmitted to BNI staff via the internet for process monitoring . Standard laboratory procedures were defined in a set of quality management documents . During 2009 through 2010 , the laboratory has processed blood samples of 1650 patients . Testing a second sample was requested for 57/1650 patients; all other patients were tested once . Retrospective verification of positive test results by sequencing the PCR products revealed that in 13/1650 cases ( 0 . 8% ) , the result was probably false positive result due to PCR contamination . The sequences of the corresponding PCR fragments matched exactly that of the positive control or the sequence of a highly positive sample processed before . For data analysis , these samples were re-classified as negative , as well as samples which have initially been reported as indeterminate ( n = 12; e . g . faint PCR signals that were not confirmed in a second blood sample ) . Applying these criteria , 1452 cases ( 88% ) were Lassa RT-PCR negative , and 198 cases ( 12% ) were positive . Undiluted and 1/10-volume RNA extract were positive in 138 ( 70% ) cases and one of both was positive in 60 ( 30% ) of the Lassa fever cases . The outcome was known for 170 cases with confirmed Lassa fever: 61 died and 109 survived . Thus , the CFR is 31% if calculated based on all Lassa fever patients or 36% if calculated based on cases with known outcome . The vast majority of the patients came from Edo State , in particular from the Local Governmental Areas ( LGA ) surrounding ISTH , namely Esan West , Esan Central , Esan North East , Etsako West , and Owan West ( Figure 2 ) . Several patients also came from the neighboring state of Ondo , and a few samples were sent from other parts of Nigeria . The median age of Lassa fever patients was 32 years . Those who died from the disease were older than those who survived ( p = 0 . 005 ) ( Figure 3 ) . The proportion of males and females was equal , and no association with outcome was observed . Children and students made up about one third of the patients . Adult patients had various professions and no specific profession was associated with the outcome of Lassa fever . When comparing the demographic data of Lassa fever negative versus positive patients , no statistically significant differences were observed with the exception of age , which tended to be higher among the positive patients ( p = 0 . 006 ) . Patients presented at the hospital 5 days ( median ) after onset of symptoms . Lassa fever positive patients presented slightly later than those who tested negative ( median difference 1 . 5 days , p = 0 . 009 ) . A blood sample for Lassa fever diagnostics was taken from 75% of the patients at the day of presentation or the following day without differences among the groups . Lassa fever patients stayed longer in hospital and had a longer duration of illness than those who tested negative ( p = 0 . 002 and p = 0 . 01 , respectively ) . Clear differences in these two categories were observed between fatal cases of Lassa fever and survivors . Survivors stayed 10 days in hospital and had duration of illness of 16 days , while patients died from Lassa fever 2 days after admission and 10 days after onset of symptoms ( figures are median; p<0 . 0001 and p<0 . 0001 , respectively ) ( Figure 3 ) . The time window for therapeutic intervention was extremely short: 25% of the fatal cases died one day after admission and the same day the blood sample was taken for Lassa virus RT-PCR; 75% died within 4 days of hospitalization and within 3 days after sampling for PCR . Patients with signs of Lassa fever were seen in virtually all clinical and outpatient departments of ISTH . The vast majority of requests for Lassa fever testing came from the emergency departments , the medical wards , and the outpatient departments . Lassa fever patients were significantly more frequent among patients attending the adult emergency department ( detection rate 19%; p<0 . 0001 ) while they were underrepresented among patients attending the outpatient departments ( detection rate 5%; p<0 . 0001 ) . Thus , nearly two thirds of laboratory-confirmed Lassa fever cases were seen in the emergency departments of ISTH . Median axilliary body temperature recorded at the time of presentation was 37 . 5°C for all patients tested . Thus , most of them had a body temperature lower than indicated in the case definition ( ≥38°C ) . Body temperature was slightly higher for those who tested positive ( p = 0 . 003 ) . However , patients who had a fatal outcome had 0 . 8°C lower temperature ( median difference ) than those who survived ( p<0 . 0001 ) ( Figure 3 ) . Indeed , nearly half of the fatal cases had normal axilliary temperature ( 35 . 5°C–37°C ) [48] and 75% had ≤38°C . A few had hypothermia ( ≤35 . 5°C ) . The CFR among Lassa fever patients was three times higher than the fatality rate among patients who tested negative for Lassa fever ( p<0 . 0001 ) , and negative patients were 10-times less frequently admitted to hospital than positive patients ( p<0 . 0001 ) . Ribavirin treatment was given to nearly all laboratory-confirmed Lassa fever patients , although several patients who tested negative received the drug as well , at least initially . While all survivors received the drug , 23% of Lassa fever patients with fatal outcome did not receive ribavirin because they died the day of presentation or the next day . The 1/10-volume sample ( defined as 2+ score ) was more frequently positive in fatal cases than in survivors ( p = 0 . 001 ) , indicating higher virus load in fatal cases . Clinical symptoms reported at presentation largely matched the known symptoms of Lassa fever [10] , [14]–[20] . The only symptom that was significantly more frequent among fatal cases was bleeding ( p = 0 . 0001 ) . Urea and creatinine blood levels were available for a subset of patients . Both values were clearly elevated in fatalities compared to survivors ( p<0 . 0001 for urea and creatinine ) ( Figure 3 ) . Two levels of seasonality were observed ( Figure 4 ) . First , the total number of tests followed a seasonal pattern . From April through October , about 25% fewer patients were tested than in the remaining months . Second , the percentage of Lassa fever positive samples dropped by about 50% in the same period . Both effects led to a seasonal pattern with high Lassa fever incidence in the hospital from November through March ( dry season ) and low incidence from April through October ( rainy season ) . The short PCR fragments obtained in routine diagnostics were sequenced and subjected to phylogenetic analysis . Sequences from Edo and Ondo State cluster within lineage II ( Figure 5 ) . New sequences obtained from samples sent from Adamawa State ( Nig11–205 and Nig11–208 from Yola ) and Ebonyi State ( Nig11–186 from Abakaliki and Nig10–148 from Izzi ) also cluster with lineage II , while sequences of samples from Nasawara State ( Nig09–072 from Akwanga ) and the Federal Capital Territory ( Nig09–121 and Nig09–193 from Abuja ) cluster with lineage III . This is in agreement with the known geographical distribution of Lassa virus lineages in Nigeria [3] , [49] . In addition , one sequence from Edo State ( Nig09–045 ) was found to cluster with the new putative lineage that was recently described in Edo State and is defined by sequence Nig05-A08 ( marked with “ ? ” in Figure 5 ) [49] . All other sequences from Edo and Ondo State form a separate clade within lineage II that can be further subdivided into three clusters ( A , B , and C ) , although the posterior probability support for cluster C is weak ( Figure 6 ) . The analysis of larger sequences might substantiate this tree topology . Strains within these three clusters do not show a strict geographical clustering , presumably because the sequences are too short to further resolve the relationships . However , there are a few visible associations between the origin of the strains and their phylogeny . Cluster A contains strains from Esan West and Uhunmwode , cluster B contains mainly strains from Esan Northeast and Central , while cluster C contains strains from all parts of Edo State as well as the strains from Ondo State . Even though the sequences are too short for an association study , it is worth mentioning that there is no obvious clustering of strains from patients with fatal outcome; they are randomly distributed over the whole phylogenetic tree .
Pilot investigations initiated in 2003 by the University of Lagos , ISTH , and BNI suggested that Edo State is a hot-spot for Lassa fever [31] . These data led to the decision to establish at ISTH a routine diagnostic service for Lassa fever to facilitate appropriate case management . It was further decided to use PCR as it offers case detection at an early stage [24] , [25] . The training of staff members of ISTH in theory and practice of diagnostic PCR in partner institutions and on-site has been of paramount importance for the implementation of this technology at ISTH . Featuring separate areas for virus inactivation , RNA extraction , and mastermix preparation , the laboratory complies with basic standards of diagnostic PCR facilities . To guarantee smooth operation of the laboratory , technologies and workflow of the diagnostic facility at BNI in Hamburg were “mirrored” at ISTH , with only minor modification . This strategy facilitated training , interchangeability of protocols , and troubleshooting . The most recent version of the GPC gene-specific RT-PCR assay [28] was chosen as a PCR assay and samples were tested using the regular extraction volume and 1/10-volume to minimize the problem of PCR inhibition [46] . Indeed , several samples tested positive with the 1/10-volume RNA preparation only and contamination was excluded in most of them by sequencing . It is likely that in these cases amplification of the undiluted sample was inhibited . A modification to the BNI procedures has been the use of diatomaceous earth for RNA preparation instead of a commercial kit to reduce the costs [40] . The main problem that arose during operation of the laboratory was PCR contamination , which was confirmed by sequencing of the PCR products . It was probably facilitated by the high analytical sensitivity of the assay ( 15 virus genome copies per reaction are detected with a likelihood of 95% ) [28] and the need to open the reaction tubes for detection in agarose gel . The staff of the laboratory was aware of this inherent problem of PCR and protocols were developed to minimize and cope with it . We found retrospective evidence for reporting a false positive result in about 1% of the cases tested , which corresponds to an analytical specificity for the whole diagnostic process of 99% and an overall positive predictive value of 90% . However , with one exception , all contaminations manifested in only one of the two reactions ( undiluted and 1/10-volume ) performed on each sample . Thus , the positive predictive value for the majority of positive samples , namely those positive in both reactions is 99% , while the positive predictive value for samples positive in only one of the two reactions is 80% . Overall , we consider these good performance characteristics for a PCR diagnostics performed in a resource-limited setting . Complementing the RT-PCR by antibody testing would further improve the reliability of the diagnostics . On the one hand , serology may be used to confirm the PCR diagnosis in patients who have already developed antibodies during the acute phase . On the other hand , antibody testing facilitates detection of patients in the convalescent stage when the virus load has dropped below the detection limit of the PCR [25] , [26] , [50] . The demographic data of the patients did not provide clues as to risk factors associated with Lassa fever; profession , geographic origin , and gender did not differ significantly between patients who tested positive and those who tested negative . However , a seasonal pattern of Lassa fever incidence was observed with the lowest number of cases during April through October , which corresponds to the rainy season . Similar , though not identical seasonal fluctuations have been described in Sierra Leone and Guinea [14] , [15] , [51] . The reason for a decrease in incidence during rainy season is not clear . Behavioral changes may play a role , as the number of all patients tested also decreased during this time , which may suggest that patients attend the hospital less frequently in rainy than in dry season . In addition , rodent dynamics and climate factors influencing the efficacy of virus transmission from the reservoir to humans may be involved [2] , [52] . The CFR of 31% is high , though in the range of previous reports . In hospitalized patients with endemic Lassa fever , the CFR ranged from 9 . 3% to 18% [14]–[16] , [51] , [53] , [54] . During nosocomial outbreaks , the CFR appears to be higher , ranging from 36% to 65% [9]–[12] . However , these figures are not fully comparable , due to differences in case definitions and diagnostic methods used in the various studies . Half of the patients with febrile illness attended ISTH at day 5 after onset of symptoms or later , and 50% of those with Lassa fever even at day 6 or later . The efficacy of ribavirin treatment decreases with progression of the disease and is hardly effective after day 6 [23] . Thus , a large number of Lassa fever patients attending ISTH did not benefit as greatly as they could have from the administration of ribavirin early in their disease course . This may explain the CFR of one third . Indeed , the time for therapeutic intervention is extremely short , as 50% of the fatal cases die before day 10 of illness and within 2 days in hospital—often before ribavirin treatment could be commenced . The severity of Lassa fever also explains why most of the patients attend the emergency department rather than the general outpatient departments . A few parameters were identified which differ significantly between fatal cases of Lassa fever and survivors and are not yet documented in the literature . An important finding was lower body temperature in fatal cases . Often the temperature was not elevated at all or even below the normal range ( <35 . 5°C ) . This resembles the sepsis-associated hypothermia which is a predictor of poor outcome [55] , [56] . Although hypothermia is common in end-stage shock and organ failure of any etiology and not specific for Lassa fever , this sign has implications for the case definition of Lassa “fever” , which apparently needs to be revised to facilitate sensitive detection of cases in the terminal stage . Another factor associated with fatal outcome was higher age . Elderly people are also at higher risk of dying from sepsis , which is thought to be related to a reduced immune status or immune dysfunction [57] , [58] . Bleeding was identified as the only clinical sign at presentation associated with poor outcome . Creatinine and blood urea levels were strongly elevated in the fatal cases suggesting renal failure . The semiquantitative PCR data also indicated higher virus load in patients with poor prognosis , which is consistent with published data [26] . The burden of Lassa fever in most regions of Nigeria is not known , as hospitals are not able to detect Lassa fever patients by laboratory testing . In future , surveillance systems including laboratory confirmation in reference centers need to be implemented in the country . Taken together , the data from ISTH indicate that fatal cases of Lassa fever are characterized by the following criteria: We propose to use the above set of criteria as a surveillance tool to identify hospitals that are attended by Lassa fever patients . While this case definition is less sensitive , as it targets fatal cases only ( “the tip of the iceberg” ) , it may be more specific for Lassa fever than existing ones . The sequences generated from the short PCR products confirmed previous studies showing that Lassa virus strains from Edo State cluster phylogenetically with lineage II [3] , [49] . Although the sequences originate from the same geographical area , they are quite diverse , which is in agreement with previous reports [3] , [6] . A first attempt to correlate Lassa virus sequences with outcome did not reveal associations at least on the level of the clades that were resolved by the phylogenetic program . More sophisticated studies are warranted to look into possible links between virus genetics and clinical presentation . In conclusion , routine diagnostics for Lassa fever has been established at ISTH . There are two major advantages for case management . First , early detection of a Lassa fever case improves protection of staff from nosocomial Lassa virus transmission . In the pre-diagnostic era , an unrecognized Lassa fever patient may have been cared for on a regular ward for several days , before clinical signs raised the suspicion of Lassa fever and appropriate measures were taken . Now , Lassa fever cases are transferred immediately to a specific ward where they are appropriately managed . In addition , close contacts to the Lassa fever patients , including hospital staff , can be monitored or offered ribavirin post-exposure prophylaxis as early as possible if the contact was very close [39] . Second , ribavirin treatment can be commenced early in all Lassa fever cases or may be terminated in non-cases if it had been provisionally commenced on clinical suspicion . Treatment is no longer based on clinical criteria , which is neither sensitive nor specific . However , rapid on-site diagnosis alone probably does not reduce the case fatality rate as long as most Lassa fever patients present too late for ribavirin treatment to be efficacious . The open PCR platform may be used also for molecular testing for other pathogens . In addition , it provides the basis for research involving the Lassa fever patient and the optimization of the supportive treatment , including renal dialysis and intensive care . Steps to upgrade the laboratory with equipment for viral load determination , serology , blood chemistry , and hematology have been undertaken . | In the past , diagnostic testing for Lassa fever patients in Nigeria has been performed nearly exclusively outside of the country . Patients thus were managed on-site based on clinical suspicion alone , posing risks to patients and health care workers and exhausting resources . To tackle this problem , we established a diagnostic PCR laboratory directly at a referral hospital serving a Lassa fever endemic area in Nigeria . Long-term collaboration between partners in the North and the South was crucial to implement this project . Training of laboratory staff in the partner institutions and on-site , mobilization of local human and financial resources , good management of the laboratory , a basic quality management and control system , and a stable supply chain for consumables and reagents were among the key factors for success . The laboratory reliably delivered results in a short turnaround time , despite some problems due to PCR contamination . The service has improved patient and contact management including treatment with ribavirin and led to better protection of health care workers against hospital-acquired infections . The data provide new insights into disease progression and a basis for further optimization of case management including supportive treatment . | [
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] | 2012 | Molecular Diagnostics for Lassa Fever at Irrua Specialist Teaching Hospital, Nigeria: Lessons Learnt from Two Years of Laboratory Operation |
Mating of budding yeast cells is a model system for studying cell-cell interactions . Haploid yeast cells secrete mating pheromones that are sensed by the partner which responds by growing a mating projection toward the source . The two projections meet and fuse to form the diploid . Successful mating relies on precise coordination of dynamic extracellular signals , signaling pathways , and cell shape changes in a noisy background . It remains elusive how cells mate accurately and efficiently in a natural multi-cell environment . Here we present the first stochastic model of multiple mating cells whose morphologies are driven by pheromone gradients and intracellular signals . Our novel computational framework encompassed a moving boundary method for modeling both a-cells and α-cells and their cell shape changes , the extracellular diffusion of mating pheromones dynamically coupled with cell polarization , and both external and internal noise . Quantification of mating efficiency was developed and tested for different model parameters . Computer simulations revealed important robustness strategies for mating in the presence of noise . These strategies included the polarized secretion of pheromone , the presence of the α-factor protease Bar1 , and the regulation of sensing sensitivity; all were consistent with data in the literature . In addition , we investigated mating discrimination , the ability of an a-cell to distinguish between α-cells either making or not making α-factor , and mating competition , in which multiple a-cells compete to mate with one α-cell . Our simulations were consistent with previous experimental results . Moreover , we performed a combination of simulations and experiments to estimate the diffusion rate of the pheromone a-factor . In summary , we constructed a framework for simulating yeast mating with multiple cells in a noisy environment , and used this framework to reproduce mating behaviors and to identify strategies for robust cell-cell interactions .
Cell-to-cell signaling via diffusible molecules is an important mode of communication between cells in many mammalian systems such as neuron axon guidance [1] , immune cell recognition [2] , and angiogenesis [3] . These interactions involve sensing an attractant from the partner and responding by moving or growing in the appropriate direction ( i . e . chemo-taxis/tropism ) , while secreting signaling molecules in a reciprocal fashion . This behavior is conserved in eukaryotes from fungi to humans [4 , 5] . The budding yeast Saccharomyces cerevisiae , undergoes a mating response that has served as a model system for studying cell-to-cell communication [6] . Yeast cells have two haploid mating types , a and α . By sensing the pheromone molecules ( α-factor and a-factor ) , a- and α-cells detect the presence of a mating partner . These secreted peptides form a spatial gradient , bind to the pheromone-specific receptors , and elicit a response that includes cell-cycle arrest , gene expression , and formation of a mating projection ( “shmoo” ) . Ultimately , the mating response results in the fusion of the two cells and nuclei to create an a/α diploid cell ( reviewed in [7] ) . Mathematical modeling has provided a useful tool for studying cell-cell interactions . Previously , moving interface models have been used to investigate deforming the shape of eukaryotic cells . In [8] , a 1D continuum model of cell motility in amoeboid cells based on a viscoelastic description of the cytoplasm was developed , and in [9] , cells in a 2D domain were treated as a two-phase fluid . The immerse boundary and finite element based approach was developed to model the actin network and cell morphogenesis in [10] , an evolving surface finite element method modeled cell motility and chemotaxis in [11] , and the boundary tracking Lagrangian framework was used in [12 , 13] . Other models used agent-based frameworks such as the Potts model , which takes into account detailed chemical networks and moving cells [14] . Level set approaches have also been adopted [15 , 16] to simulate the cell membrane deformation coupled to chemistry reaction dynamics . Previous studies focused on the relationship between morphogenesis and its underlying biochemical or mechanical machinery . In this work , we extend this concept by including the molecular dynamics within the extracellular space to study multi-cell interactions . In laboratory yeast mating assays , wild-type cells mate with approximately 100% efficiency [17] . Genetic screens have identified mutants that mate at reduced efficiency [18] . One class of mutants prevents mating altogether . In addition , Hartwell and colleagues have modified the basic assay to investigate “three-way” mating between an a-cell that can mate with either an α-cell that makes α-factor or an α-cell that does not [19 , 20] . In this mating discrimination test , wild-type a-cells mate almost exclusively with α-factor producers . Mutations that affect the sensitivity of the system , such as the deletion of SST2 ( a gene which downregulates signaling via the heterotrimeric G-protein ) or the deletion of BAR1 ( which encodes for an α-factor protease ) , dramatically reduce both mating efficiency and mating discrimination [20] . The communication between mating cells is mediated by the mating pheromones which bind their cognate G-protein-coupled receptors turning them on . Active receptor catalyzes the conversion of heterotrimeric G-protein into Gα-GTP and free Gβγ . The resulting Gβγ subunit can then recruit Cdc24 to the membrane where it activates Cdc42 . Active Cdc42 is a master regulator of the cell polarity response orchestrating the cytoskeleton , exo/endocytosis , and signaling complexes [21 , 22] . All of these processes involve noise due to Brownian motion , stochasticity in gene expression or other intracellular fluctuations [23–26] , which may affect cell assessment of signals and their responses [27] . In particular , the diffusion of ligand into the local neighborhood of the cell and the binding of ligand to receptor are thought to introduce significant stochasticity to gradient-sensing systems [24 , 28] . Therefore , it is necessary to consider the effects of noise when exploring cell behavior during mating . There has been extensive mathematical modeling of the yeast pheromone response system . The early models were non-spatial and emphasized signaling dynamics [29–31] . More recent modeling efforts have incorporated spatial dynamics , both deterministic [32–34] and stochastic [35–37] . Models have ranged from simple generic formulations to detailed mechanistic descriptions . Finally , we and others have modeled pheromone-induced morphological changes to cell shape [12 , 38] . In related research , Diener et al . employed a combination of image processing and computational modeling to describe the extracellular α-factor dynamics in a population of mating cells , and how those dynamics were altered by the protease Bar1 [39] . However , missing from the literature is modeling of the yeast mating process itself involving both a- and α-cells . In this paper , the goal was to construct the computational infrastructure for simulating the mating of two or more yeast cells , and then to investigate the factors responsible for robust mating behavior . We want to use our models to understand in greater detail the spatial dynamics that ensure efficient mating , and provide quantitative explanations and predictions on how perturbing these dynamics ( e . g . mutants ) disrupts the cell-cell interactions during mating . We succeeded in developing numerical methods for simulating yeast mating . Key elements include modeling the shape of the cell described by a moving boundary technique , and the extracellular diffusion dynamics of the pheromone ligand . Using this framework , we explored different model structures and parameters in a systematic fashion using generic models . We were able to simulate the high efficiency of mating among wild-type cells , and their ability to discriminate among partners that synthesized mating pheromone at different levels . Our simulations suggested that two critical factors ensuring robust mating under noisy conditions were the polarized secretion of mating pheromone , and the presence of the Bar1 protease . In addition , we demonstrated that supersensitive mutants disrupted both mating efficiency and discrimination , reproducing experimental data . More generally , this work makes progress toward the goal of a more detailed description of cell-cell interactions .
In this section , we describe the stochastic model for multi-cell mating systems in two-dimensional space . Cell shape is represented by a level set formulation to capture the deforming plasma membrane induced by pheromone signaling . As described in the Introduction , mating occurs when an a-cell and α-cell are in close proximity ( Fig 1A ) . They sense the pheromone gradient generated by the partner and project toward the source . In Fig 1A , cells are labeled with a marker for the polarisome ( a: Spa2-GFP or α: Spa2-mCherry ) , a cellular structure at the tip of the mating projection . From a simulation standpoint , this process can be broken down into a series of steps ( Fig 1B ) from the secretion and diffusion of pheromones to the resulting growth in the mating projection . Describing the mating process between two cells requires solving diffusion equations for the ligands in the extracellular space , which evolve according to the shifting positions of the pheromone sources . These sources in turn depend on the sensing of the ligand input and the morphological response . Thus , the cell boundary is evolved together with the molecular dynamics associated with the membrane for each cell . Unlike our previous model of a single polarizing cell which solves surface reaction-diffusion equations in Lagrangian coordinates to capture deformation of the cell membrane [12] , here we apply the level set method [40] , which can track the moving curve front implicitly by solving a Hamilton-Jacobi equation . In this way , it is easier to study the interactions of multiple cells , and it allows a straightforward extension to the case of multicellular interactions by introducing level set functions for each different cell , and inclusion of the surface diffusions for molecules on the cell membrane . With this methodology , we can distinguish between the intracellular and extracellular space , and couple extracellular pheromone diffusion with the intracellular reaction-diffusion dynamics . The numerical scheme is described in the Methods section . For simplicity , the cell is modeled as a two-dimensional ( 2D ) circle with radius of 1 μm; the actual yeast cell is a three-dimensional ( 3D ) sphere with radius 2 μm . The experimental mating assay involves placing the cells on a surface ( i . e . paper filter ) so that the mating reaction is effectively in two dimensions . The time unit is 100 seconds to approximate within an order of magnitude the growth velocity observed in experiments . In this model , the mating pheromone is denoted by f , which is the external cue of cell polarization . Two membrane-associated species , u1 and u2 , initially are uniformly distributed and then undergo polarization upon sensing the pheromone signal . The system forms a two-stage cascade in which the output of the first stage ( u1 ) is the input to the second stage whose output is u2 . The species v1 and v2 provide negative feedback ( integral feedback ) to regulate u1 and u2 ( see S1 Text ) . The cell grows in the direction determined by u2 . This model is a generic model of the mating system and abstracts away the mechanistic details of yeast mating . As studied in the previous model for the two-stage yeast cell polarity system on a single cell [41] , u1 represents the protein Gβγ , which is the output of the heterotrimeric G-protein system and the input to the Cdc42 system , and u2 represents active Cdc42 , which is the master regulator of yeast cell polarization . Finally , the peak of the u2 distribution represents the polarisome which directs new secretion driving mating projection growth . To track morphological changes of multiple cells , we use a level set function , denoted by ϕ ( x , t ) , to distinguish exterior and interior of one cell such that the domain D is decomposed into three regions: Γ = {x: ϕ ( x , t ) = 0} representing the plasma membrane , Ωin = {x: ϕ ( x , t ) < 0} corresponding to the intracellular space , and Ωex = {x: ϕ ( x , t ) > 0} is the extracellular space . The membrane is moving in a given velocity field V ( x ) which is described in the Methods section and represents growth of the projection . The stochastic dynamics of the diffusing pheromone ligand ( fα represents α-factor and fa represents a-factor ) are described in ( 1 ) and ( 2 ) : ∂fα∂t=DαΔfα+Sα ( x , t ) −kαfα+κ1fα∂2W1 ( x , t ) ∂t∂x , x∈Ωex ( 1 ) ∂fa∂t=DaΔfa+Sa ( x , t ) −kafa+κ1fa∂2W2 ( x , t ) ∂t∂x , x∈Ωex , ( 2 ) where Sα ( x , t ) and Sa ( x , t ) denote the sources of pheromones , and they are either constant or localized Gaussian distributions with support on the membrane ( see Methods ) . Each cell contains the membrane-associated species ( uj , vj ) , j = 1 , 2 , whose dynamics are described in Eqs ( 3–6 ) , and the membrane velocity is described in ( 7 ) : ∂u1∂t=DsΔsu1+k101+ ( β1f˜ ) −q1+k111+ ( γ1u1p1 ) −h1− ( k12+k13v1 ) u1+κ2u1∂2W3 ( x , t ) ∂t∂x , x∈Γ ( 3 ) ∂v1∂t=k14 ( u˜1−k1ss ) v1+κ3v1∂2W4 ( x , t ) ∂t∂x , x∈Γ ( 4 ) ∂u2∂t=DsΔsu2+k201+ ( β2u1 ) −q2+k211+ ( γ2u2p2 ) −h2− ( k22+k23v2 ) u2 , x∈Γ ( 5 ) ∂v2∂t=k24 ( u˜2−k2ss ) v2 , x∈Γ ( 6 ) V ( x , t ) =Vamp⋅u2⋅max ( 0 , 〈n→ , d→max〉 ) , x∈Γ ( 7 ) f˜=fmaxx∈Sf+0 . 1 , u˜1=∫su1ds∫sds , u˜2=∫su2ds∫sds , p1=k101+ ( β1f˜ ) −q1 , p2=k201+ ( β2u1 ) −q2 . Note that Eqs ( 3–7 ) are restricted on the plasma membrane Γ , where Δs denotes the surface Laplace-Betrami operator for the lateral surface diffusion . In Eq ( 3 ) , f˜ is associated with the pheromone factor from the opposite mating type; that is , if the cell is an a-cell , then f˜ is f˜α . In addition , instead of f , we use the normalized distribution f˜; in this definition , a constant is added to make the parameter consistent with [12] . This normalization represents the adjustable dynamic range mechanisms in the system designed to prevent the sensing from saturating ( see Discussion ) . In Eqs ( 3–6 ) , we ignore the advection terms ∇S⋅ ( ujV ) and ∇S⋅ ( vjV ) , which describe the increased surface area where the membrane species reside , because this dilution effect is minimized by the integral control feedback in the model ( see S1 Text ) . On the other hand , these terms necessitate a smaller time step because of the curvature appearing during the computation . Therefore , for numerical efficiency , we simulate Eqs ( 3–6 ) on the deforming membrane without the advection terms . In Eq ( 7 ) , V ( x , t ) denotes the normal component of the growth velocity of the plasma membrane which describes the rate and direction of membrane movement ( the tangential component is ignored by assuming it is small ) . Vamp is a constant specified with respect to the time scale , n→ is the unit outward normal vector , and d→max is the growth direction defined as the unit outward normal vector at the center of the polarisome . The normal velocity V ( x , t ) is assumed to be proportional to u2 , i . e . , active Cdc42 . The concentration and location of active Cdc42 determines the position of the polarisome , which directs the secretion [21] . We model the growth direction to be aligned with the normal direction at the polarisome . Multiplicative noise was adopted and each of the noise terms was weighted by a parameter κi , representing external or internal noise sources . The function Wi ( x , t ) is a random variable such that the white noise term ∂2Wi ( x , t ) ∂t∂x follows a normal distribution with variance the same as the time step according to the definition of a Wiener process in our simulations . For simplicity , we considered three noise effects in the simulations . One represents the diffusive noise of the extracellular ligands which is described in the pheromone equation . The second is associated with the dynamics of u1 ( Gβγ ) which represents noisy internal processes such as fluctuations in ligand-receptor binding and receptor activation of G-protein . The final noise effect represents noise in the regulatory feedback loop ( v1 ) . Noise introduced in the second stage of the model is ignored to focus on the sensing noise . In addition , we modified the definition of the velocity function in the stochastic model . Since the velocity depends on u2 which is fluctuating , it is necessary to apply filtering to smooth the dependence of the velocity function on u2 ( see S1 Text ) . Finally , we can explore the deterministic dynamics simply by choosing zero for each κi . The default initial conditions for the simulations are two cells ( one a-cell and one α-cell ) whose centers are separated by 4 μm in which the membrane species ui is uniformly distributed on the cell surface , and thus no polarisome is formed in the beginning and initial pheromone secretion will be isotropic . Unless otherwise stated , no Bar1 ( α-factor protease ) is present , i . e . , cells are considered bar1Δ; there is no background α-factor source . At this point we note some of the limitations of the model which we expand upon in the Discussion . First the model is a generic representation of the system that lacks mechanistic detail . Second we employed a quasi-steady-state approximation of α-factor spatial dynamics to speed up the simulations . Third there was not rigorous fitting of the parameters to the experimental data but rather a sampling of different regions of parameter space that produced experimentally observed behaviors . In this section , we investigated the impact of noise in the context of exploring one specific parameter in the simulations , the a-factor diffusion constant . In the natural environment , yeast mating is efficient and robust to a variety of perturbations . In this section , we explored how features of the mating process could promote robust and efficient mating; we compared different mating scenarios by modifying the model parameters . A natural extension of two-cell mating simulations is three-cell mating simulations . In three-cell simulations , the set-up can be either two α-cells and one a-cell , or two a-cells and one α-cell . In the former case , if the two α-cells are equidistant from the a-cell , we found that in the absence of noise , the a-cell projected toward the middle in between the two α-cells . Interestingly , if the two α-cells are slightly offset ( i . e . the a-cell is located 0 . 1 microns below the middle line of two α-cells so that one is closer ) , then the a-cell still projected toward the middle ( Fig 6A ) . If the a-cell is Bar1+ , then the a-cell is able to gradually reorient to the closer mating partner . However adding noise to the simulations , the Bar1+ a-cell projected toward one or the other α-cell in a random fashion whether or not the cells were offset . Although the no-noise case is somewhat artificial , it indicates how Bar1 can improve the ability to detect the gradient direction in this idealized scenario . Alternatively , the simulation can be between one α-cell and two a-cells . If the two a-cells have different genotypes , then there is a competition between the two for the single α-cell . This corresponds to mating competition experiments , a second important type of mating assay [47] , in which one mixes two a-cell genotypes with a limiting quantity of α-cells . We tested the importance of Bar1 using mating competition . In mating competition simulations between Bar1+ and bar1Δ cells we found that the Bar1+ cells mated with the single α-cell partner 20/20 times ( Fig 6B ) . Snapshots of more simulations are provided in Fig K in S1 Text . Greater insight on why the Bar1+ cell has the advantage can be provided by the α-factor profiles for two sample simulations ( Fig 6B , lower ) . The Bar1 helps to remove the excess α-factor so that the Bar1+ a-cell is able to sense the gradient from the α-cell . The bar1Δ cell is stuck in a region of high α-factor in which the gradient is shallower . The third mating arrangement is having a single a-cell choose between two α-cells of different genotypes . One specific scenario is having one α-cell make α-factor whereas the other α-cell makes less or no α-factor . Experimentally this simulation corresponds to a third important type of mating assay: mating discrimination in which the a-cell must discriminate between the α-cell mating partner secreting α-factor from α-cell decoys that do not [20 , 47] . This assay measures the ability of an a-cell to sense and respond accurately to a pheromone gradient . We imaged mating mixes using both Bar1+ and bar1Δ cells as well as a combination of the two . We found that mating was short-range when the a-cells were Bar1+ , i . e . , both a- and α-cells made short projections ( see S1 Text ) . With the bar1Δ a-cells , there was longer-range mating with only the a-cells forming longer projections . We hypothesized that degradation of α-factor by Bar1 resulting in short-range mating in the Bar1+ matings . The projection length in both simulations and experiments was defined by subtracting the initial cell radius from the distance between the center of the cell and the point that is farthest from the center on the cell membrane . The asymmetry in projections lengths in the bar1Δ matings was reminiscent of our simulations in which we varied the a-factor diffusion rate ( Fig 2 ) . In particular , as the a-factor diffusion rates became slower , the α-cell projection became shorter ( and the a-cell projection became longer ) . We attributed this difference to the reduced spread of a-factor from its source when its diffusion constant is lower . To provide an estimate of the a-factor diffusion coefficient , we determined the relative length of the α-cell projection normalized by the total distance traveled by both projections , and plotted this α-cell length for both simulations and experiments in Fig 9 . In the simulations we varied the a-factor diffusion rate from 0 . 1 to 100 . From this comparison we estimate that the a-factor diffusion rate is 1 μm2/s .
In this paper we performed computer simulations of the yeast mating process for the first time . The main advance was constructing a computational framework for yeast mating which we used to explore different model structures and parameters . We reproduced qualitatively the basic mating behaviors and calculated the simulated mating efficiency . In addition , we were able to model mating competition and mating discrimination which together with mating efficiency form the three basic assays of yeast mating [19 , 47] . From a computational perspective , we combined modeling the shape of the cell using a moving boundary technique with the extracellular diffusion of the pheromone ligands with a previously described minimal model of pheromone-induced cell polarity . The simulations were CPU intensive because of the multiple time-scales , the evolution of the level set function over the computational domain , and the calculation of the velocity field . Overall the simulation time depended on the number of cells , time step size , length of simulation , and α-factor diffusion rate . We examined for the first time the coupling among ligand secretion , ligand diffusion , and ligand-induced receptor activation which revealed new cell-cell interaction dynamics that could not be captured in single-cell simulations . We identified key factors that contributed to the efficiency and robustness of mating . First polarized secretion of mating pheromone resulted in higher mating efficiency than isotropic secretion . This finding is consistent with experimental data in which a-factor secretion through the Ste6 transporter is highly polarized [48] . It is likely that α-factor is secreted in a polarized fashion given the polarization of the secretory pathway during mating [22] . A second critical factor is the proper modulation of the sensitivity of the system . In experimental matings , strains that are “supersensitive” show considerably reduced mating efficiency and mating discrimination because they are unable to determine the pheromone gradient direction . By increasing the value of the parameter β1 in Eq ( 3 ) , we were able to mimic the supersensitive phenotype , and the resulting mating simulations were defective . Finally , the presence of Bar1 helped cells to mate in the presence of background α-factor . Bar1 has been implicated to play an important role in modulating the pheromone dynamics [38 , 39] . Our results are consistent with the conclusions in [38] that Bar1 helps to shape the α-factor gradient for optimal mating . More specifically , both results show that Bar1 can create an α-factor sink that amplifies the α-factor gradient promoting gradient-sensing . The simulations in this work incorporated stochastic effects , a generic description of intracellular signaling that drives the cell membrane , and polarized secretion of both pheromone and Bar1 . There are important limitations to this study . First , we did not attempt to present a detailed quantitative portrait of the mating process with mechanistic reactions . We employed a generic model of yeast cell polarity with a small number of variables for computational efficiency and to facilitate parameter exploration . Second , we employed a quasi-steady-state approximation of α-factor spatial dynamics , although we provide simulation data that this choice does not affect the basic results . Third , we employed mechanisms that only crudely approximate physical reality such as ligand normalization . Fourth , we have not attempted to fit the parameters to actual mating data; rather our approach was to test multiple parameters values to qualitatively explore different scenarios . We thus achieved our goal of constructing a computational framework that is capable of generating realistic-looking responses and reproducing basic behaviors . From a technical standpoint , one important future challenge is speeding up the simulations so that the boundary velocity can be reduced to a more realistic value . One possibility is to employ a quasi-steady-state approximation for the fast α-factor dynamics . For the model with multiple cells , each cell would be assigned with a level set function and a velocity field in our framework , and so there is the potential to improve the efficiency by performing parallel computation for different level set functions or representing all cells by one level set function with a mixed velocity field . Current simulations are all restricted to two-dimensional space . Theoretically it is feasible to extend this framework into three dimensions , although the computation could be very expensive because the computational cost increases exponentially with respect to dimensionality . In addition , experimentally the mating reaction occurs on a surface ( i . e paper filter ) which is effectively two-dimensional [17] . Importantly this research helps to identify the key processes to focus on for future work . The generic framework is easily extended , and we can incorporate more sophisticated and detailed mechanistic models . Because of the absence of mechanistic details , the models in this work can be thought of as “general mating models” , providing a generic description of gradient tracking informed by the yeast mating system . For example , we plan to replace the normalized f term with pheromone-induced Bar1 . In the future , an important goal is to replace the generic terms with more mechanistic terms . With a more realistic mechanistic model of pheromone-induced cell polarity , we could attempt to simulate the mating defects of a variety of mutants . Numerous mutants have been isolated that affect mating efficiency and discrimination including fus1Δ , spa2Δ , etc . [49] . One goal would be to reproduce these mating phenotypes at a quantitative level; another goal would be to predict novel mutants that may affect mating .
An overview of the model including model equations is presented in the main text in Section 1 . Here we present additional details . The evolution of the level set function Φ is governed by a Hamilton-Jacobi equation ϕt ( x , t ) +V|∇ϕ ( x , t ) |=0 , x∈D , in which the velocity field V is defined in Eq ( 6 ) . More information on how the boundary conditions are imposed on the computational grid as well as other technical details can be found in the S1 Text . The time step is set to be 4 × 10−4 for extracellular pheromone , and 0 . 01 for membrane-associated dynamics . The simulations were performed with the authors’ original MATLAB codes , and they can be provided by the authors upon request . | One of the riddles of Nature is how cells interact with one another to create complex cellular networks such as the neural networks in the brain . Forming precise connections between irregularly shaped cells is a challenge for biology . We developed computational methods for simulating these complex cell-cell interactions . We applied these methods to investigate yeast mating in which two yeast cells grow projections that meet and fuse guided by pheromone attractants . The simulations described molecules both inside and outside of the cell , and represented the continually changing shapes of the cells . We found that positioning the secretion and sensing of pheromones at the same location on the cell surface was important . Other key factors for robust mating included secreting a protein that removed excess pheromone from outside of the cell so that the signal would not be too strong . An important advance was being able to simulate as many as five cells in complex mating arrangements . Taken together we used our novel computational methods to describe in greater detail the yeast mating process , and more generally , interactions among cells changing their shapes in response to their neighbors . | [
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] | 2016 | Modelling of Yeast Mating Reveals Robustness Strategies for Cell-Cell Interactions |
AmrZ , a member of the Ribbon-Helix-Helix family of DNA binding proteins , functions as both a transcriptional activator and repressor of multiple genes encoding Pseudomonas aeruginosa virulence factors . The expression of these virulence factors leads to chronic and sustained infections associated with worsening prognosis . In this study , we present the X-ray crystal structure of AmrZ in complex with DNA containing the repressor site , amrZ1 . Binding of AmrZ to this site leads to auto-repression . AmrZ binds this DNA sequence as a dimer-of-dimers , and makes specific base contacts to two half sites , separated by a five base pair linker region . Analysis of the linker region shows a narrowing of the minor groove , causing significant distortions . AmrZ binding assays utilizing sequences containing variations in this linker region reveals that secondary structure of the DNA , conferred by the sequence of this region , is an important determinant in binding affinity . The results from these experiments allow for the creation of a model where both intrinsic structure of the DNA and specific nucleotide recognition are absolutely necessary for binding of the protein . We also examined AmrZ binding to the algD promoter , which results in activation of the alginate exopolysaccharide biosynthetic operon , and found the protein utilizes different interactions with this site . Finally , we tested the in vivo effects of this differential binding by switching the AmrZ binding site at algD , where it acts as an activator , for a repressor binding sequence and show that differences in binding alone do not affect transcriptional regulation .
Pseudomonas aeruginosa is an opportunistic , Gram negative bacterium that causes a variety of infections , mainly in immune-challenged patients [1]–[3] . More notably , chronic lung infection by P . aeruginosa is the leading cause of death in patients with the autosomal recessive disorder cystic fibrosis ( CF ) [4] . The underlying cause of the severity of these infections is due in part to the arsenal of virulence factors P . aeruginosa has at its disposal , including type III secretion systems , production of biofilms , phospholipase , exotoxin A , motility , and lipopolysaccharide . In alginate producing strains isolated from CF patients , the transcription factor AmrZ ( Alginate and Motility Regulator Z , formerly AlgZ ) is highly expressed [5] . Our previous work has shown AmrZ functions as both a transcriptional activator and repressor of several virulence factors . AmrZ is necessary for alginate production , via the activation of algD , which is the first gene in the alginate biosynthetic operon [6] . Reciprocal to this , AmrZ represses fleQ , which encodes an activator of flagellum expression [7] . AmrZ is also required for the regulation of genes responsible for type IV pili localization and twitching motility , through the interaction with a currently unknown gene target [8] . Finally , AmrZ also represses its own transcription by binding to two sites on the amrZ promoter , amrZ1 and amrZ2 [9] . The 108 amino acid , 12 . 3 kD AmrZ protein is a member of the ribbon-helix-helix ( RHH ) family of DNA binding proteins , sharing highest sequence similarity to the Arc and Mnt repressors from bacteriophage P22 [10] . Sequence analyses predict that there are over 2300 proteins containing RHH domains found in bacteria , Archaea , and bacteriophages; however , less than twenty of these proteins have been studied with structural or biochemical techniques [11] . Structural information from RHH proteins both in the presence [12]–[19] and absence [20]–[27] of operator DNA , show that they exist as dimers , formed by a hydrophobic core created by the two α-helices . The majority of RHH proteins are transcriptional repressors . AmrZ and Helicobacter pylori NikR are currently the only characterized RHH proteins known to function as both transcriptional activators and repressors [23] . DNA binding by RHH proteins occurs by the insertion of the anti-parallel β-sheet formed by one β-strand from each monomer into the major groove of DNA . The interactions between the protein and the recognition site are very specific , and mutations to either the DNA binding β-sheet , or the operator site often have a negative effect on DNA binding [27] , [28] . In addition to binding DNA as a dimer , RHH proteins also assemble as tetramers , which are stabilized by other domains in the protein , such as occurs with the C-terminal domain of Mnt [29] . Information from sequence alignments and structural predictions define three regions of the AmrZ protein , an extended N-terminus spanning residues 1–16 , the RHH domain , located from residues 13–66 , and a C-terminal domain from residues 67–108 [30] . Both the extended N-terminus and the C-terminal domain do not share any sequence similarity to other proteins , and their exact function has remained an open question . The extended N-terminus has been hypothesized to play a role in DNA binding , and is conserved in other AmrZ orthologs of P . putida and P . syringae [31] . Extended N-termini of other RHH proteins have been examined , although their functions vary between cofactor binding , oligomerization and protein-protein interactions , ATP hydrolysis , in addition to having roles in DNA recognition . The C-terminal domain of AmrZ is proposed to be involved in protein oligomerization , which is supported by glutaraldehyde cross linking assays that show AmrZ forms oligomeric species consistent with the molecular weight of dimers and tetramers in solution [30] . Of the genes that are regulated by AmrZ , the specific locations of the binding sites are only known for two of them . AmrZ functions as a transcriptional activator at the algD promoter and binds 282 base pairs upstream of the transcriptional start site . Additionally , AmrZ acts as a transcriptional repressor of its own gene and recognizes two sites on the amrZ promoter ( amrZ1 and amrZ2 ) at positions −93 and −161 . Interestingly , DNA foot-printing has been performed at each of these three sites , and little sequence consensus is shared among them [6] , [9] . Both the algD operon and amrZ are under the control of the alternative sigma factor AlgT ( AlgU/σ22 ) [32] . Expression of the algD operon requires additional factors , including the response regulators AlgB and AlgR , the nucleosome proteins IHF and AlgP , and the AlgQ protein , each being necessary , but not sufficient to activate transcription on their own [5] . To date , only the AmrZ and AlgT proteins are known to interact with the amrZ promoter region . Open questions have remained as to the exact strategies employed by AmrZ to function as both a transcriptional activator and repressor . It is unclear what specific interactions the protein makes with both activator and repressor sequences within DNA in order to carry out these functions . To answer these questions , we determined the crystal structure of an AmrZ C-terminal truncation mutant , Δ42 AmrZ , in complex with an 18 bp oligonucleotide containing the amrZ1-binding site . This structure defines the specific recognition site as two half sites separated by a linker region , and provides evidence that the extended N-terminus of AmrZ interacts with the DNA in a sequence independent manner . Site directed mutagenesis experiments of the amrZ1 DNA reveal that recognition is not only based on the direct readout of the nucleotide sequences , but also relies on recognition of the intrinsic shape of the DNA . These data allow for the creation of a model for transcriptional repression by AmrZ , where a combination of specific base recognition at two half sites and recognition of intrinsic DNA structure allow for binding . We also examined the interaction of AmrZ with the algD-binding site , where AmrZ binding functions as an activator of alginate biosynthesis . The results from these assays only identify one AmrZ binding site on algD and also suggest that the protein may utilize an additional residue in DNA binding . Finally , we demonstrate that while there are different protein interactions at the activator and repressor sequences , these differences alone do not account for the activator and repressor activity of AmrZ .
We determined the structure of a C-terminal truncation mutant of AmrZ , Δ42 , in complex with an 18 bp oligonucleotide containing the amrZ1 site to 3 . 1 Å resolution ( Table 1 and Figure 1A ) . The Δ42 variant of AmrZ ( residues 1–66 ) contains the extended N-terminus and the RHH DNA binding domain , but has a truncated C-terminal domain . Many C-terminal truncation variants of AmrZ were used in crystallization experiments , and Δ42 was the only AmrZ construct tested that crystallized either in the presence or absence of DNA . The Δ42 AmrZ protein was tested for DNA binding affinity , and compared with the wild type protein , no reduction in affinity to any of the three known AmrZ binding sequences ( amrZ1/amrZ2/algD ) was observed ( data not shown ) . The structure reveals AmrZ binds the amrZ1 site as a dimer-of-dimers , and DNA recognition occurs by the interaction with two half sites on the DNA , separated by five base pairs . There are no major structural differences between each AmrZ dimer ( Cα RMSD = 0 . 381 Å ) ( Figure 1B ) . The dimer-dimer interface , which occludes approximately 290 Å2 of surface area on each dimer , is formed by a series of interactions between specific residues located on the loop connecting α-helix 1 and α-helix 2 on chains B and C ( Figure 1C ) . The interactions in this region are symmetric , with the backbone carbonyl of His38 of one protomer forming a hydrogen bond to Arg40 of the opposing protomer , and the side chain of His39 forming a salt bridge to Glu51 , also across the interface . The relatively small interface between each dimer , in combination with evidence that AmrZ forms higher order oligomers in solution [30] , suggests there are likely additional dimer-dimer interactions mediated by the C-terminal domain of the protein . The interface between AmrZ monomers to form a dimer is primarily composed of α-helix 1 and α-helix 2 of each monomer that come together to form a hydrophobic core . The dimer interface is quite extensive , composed of 25 residues ( Figure 1D , underlined residues ) and buries approximately 1600 Å2 of each monomer . Each AmrZ dimer interacts with the amrZ1 binding site through sequence dependent interactions ( see below ) , mediated by the insertion of the anti-parallel β-sheet , formed by dimerization , into the major groove of DNA . Additionally , a number of sequence independent interactions to the phosphate backbone are formed , further supporting the protein-DNA complex ( Figure 1E ) . The protein - DNA interactions exclude a total surface area of 1469 Å2 and are symmetric on both halves of the DNA . The one exception to this is the α-helix 2 N-terminus of chain D , which is not positioned to interact with the phosphate backbone; however , this is most likely due to the lack of a 5′ phosphate group on the nucleotide A1 , an artifact of chemical DNA synthesis . The structure allows us to determine the specific nucleotide sequence recognized by AmrZ , as well as other factors that contribute to DNA recognition . The insertion of the anti-parallel β-sheet from each AmrZ dimer into the major groove of the amrZ1 site provides for the recognition of two half sites , each with the sequence 5′-GGC ( Figure 1E , orange bases ) . Sequence dependent binding by AmrZ occurs via the interaction of three residues , Lys18 , Val20 , and Arg22 , with the nucleotides . Lys18 from one AmrZ monomer is positioned where it can form hydrogen bonding interactions to the O6 and N7 atoms of the two guanine nucleotides , G23 and G24 ( G4 and G5 on the other half site ) ( Figure 2A ) . The DNA binding β-sheet also orients the residue Arg22 , from the other monomer of the dimer , to form a bidentate hydrogen bond to both the O6 and N7 atoms of the nucleotide G12 ( G31 on the other half site ) . This is on the opposite strand of the two bases with which Lys18 interacts . Bidentate hydrogen bonding , specifically between arginine residues and guanine nucleotides , are a major determinant in the selectivity of DNA bases [33] . Another relevant residue located on the DNA binding β-sheet is Val20 . Interestingly , among other RHH proteins , this position in the DNA binding β-sheet is generally conserved as a neutral hydrophilic residue . One other exception to this is the Neisseria gonorrhoeae FitAB protein in which there is also a valine at this position [18] . The structure of FitAB in complex with DNA shows the valine forms a van der Waals interaction with the C5 methyl group of a thymine base . In the AmrZ structure , it appears that residue Val20 is poised to select for cytosine bases C13 and C14 ( C32 and C33 on other half site ) via a hydrophobic interaction ( Figure 2B ) . Purine nucleotides would not be favorable in these locations since the N7 atom of the purine base would interfere with the hydrophobic pocket that is formed by Val20 , while a thymine nucleotide in this position would sterically clash with the isopropyl side chain of the valine residue . To confirm the requirement for each half site in amrZ1 recognition by AmrZ , a series of mutations were created to the amrZ1 DNA binding site . Both 1 and 2 nucleotides in each amrZ1 half site were mutated , and the affinity of WT AmrZ to each of these mutant sequences was measured using fluorescence anisotropy ( Table 2 ) . Mutating one nucleotide in each 5′-GGC AmrZ recognition half site to 5′-GTC caused a 9 . 8 fold reduction in affinity , while mutating two of the nucleotides in each half site to 5′-TTC caused a 12 . 9 fold reduction in affinity compared to binding to the native amrZ1 sequence . These results confirm the observations from the structure that sequence dependent recognition occurs through the interactions with two half sites , each with the sequence 5′-GGC . We previously evaluated the contribution of Lys18 and Arg22 to AmrZ activity using in vitro DNA binding assays at amrZ1 and transcriptional reporter assays to measure amrZ repression [30] . The mutation of Lys18 to an alanine ( K18A ) resulted in a drastic reduction in the DNA binding activity , causing a 274-fold increase in the dissociation constant ( Kd ) , compared to WT AmrZ . When amrZ was replaced in the P . aeruginosa chromosome with a gene encoding K18A AmrZ , amrZ derepression was observed , which was similar in magnitude to that observed in strains harboring null amrZ alleles . Similar results were obtained for the R22A mutant of AmrZ , which had a 44-fold increase in Kd compared to WT AmrZ in vitro , and comparable effects of amrZ transcription in vivo . When the effects of mutating the valine at position 20 to an alanine ( V20A ) were tested in vitro , a 10-fold increase in Kd was observed . Even with a smaller reduction in DNA binding ability compared to the K18A and R22A mutants , V20A AmrZ was unable to repress amrZ transcription in vivo . We observe electron density for the extended N-terminus starting at residue 10 on chains A and C . This density is only observed on the side of the AmrZ dimer that makes the specific contacts to the amrZ1 binding site . The lack of electron density of the extended N-terminus on the side of the AmrZ dimer that does not contact the DNA suggests that the N-terminus is disordered in solution , and becomes structured upon DNA binding . Residues 10–17 of the N-terminus form a looped structure , allowing the amino acids Ser13 and Arg14 in the major groove to interact with the DNA ( Figure 3 ) . This looped structure is supported by the residue Tyr11 , which forms a hydrogen bond to the backbone of Glu25 from the other monomer in the dimer , and by the head-on orientation of the carboxyl side chain of Glu25 perpendicular to the aromatic ring of Tyr11 . The side chain of Ser13 forms a hydrogen bond to a phosphate in the DNA backbone , and also positions the residue Arg14 into the major groove of the DNA; however , no contacts between Arg14 and the DNA bases are observed in the structure . This is consistent with previous studies of an AmrZ R14A mutant , which has no change in binding affinity for the amrZ1 DNA when compared to WT protein in vitro , as well as no effect on amrZ repression in vivo [30] . Other RHH proteins contain extended N-termini that contribute to DNA binding . The Staphylococcus aureus pSK41 plasmid-encoded ArtA protein has a 16 residue N-terminal domain that is necessary for recognition of at least one of the binding sites of the protein [19] . Additionally , the seven residue extended N-terminus of the Arc repressor is disordered in solution , but adopts a tandem-turn structure upon binding DNA [13] , and mutations to the N-terminus result in decreased binding to operator sites [34] . Although mutations to the extended N-terminus in AmrZ do not reduce affinity to the amrZ1 repressor site , AmrZ may act in a manner similar to ArtA , where the extended N-terminus may provide specificity for DNA binding at other sites . There are a number of interactions between AmrZ and the phosphodiester backbone of the DNA that act to position the DNA binding β-sheet in the major groove . The majority of these sequence independent interactions occur with residues located in the N-terminus of α-helix 2 , which points down towards the DNA backbone ( Figure 2C ) . The positioning of this helix allows the formation of hydrogen bonding interactions between the side chains of Ser41 and Ser44 , and the backbone amide nitrogens of Met42 and Asn43 to the phosphate groups of the DNA . This interaction is further bolstered by the positive dipole of the N-terminal end of α-helix 2 and the negatively charged phosphate backbone of the DNA . Another sequence independent interaction between AmrZ and the DNA occurs via the side chain of Arg28 , from α-helix 1 , to the backbone of the DNA . Interestingly , the location of α-helix 2 also allows the side chain of Asn43 to form two hydrogen bonding interactions to the backbone amide nitrogen and carbonyl oxygen of the DNA binding residue , Arg22 , in the opposite monomer . This also helps position Arg22 for interaction with the DNA bases . Sequence independent interactions formed by the N-terminus of α-helix 2 are one of the main structural features of RHH proteins [11] . These contacts are often observed to anchor the protein onto the DNA; however , in the case of AmrZ , this electrostatic interaction may play an additional role in recognition of the intrinsic shape of the DNA , particularly in the linker region . Analysis of the amrZ1 DNA in the structure reveals a significant narrowing of the minor groove to 2 . 8 Å in the A/T rich region between the two amrZ1 half sites ( Figure 4A ) . In addition to the narrow minor groove , there is an increase in the width of the major grooves where AmrZ interacts with each half site , most likely to accommodate the width of the anti-parallel β-sheet in this region ( Figure 4B ) . A-tract DNA , as in the amrZ1 site , has specific properties in that each ApA base pair step exhibits a negative roll , and bifurcated hydrogen bonds between each adenine and two thymine nucleotides on the opposite strand lead to propeller twisting and minor groove narrowing; A-tract DNA is also thought to be less flexible due to the extra stabilization provided by the additional bifurcated hydrogen bonds [35] . Based on this we investigated the region between the two amrZ1 half sites for any role in AmrZ binding , and whether the binding of AmrZ causes distortions to the amrZ1 DNA , or if the amrZ1 DNA is intrinsically distorted , allowing for AmrZ recognition . To test if the linker region between each AmrZ binding half site contributes to AmrZ affinity at amrZ1 , the native A/T rich linker sequence 5′-AAAAC was mutated to a G/C rich linker region with the sequence 5′-CGCGC , which resulted in a 7 . 5 fold reduction in binding ( Table 2 ) . It is important to note that this reduction in binding is not caused by the removal of specific protein - nucleotide interactions , since there are no contacts between the AmrZ protein and amrZ1 DNA in this region . Combining the mutations in the AmrZ binding half site with the mutations to the linker region ( TTC/GC amrZ1 ) caused a severe aberration in binding affinity ( 244-fold reduction ) . The results show that binding affinity is regulated by both the sequence dependent interactions between AmrZ and amrZ1 and the linker region separating these binding sites . Additional binding experiments were performed to determine if the intrinsic structure of the A/T rich linker contributes to binding affinity . The five base pair linker region on the native amrZ1 binding site was mutated to three sequences , each having their own unique properties . A sequence with the linker region mutated to 5′-TTTTC resulted in a 5 . 0-fold reduction in AmrZ binding , when compared to the WT amrZ1 sequence ( Table 2 ) . TpT base pair steps have the same properties of ApA base pair steps , including a narrow minor groove and less flexibility [35] . Mutating the amrZ1 sequence to 5′-AATTC caused a 7 . 0-fold reduction in affinity when compared to AmrZ binding to the WT amrZ1 sequence ( Table 2 ) . Molecular dynamics simulations of the interactions between the papillomavirus E2 transcription factors and their binding sites have shown that the 4 nucleotide sequence AATT has similar minor groove and propeller twist properties to A-tract DNA [36] . The last amrZ1 mutant binding site tested had a linker region containing the sequence 5′-ATATC , and AmrZ binding to this site was also altered compared to the WT amrZ1 sequence , causing a 4 . 7-fold reduction in affinity ( Table 2 ) . This site was designed to test if flexibility in the linker region allowed AmrZ to distort the DNA and form a complex . The TpA step in this sequence permits variations in roll , twist and slide due to poor stacking between these base pairs , and DNA containing these steps contain wider minor grooves , caused by the steric clashing of cross strand adenines [37] . It should be noted that the properties described for these sequences are average parameters derived from structures and that individual structures show a range of properties , specifically minor groove width [38] . Although AmrZ had reduced affinity for each of these three sequences , the most dramatic effect was mutation of the linker region to 5′-CGCGC . Binding of AmrZ to the sequence 5′-ATATC was reduced suggesting that A/T content of the sequence , which is usually thought to impart flexibility to DNA , was not the main contributor to AmrZ specificity . AmrZ binding to the two sequences harboring mutant linker regions with similar properties to the A-tract sequence ( 5′-TTTTC and 5′-AATTC ) was decreased , suggesting that there are properties unique to the 5′-AAAAC linker sequence in the native amrZ1 binding site that allow for binding specificity . Taken together , these data allow us to propose that binding specificity is directed by intrinsic distortions to the DNA , rather than the flexibility conferred by the A/T rich sequence composition . Recognizing a physical feature of the DNA rather than a specific sequence introduces degeneracy in the recognition sequence that would influence the number of potential recognition sites for AmrZ . We queried the P . aeruginosa PAO1 genome [31] for the number of binding sites with the exact amrZ1 repressor sequence and found 5 sites . If we allow the A/T linker region to be degenerate , the number of potential binding sites increases to 77 . Further biological studies will be required to determine how many of these sites function as actual regulators . Narrowed minor grooves of DNA have a strong correlation between the width and increased electronegative potential of the minor groove [38] . There are many examples of transcription factors that recognize local distortions of the minor groove in addition to sequence specific recognition in both prokaryotic and eukaryotic organisms . The Listeria monocytogenes helix-turn-helix ( HTH ) transcription factor MogR recognizes two half sites on the flaA operator site [39] . The minor groove between the two half sites is distorted , and contributes to MogR specificity for this site . Another example is the myocyte enhancer factor-2 ( MEF2 ) , a member of the MADS-box superfamily , which recognizes a narrowed minor groove on the consensus sequence to bind and activate transcription [40] . These two examples , in addition to others , use positively charged residues , specifically arginine , to recognize and form contacts with the enhanced electronegative potential of the narrow minor groove [38] . However , there are examples of proteins similar to AmrZ that recognize minor groove shape , but do not make any contacts to the minor groove . The classical example is the bacteriophage 434 repressor recognition of six binding sites on the two operator regions , OR and OL , which is greatly modulated by the sequence composition of the central region of these sites [41] . These variations in binding affinities have been shown to be biologically important in directing the lysogenic or lytic fate of bacteriophage 434 [42] . Although the 434 repressor positions an arginine residue near the minor groove , there are no specific contacts by the protein to this region , and mutational analysis shows that this arginine does not contribute to binding affinity [41] . Recently , recognition of the intrinsic structure of narrowed minor grooves has been studied with the DNA bending protein Fis , which is responsible for the compaction of bacterial DNA [43] . An A/T rich ( 5′-AATTT ) narrowed minor groove , located between two Fis binding sites is compressed , allowing for the insertion of two HTH domains into the adjacent major grooves of DNA . Mutations to this narrow minor groove sequence cause changes in binding , with the biggest change occurring by mutating the sequence to a G/C rich sequence ( 5′-GGCGC ) . Narrowed minor grooves between binding half sites have been observed in other RHH protein - DNA structures . In the structure of Arc in complex with DNA , the minor groove between half sites is narrowed to 1 . 2 Å , and the sequence in this region has the sequence 5′-GTGCT [13] . Likewise , in the Streptococcus sp . CopG-DNA structure the minor groove is narrowed to 1 . 9 Å and has the sequence 5′-TTGAG [14] . The DNA in complex with the inc18 plasmid encoded omega protein has a minor groove width of 2 . 7 Å , and the A/T rich sequence 5′-AAAT . Also , due to a fortuitous packing arrangement in the crystal , both bound and free DNA were observed , with the free DNA having similar secondary structure as the omega bound DNA [16] . For each of these proteins , the contributions of the linker region between the two half sites to binding have not been determined . Alterations in the sequence specific half sites , the linker region , or both can modulate affinity for AmrZ to amrZ1; however , the exact mechanism by which AmrZ recognizes the distorted structure of the minor groove remains enigmatic . In the Δ42AmrZ-amrZ1 structure ( Figure 1A ) , there are no positively charged amino acids that contact the minor groove . The extended N-terminus of AmrZ contains an arginine at position 2 which might make these contacts; however , in vitro DNA binding assays performed with various N-terminal truncation mutants of AmrZ showed no decrease in binding to amrZ1 [30] . The phosphate backbone on either side of the narrow minor groove of amrZ1 is contacted on each side by the N-terminus of α-helix 2 from chains A and C ( Figure 1A , 2C ) . This attraction is most likely enhanced due to the positive dipole formed by the N-terminus of the α-helix and the increased electronegative potential of the narrowed minor groove . In addition to functioning as a repressor when bound to amrZ1 , AmrZ binding to the algD site is necessary for the activation of genes responsible for alginate biosynthesis . Interestingly , there are significant divergences between the activator and repressor sequences , and AmrZ affinity to the algD binding site is approximately 24 fold reduced compared to the amrZ1 binding site ( Tables 2 & 3 ) . Using the information from the AmrZ interaction with the repressor amrZ1 binding site , we asked if we could predict how AmrZ interacts with the binding site on the algD promoter ( Figure S1 ) . We set out to determine the features of the algD sequence necessary for AmrZ recognition and activation . By aligning the left half AmrZ binding site on amrZ1 ( 5′-GGC ) to the algD sequence ( positions 5–7 ) , it became apparent that there is no similar right half binding site on algD , and additionally , the sequence of the linker region is also different ( Figure 5A ) . In order to probe the interaction between AmrZ and algD , multiple single nucleotide mutations of the algD site were created , and binding affinity of AmrZ to each of the mutant algD sequences was measured with fluorescence anisotropy . Through mutagenesis of nucleotides in the proposed left half binding site in algD , we show that AmrZ recognizes the sequence 5′-GGC at this site . The guanine nucleotides at positions 5 and 6 on one strand of the algD binding site and positions 7 and 8 on the other strand ( Figure 5A ) were mutated to thymine bases , and the binding affinity of AmrZ to each of these mutant sequences was measured ( Table 3 , Figure 5B ) . The mutation to position 5 resulted in a slight increase to AmrZ affinity , while mutations to positions 6 , 7 , and 8 each resulted in significant reductions in affinity . To determine the nucleotides AmrZ interacts with on the right half of the algD binding site , guanine bases at position 16 on one strand , and positions 13 , 14 , and 17 on the opposite strand of algD were mutated to thymine residues . No significant differences in binding affinity to these sequences are observed ( Table 3 , Figure 5B ) , suggesting that AmrZ interacts with this half site in a different manner than what is observed at amrZ1 . Our previous binding experiments show the same residues , Lys18 , Val20 , and Arg22 are involved in the sequence dependent interactions with algD [30] . In addition , we found that Arg14 is also necessary for binding , with the R14A mutant of AmrZ exhibiting a 5 fold reduction in binding affinity at algD . This arginine residue is also required for transcriptional activation of algD , where R14A AmrZ only retains 3% of WT activity in vivo . From the AmrZ - amrZ1 structure , the extended N-terminus forms a looped structure which positions Arg14 into the major groove of DNA ( Figure 3 ) ; however , no specific contacts between this residue and the amrZ1 DNA are observed , and mutations of this residue have no effects on in vitro and in vivo activity at the repressor site . The differences we observe in interactions of AmrZ with the activator algD binding sequence versus the repressor amrZ1 binding sequence led us to ask if these different binding modes alone could account for activation or repression activity . To test this hypothesis we switched the AmrZ binding site in the algD promoter ( activator ) with the amrZ1 binding site ( repressor ) and introduced this variant into an algD::lacZ transcriptional fusion , which was stably integrated into the genome of the mucoid P . aeruginosa strain FRD1 ( FRD1 palgDamrZ1-lacZ ) . The position and length of the switched binding site were the same as in the native algD promoter . With this construct we measured relative activation at algD with a β-galactosidase activity assay compared to an algD::lacZ transcriptional fusion containing the wild type algD AmrZ binding site ( FRD1 palgD-lacZ ) . The results of this experiment ( Table 4 ) reveal activation of algD remains unchanged when the amrZ1 binding site replaced the native site . Cell lysates from the FRD1 palgDamrZ1-lacZ strain had 527 . 7 units of β-galactosidase activity compared to 536 . 1 units for FRD1 palgD-lacZ . Expression of both palgD-lacZ and palgDamrZ1-lacZ were significantly reduced in amrZ mutant P . aeruginosa strains ( Table 4 ) , indicating that the reporter fusion faithfully reproduced what has been observed previously regarding AmrZ activation of algD [5] , [10] , [30] . The activation of algD with the amrZ1 repressor site at its promoter supports a model in which AmrZ binding alone does not regulate activation or repression of transcription , but rather interactions of AmrZ with other regulators at the amrZ and algD promoters likely contribute to repression or activation , respectively . This is consistent with the previous evidence that the AlgB , AlgR , IHF , and CysB regulators are known to bind on the algD promoter and are necessary for activation [44]–[48] , suggesting a possible interaction of AmrZ with one of these proteins . To date , no other regulators have been identified to bind the amrZ promoter; however , it is possible one of these same regulators may also interact with AmrZ there as well . An additional determinant likely dictating activation versus repression is the position of the AmrZ binding site relative to the start of transcription , which differs for algD ( −282 ) and amrZ1 ( −93 ) . AmrZ functions as both a transcriptional activator and repressor of P . aeruginosa virulence genes . We have determined the structure of Δ42 AmrZ in complex with an 18 base pair oligonucleotide containing the amrZ1 binding site . AmrZ binding to this site results in the repression of amrZ transcription . By combining structural and biochemical data , we developed a model for AmrZ recognition at amrZ1 . Using the suggested terminology from the recent review by Rohs et al . [49] , the protein-DNA specificity of AmrZ can be classified by major groove base readout through protein residues in the β-sheet with two GGC half sites in the DNA . This is combined with local shape readout utilizing minor groove distortions in the linker region between the half sites . We also probed the interaction of AmrZ with another biologically important binding site algD , which leads to the activation of alginate biosynthesis . In contrast , we observed stark differences in the physical interactions that AmrZ makes with the algD sequence that suggest the protein likely utilizes a different mode of recognition at this site . AmrZ binds the algD sequence with lower affinity , and mutagenesis of the algD sequence shows that only one half site contributes to AmrZ binding . However , these differences in protein binding at the promoter sequences are alone not sufficient to account for the activator or repressor activity of AmrZ , and likely the position of AmrZ binding at the promoter and/or protein interactions with other regulators are also necessary for biological function .
The gene encoding WT AmrZ was PCR amplified from the P . aeruginosa strain PAO1 with the primers amrZ_F ( 5′-CGCCATCACATATGCGCCCACTGAAACAGGC ) and amrZ_wt_R ( 5′-CGCCATCAGGATCCTCAGGCCTGGGCCAGCTC ) . The resulting gene product was then inserted into a modified pET19 expression vector ( Novagen ) which encodes an N-terminal poly-Histidine tag , followed by a Rhinovirus 3C protease cleavage site , which permits the removal of the affinity tag ( PreScission Protease , GE Healthcare ) . The pET19-amrZ vector was transformed into E . coli C41 ( DE3 ) cells for expression . One liter of LB-Broth ( Luria-Bertani ) supplemented with 50 µg/ml of ampicillin was inoculated with 10 ml of an overnight culture of the C41 cells containing the pET19-amrZ vector . The cells were grown at 37°C to an OD600 = 0 . 5 , and induced with 1 mM isopropyl β-D-thiogalactopyranoside ( IPTG ) at 16°C for 20 hours . Prior to induction with IPTG , cells were rapidly cooled on ice to 20°C to bring the temperature of the culture close to the induction temperature . Induction of the cells at low temperature was necessary for protein solubility during overexpression . Cells were harvested by centrifugation , resuspended in lysis buffer ( 100 mM KH2PO4 pH 7 . 5 , 500 mM NaCl , 10% glycerol , 4 M urea ) , and lysed using an EmulsiFlex C-5 cell homogenizer ( Avestin ) . Cell debris was removed at 30 , 000× g and the supernatant was passed over a 10 ml Ni-NTA ( Qiagen ) column equilibrated with lysis buffer . This column was washed with 20 column volumes of wash buffer 1 ( 100 mM KH2PO4 pH 7 . 5 , 500 mM NaCl , 10% glycerol , 35 mM imidazole , 3 M urea ) , followed by 10 column volumes of wash buffer 2 ( 100 mM KH2PO4 pH 7 . 5 , 500 mM NaCl , 10% glycerol , 50 mM imidazole , 2 M urea ) . Bound AmrZ was eluted with elution buffer ( 100 mM KH2PO4 pH 7 . 5 , 500 mM NaCl , 10% glycerol , 500 mM imidazole , 1 M urea ) , treated with PreScission Protease according to the manufacturer's directions , and dialyzed over night at 4°C against 100 mM Bis-Tris pH 5 . 5 , 100 mM NaCl , 5% glycerol , 2 mM dithiothreitol ( DTT ) , and 0 . 5 mM EDTA . The partial denaturing conditions introduced by the 4 M urea were necessary for protein solubility and affinity to the Ni-NTA column , and no change in secondary structure or DNA binding affinity was observed compared to protein purified without urea present . AmrZ was then passed over a MonoS cation exchange column , and eluted with a 0 . 1 M–1 M gradient of NaCl . Purity of the peak fractions was verified by SDS-PAGE , and fractions containing pure WT AmrZ were pooled . For crystallization experiments , AmrZ was dialyzed against 100 mM Bis-Tris pH 5 . 5 , 100 mM NaCl , 2% glycerol , while for DNA binding assays , AmrZ was dialyzed against a buffer containing 100 mM Bis-Tris pH 6 . 5 , 150 mM NaCl , 5% glycerol . WT AmrZ was then concentrated to 20 mg/ml for crystallization experiments , or 1 mg/ml for DNA binding assays , aliquoted , flash frozen in liquid nitrogen , and stored at −80°C . Concentration of WT AmrZ was measured using the BCA assay ( Thermo Scientific ) using a standard curve of lysozyme as a reference . The Δ42 C-terminal truncation mutant of AmrZ was amplified from the P . aeruginosa strain PAO1 using the primers amrZ_F and amrZ_Δ42_R ( 5′-CGCCATCAGGATCCTCAAACACCGAGATTGTCTTG ) . Expression and purification of this protein was carried out using the procedures outlined for WT AmrZ . Crystallization trials of AmrZ were carried out by screening multiple AmrZ C-terminal deletion constructs against a library of double stranded DNA oligonucleotides containing the amrZ1 binding site ( Integrated DNA Technologies ) . Initial crystals were obtained only with the Δ42 AmrZ C-terminal truncation and an 18 bp oligonucleotide in a condition containing 6% PEG 8 K , 0 . 1 M MES pH 6 . 0 , 0 . 1 M CaCl2 , 0 . 1 M NaCl . For experimental phasing , selenomethionine ( Se-Met ) derivatized Δ42 AmrZ was prepared using published methods [50] . Purification of this protein was performed using the methods described above , with the only exception being the addition of 5 mM DTT in the final dialysis buffer . Crystals of the Se-Met Δ42 AmrZ - 18 bp amrZ1 complex were obtained by mixing the protein and DNA in a 1∶1 . 5 molar ratio ( 810 µM AmrZ: 607 . 5 µM amrZ1 ) in the presence of 50 mM MgSO4 . This complex was crystallized by the hanging drop vapor diffusion method at 25°C at a 1∶1 ratio with reservoir solution containing 3% PEG8K , 0 . 1 M MES pH 6 . 0 , 0 . 15 M NaCl , and 2 mM TCEP pH 8 . 0 . Crystals grew within 2–3 weeks and were soaked in a solution containing 20% 2-methyl-1 , 3 propanediol for cryo-protection before being frozen in liquid nitrogen for data collection . Diffraction data for crystals containing the Δ42 AmrZ: 18 bp amrZ1 complex were collected on beamline X25 at the National Synchrotron Light Source ( NSLS ) , Brookhaven National Labs . The dataset was collected at the selenium peak , with an X-ray wavelength of 0 . 9793 nm . Indexing , integration and scaling of the data were performed using HKL2000 program suite [51] . Phasing of the structure was performed using SAD methods with the program SOLVE [52] , and density modification was performed using RESOLVE [53] . Manual model building was performed in Coot [54] , and refinement was carried out using the programs REFMAC5 [55] within the CCP4 program suite [56] , and CNS [57] . Data collection and refinement statistics are found in Table 1 . The atomic coordinates and structure factors have been deposited in the Protein Data Bank under the PDB id 3QOQ . Binding affinity of the various amrZ1 and algD binding site mutants were performed using fluorescence anisotropy as previously described [30] , [58] . In brief , increasing concentrations of WT AmrZ were incubated in a reaction ( 25 µl ) containing 1 nM 22-mer 5′-6-carboxy-fluorescein ( 6-FAM ) labeled DNA oligonucleotide ( IDT ) containing either the amrZ1 or the algD sequences , 100 nM nonspecific DNA of random sequence , 100 µg/ml bovine serum albumin ( BSA ) , 100 mM Bis-Tris pH 6 . 5 , 150 mM NaCl , and 5% glycerol . DNA concentrations were kept 1 nM ( <<Kd ) to ensure equilibrium measurements of binding constants . Anisotropy measurements were recorded at 25°C on a Safire2 microplate reader with a fluorescence polarization module ( Tecan Group , Ltd . ) , using an excitation wavelength of 470 nm and an emission wavelength of 525 nm . Anisotropy data were scaled and normalized using Equation 1 below: ( 1 ) In this equation , Aobs is the measured anisotropy value for each AmrZ concentration , A0 is the anisotropy of the unbound DNA , and Amax is the maximum anisotropy observed in each experiment . The dissociation constant ( Kd ) was calculated by fitting the data to the equation for a single state binding model ( Equation 2 ) . ( 2 ) Fitting of the data to Equation 2 was performed using SigmaPlot . Raw data and fits for AmrZ binding to each DNA sequence can be found in Figures S2 and S3 , with the results from these experiments being presented in Tables 2 and 3 . Results presented are the averages of four independent experiments . The program CURVES+ [59] was used to measure the major and minor groove widths of the amrZ1 DNA . The buried surface areas formed by protein-DNA interactions and by protein-protein interactions were measured using the programs AREAIMOL in the CCP4 suite [56] and PDBsum [60] , respectively . Ramachandran statistics found in Table 1 were calculated with PDBsum [60] . Electrostatic surface representations of the protein and DNA were created by first generating a PQR file , which contains charge and radius information for each atom , with the program PDB2PQR [61] , followed by visualization of the electrostatic surface using the APBS program [62] . All figures of structural representations were prepared using the program PyMol [63] . The AmrZ binding site ( ABS ) ( CCATTGGCCATTACCAGCCTCCC ) in the algD promoter was replaced by the same-length amrZ1 ABS ( GTACTGGCAAAACGCCGGCACGC ) from the amrZ promoter by site-directed mutagenesis [64] . Mutagenesis was achieved by primers algD74 ( GCGTGCCGGCGTTTTGCCAGTACATTACGCCGGAGATGGCATTTC ) and algD75 ( GTACTGGCAAAACGCCGGCACGCGCCATTACATGCAAATTACGATTGC ) , together with flanking primers algD65 ( CCCCAAGCTTCTCTTTCGGCACGCCGAC ) and algD66 ( CCGGGATCCCCGACATAGCCCAAACCAAAG ) . PCR products of algD65/algD74 and algD66/algD75 were denatured and hybridized . The products were used as the template for the second PCR , with primers algD65 and algD66 . With HindIII and BamHI sticky ends , the final PCR product was cloned into HindIII and BamHI double digested mini-CTX-lacZ transcriptional fusion vector [65] , resulting in a new plasmid pBX8 , which harbors modified algD::lacZ transcriptional fusion ( palgDamrZ1-lacZ ) . The sequence of the palgDamrZ1-lacZ promoter was verified by PCR and sequencing . The plasmid pBX8 was transferred into P . aeruginosa FRD1 using E . coli strain SM10 . The modified palgDamrZ1-lacZ transcriptional fusion was integrated at the attB site within the chromosome of FRD1 and FRD1 ΔamrZ [30] , and the unnecessary portion of the fragment was removed by pFLP2 [66] , resulting in FRD1 palgDamrZ1-lacZ or FRD1 ΔamrZ palgDamrZ1-lacZ , respectively . P . aeruginosa in mid-log phase were pelleted and washed with Z-buffer ( 110 mM Na2HPO4 , 45 mM NaH2PO4 , 10 mM KCl , 2 mM MgSO4 , pH7 . 0 ) . Cells were lysed through three rounds of fast freezing at −80°C then thawing at 37°C , followed by mild sonication . Samples were centrifuged at 18 k× g and 4°C for 10 min at 21 , 000× g . The supernatants were analyzed for β-galactosidase activity by mixing 10 µl of a sample supernatant with 80 µl of Z-buffer . To start the reaction 20 µl of 4 mg/ml orthonitrophenol was added . The color change in the reaction was monitored with time and reactions were stopped by addition of 40 µl 1 M Na2CO3 for reading . Wild type FRD1 palgD::lacZ transcriptional fusion was the positive control , and FRD1 lacZ with no promoter acted as the negative control . The absorbance at both 420 nm and 550 nm of each reaction solution was read in a Molecular Devices M5 microplate reader . Miller Units were calculated from different strains as outlined [67] . | The bacterium Pseudomonas aeruginosa causes a variety of human infections and is the leading cause of death in patients with cystic fibrosis . The main reason for the severity of these infections arises from the ability of P . aeruginosa to express virulence factors that protect it from the host immune system . Several of these processes are controlled by a transcription factor called AmrZ , a potential target for anti-microbial therapeutics . AmrZ is unusual in that it has the ability to both activate some genes , such as for alginate biofilm , and repress others , as with flagellum and itself . Here we determine the three dimensional structure of AmrZ bound to DNA containing a repressor sequence . Our structure shows the specific interactions the protein makes with the DNA for binding and repression . It also reveals that both the sequence and shape of the DNA are important for tight association . We next examined the binding of the protein to DNA containing an activator sequence and found that it has different interactions . However , by switching the AmrZ binding site at algD , where it acts as an activator , for a repressor binding sequence in P . aeruginosa , we show that differences in binding alone do not account for transcriptional regulation . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] | [
"microbial",
"metabolism",
"macromolecular",
"assemblies",
"microbiology",
"dna",
"transcription",
"protein",
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"bacterial",
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] | 2012 | The Transcription Factor AmrZ Utilizes Multiple DNA Binding Modes to Recognize Activator and Repressor Sequences of Pseudomonas aeruginosa Virulence Genes |
The functional significance of correlations between action potentials of neurons is still a matter of vivid debate . In particular , it is presently unclear how much synchrony is caused by afferent synchronized events and how much is intrinsic due to the connectivity structure of cortex . The available analytical approaches based on the diffusion approximation do not allow to model spike synchrony , preventing a thorough analysis . Here we theoretically investigate to what extent common synaptic afferents and synchronized inputs each contribute to correlated spiking on a fine temporal scale between pairs of neurons . We employ direct simulation and extend earlier analytical methods based on the diffusion approximation to pulse-coupling , allowing us to introduce precisely timed correlations in the spiking activity of the synaptic afferents . We investigate the transmission of correlated synaptic input currents by pairs of integrate-and-fire model neurons , so that the same input covariance can be realized by common inputs or by spiking synchrony . We identify two distinct regimes: In the limit of low correlation linear perturbation theory accurately determines the correlation transmission coefficient , which is typically smaller than unity , but increases sensitively even for weakly synchronous inputs . In the limit of high input correlation , in the presence of synchrony , a qualitatively new picture arises . As the non-linear neuronal response becomes dominant , the output correlation becomes higher than the total correlation in the input . This transmission coefficient larger unity is a direct consequence of non-linear neural processing in the presence of noise , elucidating how synchrony-coded signals benefit from these generic properties present in cortical networks .
Simultaneously recording the activity of multiple neurons provides a unique tool to observe the activity in the brain . The immediately arising question of the meaning of the observed correlated activity between different cells [1] , [2] is tightly linked to the problem how information is represented and processed by the brain . This problem is matter of an ongoing debate [3] and has lead to two opposing views . In one view , the high variability of the neuronal response [4] to presented stimuli and the sensitivity of network activity to the exact timing of spikes [5] suggests that the slowly varying rate of action potentials carries the information in the cortex . A downstream neuron can read out the information by pooling a sufficient number of merely independent stochastic source signals . Correlations between neurons may either decrease the signal-to-noise ratio [6] or enhance the information [7] in such population signals , depending on the readout mechanism . Correlations are an unavoidable consequence of cortical connectivity where pairs of neurons share a considerable amount of common synaptic afferents [8] . Recent works have reported very low average correlations in cortical networks on long time scales [9] , explainable by an active mechanism of decorrelation [10] , [11] , [12] . On top of these correlations inherent to cortex due to its connectivity , a common and slowly varying stimulus can evoke correlations on a long time scale . In the other view , on the contrary , theoretical considerations [13] , [14] , [15] , [16] argue for the benefit of precisely timed action potentials to convey and process information by binding elementary representations into larger percepts . Indeed , in frontal cortex of macaque , correlated firing has been observed to be modulated in response to behavioral events , independent of the neurons' firing rate [17] . On a fine temporal scale , synchrony of action potentials [18] , [19] , [20] has been found to dynamically change in time in relation to behavior in primary visual cortex [21] and in motor cortex [17] , [22] . The observation that nearby neurons exclusively show positive correlations suggests common synaptic afferents to be involved in the modulation of correlations [23] . In this view , the measure of interest are correlations on a short temporal scale , often referred to as synchrony . The role of correlations entails the question whether cortical neurons operate as integrators or as coincidence detectors [18] , [24] . Recent studies have shown that single neurons may operate in both regimes [25] . If the firing rate is the decisive signal , integrator properties become important , as neural firing is driven by the mean input . As activity is modulated by the slowly varying signal , correlations extend to long time scales due to co-modulation of the rate . Integrators are thus tailored to the processing of rate coded signals and they transmit temporal patterns only unreliably . Coincidence detectors preferentially fire due to synchronously arriving input . The subthreshold membrane potential fluctuations reflect the statistics of the summed synaptic input [26] , which can be used to identify temporally precise repetition of network activity [27] . A direct probe for the existence of synchronous activity are the resulting strong deflections due to synchronous arrival of synaptic impulses . Such non-Gaussian fluctuations have indeed been observed in auditory cortex in vivo [28] and in the barrel cortex of behaving mice [29] . In this regime , coincidence detector properties become crucial . Coincidence detectors are additionally sensitive to stimulus variance [25] , [30] and correlations between pairs of neurons in this regime arise from precisely timed firing . This type of correlation is unaffected by firing rate , can encode stimulus properties independently and moreover arises on short time scales [25] . The pivotal role of correlations distinguishing the two opposing views and the appearance of synchrony at task-specific times [17] , [21] , [22] suggests to ask the following question , illustrated in Fig . 1A: Can the experimentally observed synchrony between the activity of two neurons be explained solely by to the convergent connectivity with independently activated shared inputs or are in addition correlations among the afferents to both neurons required ? If shared input is sufficient , synchrony is just a side effect of the convergent connectivity in the cortex . However , if synchronous activation of common afferents is required , it is likely that spike synchrony is used to propagate information through the network . A functional interpretation is assigned to synchrony by the picture of the cell assembly [13] , [14] , [31] , [32] , where jointly firing neurons dynamically form a functionally relevant subnetwork . Due to the local connectivity with high divergence and convergence , any pair of neurons shares a certain amount of input . This common input may furthermore exhibit spike synchrony , representing the coherent activity of the other members of the cell assembly . In the assembly picture , the synchronous input from peer neurons of the same assembly is thus considered conveying the signal , while theTe input from neurons outside of the assembly is considered as noise [33] . One particular measure for assessing the transmission of correlation by a pair of neurons is the transmission coefficient , i . e . the ratio of output to input correlation . When studying spiking neuron models , the synaptic input is typically modeled as Gaussian white noise , e . g . by applying the diffusion approximation to the leaky integrate-and-fire model [34] . In the diffusion limit , the transmission coefficient of a pair of model neurons receiving correlated input mainly depends on the firing rate of the neurons alone [35] , [36] . For low correlations , linear perturbation theory well describes the transmission coefficient , which is always below unity , i . e . the output correlation is bounded by the input correlation , pairs of neurons always lose correlation [37] . Analytically tractable approximations of leaky integrate-and-fire neural dynamics have related the low correlation transmission to the limited memory of the membrane voltage [38] . The transmission is lowest if neurons are driven by excitation and inhibition , when fluctuations dominate the firing . In the mean driven regime the transmission coefficient can reach unity for integral measures of correlation [38] . Understanding the influence of synchrony among the inputs on the correlation transmission requires to extend the above mentioned methods , as Gaussian fluctuating input does not allow to represent individual synaptic events , not to mention synchrony . Therefore , in this work we introduce an input model that extends the commonly investigated Gaussian white noise model . We employ the multiple interaction process ( MIP ) [39] to generate an input ensemble of Poisson spike trains with a predefined pairwise correlation coefficient . We use these processes containing spike synchrony as the input common to both neurons and model the remaining afferents as independent Poisson spike trains . Furthermore , contrary to studies that measure the integrated output correlation ( count correlation ) [35] , [36] , we primarily consider the output correlation on the time scale of milliseconds , i . e . the type of correlation determined by the coincidence detection properties of neurons . In section “Results” we first introduce the neuron and input models . In section “Understanding and Isolating the Effect of Synchrony” we study the impact of input synchrony on the firing properties of a pair of leaky integrate-and-fire neurons with current based synapses . Isolating and controlling this impact allows us to separately study the effect of input synchrony on the one hand and common input on the other hand on the correlation transmission . In section “Correlation Transmission in the Low Correlation Limit” and “Correlation Transmission in the High Correlation Limit” we present a quantitative explanation of the mechanisms involved in correlation transmission , in the limit of low and high correlation , respectively , and show how the transmission coefficient can exceed unity in the latter case . In section “Discussion” we summarize our findings in the light of previous research , provide a simplified model that enables an intuitive understanding and illustrates the generality of our findings . Finally , we discuss the limitations of our theory and consider possible further directions .
These two observations – the increase of input correlation and output firing rate induced by input synchrony – foil our objective to understand the sole impact of synchronous input events on the correlation transmission of neurons . In the following we will therefore first provide a quantitative description of the effect of finite sized presynaptic events on the membrane potential dynamics and subsequently describe a way to isolate and control this effect . The synchronous arrival of events has a -fold effect on the voltage due to the linear superposition of synaptic currents . The total synaptic input can hence be described by a sequence of time points and independent and identically distributed ( i . i . d ) random number that assume a discrete set of synaptic amplitudes each with probability . The train of afferent impulses follows Poisson statistics with some rate . Assuming small weights and high , stationary input rate , a Kramers-Moyal expansion [40] , [41] , [42] can be applied to ( 1 ) to obtain a Fokker-Planck equation for the membrane potential distribution ( 3 ) Only the first two moments and of the amplitude distribution enter the first ( ) and second ( ) infinitesimal moments as [43] , cf . Appendix Input–Output Correlation of an Integrate–and–Fire Neuron for a detailed derivation ( 4 ) In the absence of a threshold , the stationary density follows from the solution of as a Gaussian with mean and variance . Equation ( 3 ) and ( 4 ) hold in general for excitatory events with i . i . d . random amplitudes arriving at Poisson time points . Given the common excitatory afferents' activities are generated by a MIP process , the number of synchronized afferents follows a binomial distribution , with moments and . Note that throughout the manuscript we choose the number of common inputs to be an integer , and we restrict the values of accordingly . The total rate of arriving events is independent of , as is the contribution to the mean membrane potential . Further we assume the neurons to be contained in a network that is in the balanced state , i . e . , and that all afferents have the same rate . Thus , excitation and inhibition cancel in the mean so that . Due to the independence of excitatory and inhibitory spike trains they contribute additively to the variance in ( 4 ) . The variance due to inhibitory afferents with rate is , with . An analog expression holds for the contribution of unsynchronized disjoint excitatory afferents . The contribution of excitatory afferents from the MIP follows from ( 4 ) as . So together we obtain ( 5 ) Fig . 1B shows that ( 5 ) is in good agreement with simulation results . We are further interested in describing the correlation between the membrane potentials of both neurons . The covariance is caused by the contribution from shared excitation , in addition to the contribution from shared inhibition , which together result in the correlation coefficient ( 6 ) Again , Fig . 1C shows that ( 6 ) is in good agreement with simulation results . In order to isolate and control the effect of the synchrony parameter on the variance ( 5 ) and the input correlation ( 6 ) , in the following we will compare two distinct scenarios: In the first scenario , common input alone causes the input correlation and spiking synchrony among afferents is zero ( ) . In the second scenario we generate the same amount of input correlation but realize it with a given amount of spike synchrony . In order to have comparable scenarios , we keep the marginal statistics of individual neurons the same , measured by the membrane potential mean and variance . In scenario 1 ( ) the input correlation ( 6 ) equals the common input fraction . In scenario 2 ( ) the same input correlation can be achieved by appropriately decreasing the fraction of common inputs to . The value of is determined by the positive root of the quadratic equation ( 6 ) solved for . In neither scenario does the input correlation depend on the afferent rate . In scenario 2 we can hence choose in order to arrive at the same variance as in scenario 1 . To this end we solve ( 5 ) for to obtain the reduced afferent rate . We evaluate this approach by simulating the free membrane potential of a pair of leaky integrate-and-fire neurons driven by correlated input . For different values of we chose and , shown in Fig . 2A and B , to keep the variance and the correlation constant . Fig . 2A shows that the adjustment of the common input fraction becomes substantial only for higher values of : while for the reduced is only slightly smaller than , for and it is reduced to . Fig . 2B shows that even for small amounts of input synchrony , needs to be decreased considerably in order to prevent the increase of membrane potential variance ( Fig . 1B ) . In the extreme case of and ( both neurons receive identical and strongly synchronous excitatory input ) an initial input firing rate of Hz needs to be decreased to Hz . Fig . 2C and D confirm that indeed both the correlation and the variance of the free membrane potential remain constant throughout the whole range of and for all simulated values of . In order to study the transmission of correlation by a pair of neurons , we need to ensure that the single neuron's working point does not change with the correlation structure of the input . The diffusion approximation ( 3 ) suggests , that the decisive properties of the marginal input statistics are characterized by the first ( ) and second moment ( ) . As we supply balanced spiking activity to each neuron , the mean is solely controlled by the resting potential , as outlined above . For any given value of and , choosing the afferent rate ensures a constant second moment . Consequently , Fig . 3 confirms that the fixed working point ( ) results in an approximately constant neural firing rate for weak to moderate input synchrony . For strong synchrony , fluctuations of the membrane potential become non-Gaussian and the firing rate decreases; the diffusion approximation breaks down . In studies which investigate the effect of common input on the correlation transmission of neurons , the input correlation is identical to the common input fraction [35] , [36] . In the presence of input synchrony this is obviously not the case ( Fig . 1C ) . Choosing the afferent rate and the common input fraction according to and , respectively , enables us to realize the same input correlation with different contributions from shared inputs and synchronized events . We may thus investigate how the transmission of correlation by a neuron pair depends on the relative contribution of synchrony to the input correlation . Fig . 3A shows the output synchrony as a function of for four fixed values of input synchrony . As the input correlation is by construction the same for all values of , changes in the output synchrony directly correspond to a different correlation transmission coefficient . Even weak spiking synchrony ( ) in the common input effectively increases the output synchrony , compared to the case where the same input correlation is exclusively caused by common input ( ) . Stronger synchrony ( and ) further increases this transmission . In Fig . 3B we confirm that the increase of output spike synchrony is not caused by an increase of the output firing rate of the neurons , but rather their rate remains constant up to intermediate values of . The drastic decrease of the output firing rate for does not rebut our point , but rather strengthens it: correlation transmission is expected to decrease with lower firing rate [35] , [36] for Gaussian inputs . However , here we observe the opposite effect in the case of strongly non-Gaussian inputs due to synchronous afferent spiking . We will discuss this issue in the following paragraph , deriving an analytical prediction for the correlation transmission . Moreover , we observe that the increased transmission is accompanied by a sharpening of the correlation function with respect to the case of ( cf . Fig . 3C and D ) . For correlated inputs caused by common inputs alone ( no synchrony , ) or by weak spiking synchrony ( ) the transmission curves in Fig . 3A are always below the identity line . This means that the neural dynamics does not transmit the correlation perfectly , but rather causes a decorrelation . Recent work has shown that the finite life time of the memory stored in the membrane voltage of a leaky integrate-and-fire neuron is directly related to this decorrelation [38] . Quantitative approximations of this decorrelation by non-linear threshold units can be understood in the Gaussian white noise limit [37] , [35] , [36] . For input correlation caused by spiking synchrony , however , we observe a qualitatively new feature here . In the presence of strong spiking synchrony ( ) , in the regime of high input correlation ( ) the correlation transmission coefficient exceeds unity . In other words , the neurons correlate their spiking activity at a level that is higher than the correlation between their inputs . In order to obtain a quantitative understanding of this boost of correlation transmission by synchrony , in the following two sections we will in turn investigate the mechanisms in the limit of low and high input correlations , respectively . In the limit of low input correlation Fig . 3 suggests that the main difference of the correlation functions is in the central peak caused by coincident firing of both cells . As the remainder of the covariance function only changes marginally , we can as well consider integral measures of the covariance function . Calculating the time integral of the covariance function can conveniently be accomplished by an established perturbative approach that treats the common input as a small perturbation and only requires the DC-susceptibility of the neuron to be determined [37] , [44] , [35] , [36] . As the covariance function typically decays to zero on a time scale of about , the integral correlation is well approximated by the covariance between spike counts in windows of , considered in this subsection . For Gaussian white noise input and in the limit of low input correlation , the correlation transmission is well understood [37] , [44] , [35] , [36] . The employed diffusion approximation assumes that the amplitudes of synaptic events are infinitesimally small . For uncorrelated Poisson processes and large number of afferents , the theory is still a fairly good approximation . For small synaptic jumps approximate extensions are known [45] , [46] and exact results can be obtained for jumps with exponentially distributed amplitudes [47] . However , in order to treat spiking synchrony in the common input to a pair of neurons , we need to extend the perturbative approach here . Before deriving an expression for the correlation transmission by a pair of neurons , we first need the firing rate deflection of a neuron caused by a single additional synaptic impulse of amplitude at on top of synaptic background noise . Within the diffusion approximation , the background afferent input to the neuron can be described by the first two moments and ( 4 ) . We denote as the centralized ( zero mean ) spike train and as the excursion of the firing rate of neuron with respect to the base rate caused by the additional impulse and averaged over the realizations of the background input , illustrated in Fig . 4B . An additional impulse is equivalent to an instantaneous perturbation of both , the first ( ) and the second ( ) moment with prefactors and , respectively , as shown in section “Impulse Response to Second Order” . The DC-susceptibility is therefore a quadratic function in the amplitude ( 7 ) where the prefactors and depend on the working point of the neuron and hence on the background noise parameterized by and . A similar approximation to second order in was performed for periodic perturbations of the afferent firing rate [48 , cf . Appendix , eq . A3] and for impulses in [12 , cf . App . 4 . 3 and Fig . 8 for an estimate of the validity of the approximation] . Note that this approximation extends previous results that are first order in [49] , [46] . The DC-susceptibility can be interpreted as the expected number of additional spikes over baseline caused by the injected pulse of amplitude . As the marginal statistics of the inputs to both neurons are the same they fire with identical rates . Each commonly received impulse to both cells contributes to the cross covariance function between the outgoing spike trains , defined as ( 8 ) where the expectation value is taken over realizations of the disjoint inputs , the common input , and over time . drops to zero for . The average over realizations of the afferent input ensembles can be performed separately over realizations of the common and the disjoint inputs , [49] , leading toTransforming to frequency domain with respect to and applying the Wiener-Khinchine theorem [50] , the cross spectrum between the centralized spike trains readsWith the definition of the Fourier transform , for the cross spectrum equals the time integral of the cross correlation function . Performing the average over the common sources we obtain two contributions , due to synchronous excitatory pulses from the MIP [39] , giving rise to synchronously arriving events , being distributed according to a binomial distribution , and due to common inhibitory inputs each active with Poisson statistics and rate , leading towhere is the integral of the response to a single impulse of amplitude . So with ( 7 ) we have and finally obtain ( 9 ) where are the moments of the binomial distribution ( Section “Moments of the Binomial Distribution” ) . In order to obtain a correlation coefficient , we need to normalize the integral of ( 9 ) by the integral of the auto-covariance of the neurons' spike trains . This integral equals [51] , [44] , with the Fano factor . In the long time limit the Fano factor of a renewal process equals the squared coefficient of variation [52] , which can be calculated in the diffusion limit [40 , App . A1] . Thus , we obtain ( 10 ) Fig . 4A shows that the output spike correlation of a pair of neurons is fairly well approximated by in the lower correlation regime . While the approximation is good over almost the whole displayed range of for and , for the theory only works for values of in agreement with previous studies [35] , [36] applying a similar perturbative approach to the case of Gaussian input fluctuations . In order to understand how the neurons are able to achieve a correlation coefficient larger than one , we need to take a closer look at the neural dynamics in the high correlation regime . We refer to the strong pulses caused by synchronous firing of numerous afferents as MIP events . Fig . 5A shows an example of the membrane potential time course that is driven by input in the high correlation regime . At sufficiently high synchrony as shown here , most MIP events elicit a spike in the neuron , whereas fluctuations due to the disjoint input alone are not able to drive the membrane potential above threshold . Thus , in between two MIP events the membrane potential distribution of each neuron evolves independently and fluctuations are caused by the disjoint input alone . Fig . 5B shows the time-dependent probability density of the membrane potential , triggered on the time of arrival of a MIP event . We observe that most MIP events cause an action potential , followed by the recharging of the membrane after it has been reset to at . After a short period of repolarization the membrane potential quickly reaches its steady state . The contribution of the common , excitatory afferents to the membrane potential statistics is limited to those occasional strong depolarizations . Between two such events they neither contribute to the mean nor to the variance of . Hence the effective mean and variance of the membrane potential are due to the disjoint input alone , given by and with and . Fig . 5C shows in gray the empirical distribution of the membrane potential between two MIP events after it has reached the steady state . It is well approximated by a Gaussian distribution with mean and variance . The membrane potential can therefore be approximated as a threshold-free Ornstein-Uhlenbeck process [53] , [54] . Let us now recapitulate these last thoughts in terms of a pair of neurons: In the regime of synchronized high input correlation ( e . g . , ) , MIP events become strong enough so that most of them elicit a spike in both neurons . At the same time , the uncorrelated , disjoint sources ( which can be considered as sources of noise ) induce fluctuations of the membrane potential which are , however , not big enough to drive the membrane potential above threshold . Thus , while the input to both neurons still contains a considerable amount of independent noise , their output spike trains are ( for sufficiently high ) a perfect duplicate of the mother spike train that generates the MIP events in their common excitatory input , explaining the observed correlation transmission coefficient larger than unity . Note that this is the reason for the drastic decrease of the output firing rate in Fig . 3B , which in the limit of high input correlation approaches the adjusted input firing rate ( Fig . 2B ) . We would like to obtain a qualitative assessment of the correlation transmission in the high correlation input regime . Since the probability of output spikes caused by the disjoint sources is vanishing , the firing due to MIP events inherits the Poisson statistics of the mother process . Consequently , the auto-covariance function of each neurons' output spike train is a -function weighted by its rate , where is the probability that a MIP event triggers an outgoing spike in one of the neurons . The output correlation can hence be approximated by the ratio ( 11 ) where is the probability that a MIP event triggers an outgoing spike in both neurons at the same time . Note that the approximation ( 11 ) holds for arbitrary time scales , as the spike trains have Poisson statistics in this regime . In order to evaluate and , we use the simplifying assumption that the last MIP event at caused a reset of the neuron to , so the distribution of the membrane potential evolves like an Ornstein-Uhlenbeck process as [54] ( 12 ) which is the solution of ( 3 ) with initial condition . We evaluate from the probability mass of the voltage density shifted across threshold by an incoming MIP event as ( 13 ) where the survivor function is the probability that after a MIP event occurred at the next one has not yet occurred at . So is the probability that no MIP event has occurred in and it will occur in [52] . The binomial factor is the probability for the amplitude of a MIP event to be and the last integral is the probability that a MIP event of amplitude causes an output spike [46] . We first express in terms of the error function using ( 12 ) with the substitution , to obtain ( 14 ) where we used the definition of the error function . We further simplify the first integral in ( 13 ) with the substitution tothus finally obtaining ( 15 ) where we introduced as a shorthand for ( 14 ) with and expressed in terms of the substitution variable as and , following from ( 12 ) . In order to approximate the probability that the MIP event triggers a spike in both neurons we need to square the second integral in ( 13 ) , because the voltages driven by disjoint input alone are independent , so their joint probability distribution factorizes , leading to ( 16 ) It is instructive to observe that , because given by ( 14 ) is a probability . Therefore it follows that , with equality reached if or . Hence from the definitions ( 15 ) and ( 16 ) it is obvious that , as it should be and the ratio ( 11 ) defines a properly bounded correlation coefficient in the high input correlation regime . So far , we have considered both neurons operating at a fixed working point , defined by the mean and variance ( 4 ) . Due to the non-linearity of the neurons we expect the effect of synchronous input events on their firing to depend on the choice of this working point . We therefore performed simulations and computed ( 2 ) using four different values for the mean membrane potential ( Fig . 6 ) . This was achieved by an appropriate choice of a DC input current and accordingly adjusting the input firing rate in order to keep the mean firing rate constant ( Fig . 6A , inset ) . The data points from simulations in Fig . 6A show that different working points of the neurons considerably alter the correlation transmission in the limit of high input correlation . At working points near the threshold ( ) MIP events more easily lead to output spikes , thereby boosting the transmission of correlation , as compared to working points that are further away from the threshold ( ) . Solid lines in Fig . 6A furthermore show that ( 11 ) indeed provides a good approximation of the output spike correlation when the input to both neurons is strongly synchronized . Obviously , the assumption has to hold that the probability density of the membrane potential is sufficiently far from the threshold , which for is only the case if . Hence , the approximation becomes less accurate for lower input correlations , as expected . Note that , as opposed to Fig . 1E , the effective common input fraction in Fig . 6A is much lower than . Fig . 6B shows the same data as a function of the actual fraction of shared afferents . It reveals that the gain of correlation transmission above unity is already reached at fractions of common input as low as ( for ) , which is a physiologically plausible value . A further approximation of ( 15 ) and ( 16 ) confirms the intuitive expectation that the mean size of a synchronous event compared to the distance of the membrane potential to the threshold plays an important role for the output synchrony: if synchrony is sufficiently high , say , the binomial distribution is rather narrow and has a peak at . Inserting this mean value into ( 15 ) and ( 16 ) we obtain the approximationwhich shows that the response probability at time after a spike mainly depends on . Measuring the integral of the output correlation over a window of , in the limit of high input correlation and strong synchrony the picture qualitatively stays the same . Spikes are predominantly produced by the strong depolarizations caused by the synchronously arriving impulses . The output spike trains hence inherit the Poisson statistics from the arrival times of the synchronous volleys . As for marginal Poisson statistics and exactly synchronous output spikes the correlation coefficient does not depend on the time window over which the correlation is measured , the output correlation coefficient is uniquely determined by the ratio of the rates that both neurons fire together over the rate of each neuron firing individually , expressed by ( 11 ) . This theoretical expectation is shown in Fig . 7A and B to agree well with the simulation results for different values of the mean membrane potential . A qualitatively new behavior is observed in the intermediate range of input correlation : the input correlation is transmitted faithfully to the output with a gain factor around unity . Note that in the absence of synchrony the correlation gain is strictly below unity , as shown in Fig . 4 . In the following we consider the point to provide a qualitative argument explaining the unit gain . Fig . 7C shows the average postsynaptic amplitude caused by a volley of synchronously arriving impulses , which is about fluctuating only weakly with a small standard deviation of around . Fig . 7D shows that the mean membrane potential due to the disjoint input alone is around , so two synchronous impulses closely appearing in time are sufficient to fire the neuron . Moreover , the fluctuations caused by the disjoint afferents alone are strong ( around ) and with the mean membrane potential of around they are sufficient to fire the cell . As the integral over the covariance function equals the count covariance over long windows of observation , we consider the spike counts and in a long time window . As each source of fluctuations ( disjoint and common inputs ) alone is already sufficient to fire the cell , both sources mutually linearize the neuron . Averaging the deviation of the spike count from baseline separately over each source of noise ( over common , over disjoint sources ) this deviation can be related linearly to the fluctuation of the respective other source , , . If such a linear relationship holds , it is directly evident that correlations are transmitted faithfully So far , for we have considered the case of input events in the common excitatory input that are perfectly synchronized . In the following we investigate how the transmission of strong synchrony changes if the common excitatory input events are not perfectly synchronous by randomly jittering the spike times in each volley according to a normal distribution with a standard deviation . Fig . 8A shows that increasing the temporal jitter of the spike volleys results in a decrease of the mean output firing rate of neurons , in line with the decrease of the input variance caused by the jittering . Fig . 8B shows that also the output synchrony between the neurons is substantially decreased with increasing jitter . This observation is the result of three consequences of the jitter . Firstly , from the decreased firing rate observed in Fig . 8A we expect the correlation transmission to decrease [35] , [36] . Secondly , due to the measurement of output synchrony on the precise time scale of , every dispersion of the input spikes exceeding this time window lowers the output correlation . Thirdly , for a jitter width comparable to the membrane time constant the leak term of the integrate-and-fire neuron reduces the summed effect of the input spikes on the membrane potential the more , the stronger the dispersion of the spike times . Thus , when considering the output synchrony even with a jitter as small as 1 ms the case of is not reached in the regime of high input correlation . However , on longer correlation time windows ( Fig . 8C , D ) a correlation gain is possible with jitter widths up to 5 ms . This is intuitively expected , because spikes arriving within a short time interval compared to the membrane time constant ( here ) have in sum the same effect as if arriving in synchrony . Thus , measuring the output correlation on the same time scale as the jitter ‘collects’ this cumulative effect .
In this work we investigate the correlation transmission by a neuron pair , using two different types of input spike correlations . One is caused solely by shared input – typically modeled as Gaussian white noise in previous studies [35] , [36] – while in the other the spikes in the shared input may additionally arrive in synchrony . In order to shed light on the question whether cortical neurons operate as integrators or as coincidence detectors [18] , [24] , [25] , we investigate their efficiency in detecting and transmitting spike correlations of either type . We showed that the presence of spike synchrony results in a substantial increase of correlation transmission , suggesting that synchrony is a prerequisite in explaining the experimentally observed excess spike synchrony [17] , [21] , [22] , rather than being an epiphenomenon of firing rate due to common input given by convergent connectivity [8] . To model correlated spiking activity among the excitatory afferents in the input to a pair of neurons we employ the Multiple Interaction Process ( MIP ) [39] , resulting in non-Gaussian fluctuations in the membrane potential of the receiving neurons . In this model the parameter defines the pairwise correlation coefficient between each pair of spike trains . If is large enough and all spike trains are drawn independently ( ) the summation of all spike trains is approximately equivalent to a Gaussian white noise process [41] , [54] . However , introducing spike correlations between the spike trains ( ) additionally allows for the modeling of non-Gaussian fluctuating inputs . Such correlations have a strong effect on the membrane potential statistics and the firing characteristics of the neurons . The fraction of common input and the synchrony strength each contribute to the total correlation between the inputs to both neurons . We show how to isolate and control the effect of input synchrony such that ( 1 ) a particular input correlation can be realized by an ( almost ) arbitrary combination of input synchrony and common input fraction , and ( 2 ) the output firing rate of the neurons does not increase with . This enables a fair comparison of transmission of correlation due to input synchrony and due to common input . We find that the non-linearity of the neuron model boosts the correlation transmission due to the strong fluctuations caused by the common source of synchronous events . Given a fixed input correlation , the correlation transmission increases with . Most notably , this is the case although the output firing rate of the neurons does not increase and is for the most part constant , suggesting that the correlation susceptibility of neurons is not a function of rate alone , as previously suggested [35] , but clearly depends on pairwise synchrony in the input ensemble . Previous studies have shown how to apply Fokker-Planck theory and linear perturbation theory to determine this transmission of correlation by pairs of neurons driven by correlated Gaussian white noise [37] , [44] , [35] , [36] . In order to understand the effect of synchrony on the correlation transmission here we extended the Fokker-Planck approach to synaptic input of finite amplitudes . In the limit of low input correlation this extension indeed provides a good approximation of the output correlation caused by inputs containing spike correlations . Alternative models that provide analytical results are those of thresholded Gaussian models [55] , [56] or random walk models [38] . In order to study transmission in networks with different architecture than the simple feed-forward models employed here , our results may be extended by techniques to study simple network motifs developed in [57] . Hitherto existing studies argue that neurons either loose correlation when they are in the fluctuation driven regime or at most are able to preserve the input correlation in the mean driven regime [58] . Here , we provide evidence for a qualitatively new mechanism which allows neurons to exhibit more output correlation than they receive in their input . Fig . 3A and Fig . 7A show that in the regime of high input correlation the correlation transmission coefficient can exceed unity . This effect , observed at realistic values of pairwise correlations ( ) and common input fractions ( ) , does not depend on the time scale of the measure of output spike correlation and furthermore withstands a jittering of the input synchrony up to the time scale of the membrane time constant . This time scale is on the same order as the experimentally observed dynamically changing precision of synchrony [59] , accessible through theoretical and methodological advances to determine and detect significant spike synchrony [19] , [60] . We provide a quantitative explanation of the mechanism that enables neurons to exhibit this behavior . We show that in this regime of high input correlation the disjoint sources and the common inhibitory sources do not contribute to the firing of the neurons , but rather the neurons only fire due to the strong synchronous events in the common excitatory afferents . Based on this observation , we derive an analytic approximation of the resulting output correlation beyond linear perturbation theory that is in good agreement with simulation results . We presented a quantitative description of the increased correlation transmission by synchronous input events for the leaky integrate-and-fire model . Our analytical results explain earlier observations from a simulation study modeling synchrony by co-activation of a fixed fraction of the excitatory afferents [61] . However , the question remains what the essential features are that cause this effect . An even simpler model consisting of a pair of binary neurons is sufficient to qualitatively reproduce our findings and to demonstrate the generality of the phenomenon for non-linear units , allowing us to obtain a mechanistic understanding . In this model , whenever the summed input exceeds the threshold the corresponding neuron is active ( ) otherwise it is inactive ( ) . In Fig . 9 we consider two different implementations of input correlation , one using solely Gaussian fluctuating common input ( input ) , the other representing afferent synchrony by a binary input common to both neurons ( input ) . The binary stochastic signal has value with probability and otherwise , drawn independently for successive time bins . Background activity is modeled by independent Gaussian white noise in both scenarios . The input corresponds to the simplified model presented in [35 , cf . Fig . 4] that explains the dependence of the correlation transmission of the firing rate . In order to exclude this dependence , throughout Fig . 9 we choose the parameters such that the mean activity of the neurons remains unchanged . As shown in the marginal distribution of the input current to a single neuron in Fig . 9B , in the scenario the binary process causes an additional peak with weight centered around . Equal activity in both scenarios requires a constant probability mass above threshold , which can be achieved by an appropriate choice of . In scenario the input correlation equals the fraction of shared input , as in [35] , whereas in scenario the input correlation is , where is the variance of the binary input signal . Comparing both scenarios , in Fig . 9C–G we choose such that the same input correlation is realized . As for our spiking model , Fig . 9C shows an increased correlation transmission due to input synchrony . This observation can be intuitively understood from the joint probability distribution of the inputs ( Fig . 9D–G ) . Whenever any of the inputs exceeds the threshold ( ) the corresponding neuron becomes active , whenever both inputs exceed threshold at the same time ( ) , both neurons are synchronously active . Therefore , , the probability mass on the right side of for input ( corresponding definition for ) , is a measure for the activity of the neurons . Analogously , , the probability mass in the upper right quadrant above both thresholds is a measure for the output correlation between both neurons . Since by our model definition the mean activity of both neurons is kept constant , the masses and are equal in all four cases . However , the decisive difference between scenarios with inputs and is the proportion of on the total mass above threshold . This proportion is increased by the common synchronous events , observable by comparing Fig . 9D , E . The more this proportion approaches , , the more the activity of both neurons is driven by ( Fig . 9F ) . At the same time the contribution of the disjoint fluctuations on the output activity is more and more suppressed . As the correlation coefficient relates the common to the total fluctuations , the correlation between the outputs can exceed the input correlation if the transmission of the common input becomes more reliable than the transmission of the disjoint input ( cf . point marked as F in Fig . 9C ) . The situation illustrated in Fig . 9 is a caricature of signal transmission by a pair of neurons of a cell assembly . The signal of interest among the members of the assembly consists of synchronously arriving synaptic events from peer neurons of the same assembly . In our toy model such a volley is represented by an impulse of large amplitude . The remaining inputs are functionally considered as noise and cause the dispersion of and observable in Fig . 9D–F . In the regime of sufficiently high synchrony ( corresponding to large ) in Fig . 9F , the noise alone rarely causes the neurons to be activated , it is suppressed in the output signal due to the threshold . The synchrony coded signal , however , reliably activates both neurons , moving and into the upper right quadrant . Thus a synchronous volley is always mapped to in the output , irrespective of the fluctuations caused by the noise . In short , the non-linearity of neurons suppresses the noise in the input while reliably detecting and transmitting the signal . A similar effect of noise cancellation has recently been described to prolong the memory life-time in chain-like feed forward structures [62] . Several aspects of this study need to be taken into account when relating the results to other studies and to biological systems . The multiple interaction process as a model for correlated neural activity might seem unrealistic at first sight . However , a similar correlation structure can easily be obtained from the activity of a population of neurons . Imagine each of the neurons to receive a set of uncorrelated afferents causing a certain mean membrane potential and variance . The entire population is then described by a membrane potential distribution . In addition , each neuron receives a synaptic input of amplitude that is common to all neurons . Whenever this input carries a synaptic impulse , each of the neurons in the population has a certain probability to emit a spike in direct response . The probability equals the amount of density shifted across threshold by the common synaptic event . Given the value and its slope of the membrane potential density at threshold , the response probability is to second order in the synaptic weight . Employing the diffusion approximation to the leaky integrate-and-fire neuron , the density vanishes at threshold and the slope is given by [34] . The response probability hence is . For typical values of , , and the estimate yields to get the copy probability used in the current study . Such a synaptic amplitude is well in the reported range for cortical connections [28] . As each of the neurons within the population responds independently , the resulting distribution of the elicited response spikes is binomial , as assumed by the MIP . Moreover , since our theory builds on top of the moments of the complexity distribution it can be extended to other processes introducing higher order spike correlations [61] , [39] . The correlation transmission coefficient can only exceed unity if the firing of the neurons is predominantly driven by the synchronously arriving volleys and disjoint input does not contribute to firing . The threshold then acts as a noise gate , small fluctuations caused by disjoint input do not penetrate to the output side . In the mean driven regime , i . e . when , this situation is not given since every fluctuation in the input either advances ( excitatory input ) or delays ( inhibitory input ) the next point of firing . Especially at high firing rates the ‘forgetting’ of the fluctuation due to the leak until the next firing can be neglected , the leaky integrate-and-fire neuron behaves like a perfect integrator . Perfect integrators transmit fluctuations linearly , so [58] . Given strong input synchrony ( and , simulation results show that in the regime up to input correlations the neurons exhibit such a linear transmission ( data not shown ) . For the correlation transmission decreases as the firing rate substantially decreases in the regime of high . This smaller firing rate moves the dynamics away from the perfect integrator as the neurons loose more memory about the commonly received pulses between two spikes . The boost of output correlation by synchronous synaptic impulses relies on fast positive transients of the membrane potential and strong departures from the stationary state: An incoming packet of synaptic impulses brings the membrane potential over the threshold within short time . Qualitatively , we therefore expect similar results for short , but non-zero rise times of the synaptic currents . For long synaptic time constants compared to the neuronal dynamics , however , the instantaneous firing intensity follows the modulation of the synaptic current adiabatically [44] , [63] . A similar increase of output synchrony in this case can only be achieved if the static curve of the neuron has a significant convex non-linearity . The choice of the correlation measure is of importance when analyzing spike correlations . It has been pointed out recently that the time scale on which spike correlations are measured is among the factors that can systematically bias correlation estimates [3] . In particular , spike count correlations computed for time bins larger than the intrinsic time scale of spike synchrony can be an ambiguous estimate of input cross correlations [64] . Considering the exactly synchronous arrival of input events generated by the MIP , we chose to measure count correlations on a small time scale of as well as on a larger scale of . It has been proposed that the coordinated firing of cell assemblies provides a means for the binding of coherent stimulus features [14] , [15] , [16] . Member neurons of such functional assemblies are interpreted to encode the relevant information by synchronizing their spiking activity . Under this assumption the spike synchrony produced by the assembly can be considered as the signal and the remaining stochastic activity as background noise . In order for a downstream neuron to reliably convey and process the incoming signal received from the assembly , it is essential to detect the synchronous input events carrying the signal and to discern them from corrupting noise . Moreover , the processing of such a synchrony-based code must occur independently of the firing rate of the assembly members . We have shown that indeed the presence of afferent spike synchrony leads to increased correlation susceptibility compared to the transmission of shared input correlations . The finding of a correlation susceptibility that is not a function of the firing rate alone [35] demonstrates a limitation of the existing Gaussian white noise theory that fails to explain the qualitatively different correlation transmission due to synchrony . Though in the limit of weak input correlation the correlation in the output is bounded by that in the input , in agreement with previous reports [37] , [35] , [58] , our results show that for high input correlation caused by synchrony , neurons are able to correlate their outputs stronger than their inputs . This finding extends the prevailing view of correlation propagation as a ‘transmission’ , as this notion implies that a certain quantity is transported , and hence can at most be preserved . We have shown in a mechanistic model how this correlation gain results from the non-linearity of cortical neurons enabling them to actively suppress the noise in their input , thus sharpening the signal and improving the signal-to-noise ratio . In convergent-divergent feed forward networks ( synfire chains ) , this mechanism reshapes the incoming spike volley [65] , so that synchronized activity travels through the feed forward structure in a stable manner or builds up iteratively from a less correlated state , if the initial correlations exceed a critical value [66] , [67] . From our findings we conclude that the boosting of correlation transmission renders input synchrony highly effective compared to shared input in causing closely time-locked output spikes in a task dependent and time modulated manner , as observed in vivo [22] .
We here derive an approximation for the integral of the impulse response of the firing rate with respect to a perturbing impulse in the input . A similar derivation has been presented in [12 , App . 4 . 3] . Consider a neuron receiving background spiking input with a first and second moment and , respectively , and an additional incoming impulse of amplitude at time . The arrival of the impulse causes an instantaneous shift of the membrane potential by . Therefore the probability density at voltage is increased in proportion to the density at before the jump , whereas the density is decreased by the states that were at . This amounts to an additional term in the Fokker-Planck equation ( 3 ) , which readsApplying a Kramers-Moyal expansion [41] ( a Taylor expansion in up to second order ) to the additional term , we getCombining the terms proportional to the first and second order derivative with the corresponding terms appearing in eq:P ( V , t ) leads toSo the additional impulse can be considered as a -shaped perturbation of the first and second infinitesimal moment . We therefore introduce a formal dependence of and on a time dependent function asIf we are interested in the effect of an impulse of small amplitude , we may linearly approximate the response of the neuron to the impulse . It generally holds that to linear approximation in the integral of the response to an impulse equals the response to a unit-step in the parameter , because . In the limit of the step response equals the DC-susceptibility , which can be expressed as the derivative with respect to the perturbed quantity . Therefore we obtain to linear approximation ( 17 ) Using the well known expression for the mean first passage time [68] , [40] for a neuron with stationary input ( 18 ) ( 17 ) can be evaluated as ( 19 ) where we applied the chain rule to express and as well as , so finally for . The first four moments of the binomial distribution are [69] | Whether spike timing conveys information in cortical networks or whether the firing rate alone is sufficient is a matter of controversial debate , touching the fundamental question of how the brain processes , stores , and conveys information . If the firing rate alone is the decisive signal used in the brain , correlations between action potentials are just an epiphenomenon of cortical connectivity , where pairs of neurons share a considerable fraction of common afferents . Due to membrane leakage , small synaptic amplitudes and the non-linear threshold , nerve cells exhibit lossy transmission of correlation originating from shared synaptic inputs . However , the membrane potential of cortical neurons often displays non-Gaussian fluctuations , caused by synchronized synaptic inputs . Moreover , synchronously active neurons have been found to reflect behavior in primates . In this work we therefore contrast the transmission of correlation due to shared afferents and due to synchronously arriving synaptic impulses for leaky neuron models . We not only find that neurons are highly sensitive to synchronous afferents , but that they can suppress noise on signals transmitted by synchrony , a computational advantage over rate signals . | [
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] | 2013 | Noise Suppression and Surplus Synchrony by Coincidence Detection |
Chagas disease ( CD ) is endemic in Central and South America , Mexico and even in some areas of the United States . However , cases have been increasingly recorded also in non-endemic countries . The estimated number of infected people in Europe is in a wide range of 14000 to 181000 subjects , mostly resident in Spain , Italy and the United Kingdom . Retrospective , observational study describing the characteristics of patients with CD who attended the Centre for Tropical Diseases ( Negrar , Verona , Italy ) between 2005 and 2013 . All the patients affected by CD underwent chest X-ray , ECG , echocardiography , barium X-ray of the oesophagus and colonic enema . They were classified in the indeterminate , cardiac , digestive or mixed category according to the results of the screening tests . Treatment with benznidazole ( or nifurtimox in case of intolerance to the first line therapy ) was offered to all patients , excluding the ones with advanced cardiomiopathy , pregnant and lactating women . Patients included were 332 ( 73 . 9% women ) . We classified 68 . 1% of patients as having Indeterminate Chagas , 11 . 1% Cardiac Chagas , 18 . 7% as Digestive Chagas and 2 . 1% as Mixed Form . Three hundred and twenty-one patients ( 96 . 7% ) were treated with benznidazole , and most of them ( 83 . 2% ) completed the treatment . At least one adverse effect was reported by 27 . 7% of patients , but they were mostly mild . Only a couple of patients received nifurtimox as second line treatment . Our case series represents the largest cohort of T . cruzi infected patients diagnosed and treated in Italy . An improvement of the access to diagnosis and cure is still needed , considering that about 9200 infected people are estimated to live in Italy . In general , there is an urgent need of common guidelines to better classify and manage patients with CD in non-endemic countries .
Chagas disease ( CD ) is a protozoan zoonosis caused by Trypanosoma cruzi ( T . cruzi ) , with a widespread distribution from the South of the United States to Mexico , Central and South America [1] . Current data indicate that between 7 and 8 million people are infected in this area [2] . Other authors suppose that in North America ( Canada , USA , Mexico ) there might be many more infected subjects than it was previously estimated ( 1 to 6 million people ) [3] , therefore global estimations should be revised . In endemic countries , T . cruzi infection is usually transmitted through contact with faeces of blood-sucking triatomines , rarely after oral ingestion of food contaminated by triatomines faeces . Other non-vectorial routes of transmission are transplacentary , blood transfusion and organ/tissue transplantation [1] . The acute stage of the infection is frequently asymptomatic ( 95% of cases ) ; when symptomatic , acute CD can cause myocarditis and encephalomyelitis with reported 5–10% mortality [1] . If not treated , the acute phase is followed by a chronic stage , corresponding to an indeterminate form , lasting long-life in around 60–70% of patients . During this phase patients are clinically silent: however , after 10 to 30 years , around 30–40% of infected people will develop symptomatic chronic CD , which is mainly characterized by cardiac and gastrointestinal disorders [1] . As a consequence of migration flows , the disease has been increasingly recorded also in non-endemic countries and is becoming a global health problem [4] . The estimated number of infected people in Europe is in a wide range of 14 , 000 to 181 , 000 subjects [5] . Italy has a large number of residents of Latin American origin , second in Europe only to Spain [6] . Strasen et al estimate that in Italy there are about 9 , 200 cases ( range 1 , 400–17 , 000 ) [5] . The majority of Latin American migrants reached Italy in the past ten years , with a growing trend . Migrants from different countries tend to have a patchy distribution in Italian Regions , with a major concentration in Northern Italy and in Rome . For instance most Bolivians live in Bergamo Province , Lombardy , Ecuadorians in Liguria Region , and Peruvians in big cities such as Milan , Florence and Rome [6] . The Centre for Tropical Diseases ( CTD ) , Hospital Sacro Cuore ( Negrar , Verona ) contributed to describe this new epidemiological scenario and became a reference center in the management of CD . Aim of this paper is to describe the characteristics of patients with CD attended at our center .
This is a retrospective , observational study , including all patients with CD attended at CTD between 2005 and 2013 . Most of these patients were identified through a specific screening programme offered by our center to Latin American communities resident in Northern Italy; moreover , other patients were referred from other centers to the CTD on the basis of clinical suspicion . Up to 2014 , there were no other screening programmes in Italy focused on CD . Information collected included clinical and epidemiological characteristics such as: age , gender , country of origin , history of living in rural environments and mud houses , time since arrival to our country , history of blood transfusion in endemic countries , co-morbidities causing immunosuppression , cardiac and gastrointestinal assessment , completion of treatment . Data were anonymized and entered in an Epi-Info database . Chagas Disease diagnosis was based on two concordant positive serological tests , using different antigens , a recombinant antigen-based ELISA ( BioELISA Chagas , Biokit , Lliça d′Almunt , Spain ) plus either a T . cruzi lysate antigen-based ELISA ( BioELISA Chagas III , BiosChile , Santiago , Chile ) or an immunochromatographic assay ( Chagas Quick Test , Cypress Diagnostics , Belgium ) . As recommended by the World Health Organization [7] , in case of discordant result , a third assay ( an immunoblot , TESA-cruzi ) [8] was performed . Patients with CD were examined following an internal protocol that included haematology and biochemistry . Cardiac involvement was assessed with chest X-ray , electrocardiogram ( ECG ) , echocardiography [9] . Chest X-ray was considered pathological in case of cardiac enlargement , defined as cardiothoracic ratio greater than 0 , 5 . The ECG was evaluated by a cardiologist and considered pathological in case of bradycardia <50 bpm , atrio-ventricular block , bundle branch block or hemiblock , low voltages , tachyarrhythmia of any origin , Q-waves suggestive of necrosis and T-waves changes suggesting ischemia . The echocardiography was considered pathological in case of systolic dysfunction , defined as a left ventricular ejection fraction ( LVEF ) below 45% , segmental abnormalities of myocardial contraction , presence of aneurismas ( typically apical ) , dilated cardiomyopathy [9] . The modified Brazilian classification was followed for cardiac CD classification [10] . Digestive involvement was assessed with barium X-ray of the oesophagus ( oesophagogram ) [11] and colonic enema [12] . Oesophagogram was considered pathological following Rezende's classification [11] . Barium enema was defined pathological in case of megacolon ( descending colon diameter>6 , 5 cm or ascending colon diameter>8 cm , or caecum diameter>12 cm ) [13] or dolichocolon ( length from the anus to the transition to the descending colon>70 cm ) [13] . T . cruzi infected patients without cardiac or digestive involvement were classified as Chagas indeterminate form; patients with cardiac or digestive involvement were classified respectively as Chagas cardiac form or Chagas digestive form , while patients with both cardiac and digestive form were classified as Chagas mixed form . Specific treatment with benznidazole at 5 mg/kg/day divided into three daily doses for 60 days was offered to all infected patients , after careful explanation of benefits and risks of the therapy . Although current guidelines [14] recommend to offer the treatment to patients under 50 years of age , we extended the age limit up to 60 year-old patients , in the absence of severe comorbidities . Patients with advanced cardiomyopathy , pregnant ( pregnancy test was performed in all women of child-bearing age before treatment ) and lactating women were excluded . In patients weighing more than 60 kg , a fixed daily dose of 300 mg was given for a total number of days equal to the patient's weight in kilograms , resulting in a total dose that is equivalent to 5 mg/kg per day for sixty days [15] . In case of important early side effects due to benznidazole , nifurtimox ( 10 mg/kg/day for 60 days ) was prescribed . Patients were considered adequately treated when the drug was taken for at least one month [16] . In case of mild adverse effects , continuation of treatment was encouraged , while benznidazole was stopped in case of severe adverse effects: for instance , severe allergic skin reactions , peripheral neuropathy , leucopoenia ( WBC <2500 cells/µL ) . Patients underwent a blood test ( cell blood count and general biochemistry ) at the beginning of the treatment and after 21 days . At day 30 they came for follow up visit to our outpatient clinic: if no significant laboratory or clinical adverse events were recorded , the remaining tablets were given to the patient for the completion of treatment . During the second month of treatment , follow-up was done through phone call or visit according to physician's judgement . We assessed the adherence to the treatment on the basis of what the patients reported during the phone calls/visits . Pregnant and lactating women found positive at the screening test for CD were followed up in a different way , because they could not undergo all the diagnostic examinations needed to evaluate a possible cardiac/intestinal involvement ( barium enema and chest X ray for instance ) and could not take the treatment . Therefore , their data were included in this analysis only once they could complete the staging and receive the therapy ( that is after they stopped to breastfeed ) . Data were analysed with Epi-Info program , version 7 . 1 . 0 . 6 , Centers for Disease Control , August , 2012 . Chi-squared test was used for dichotomous variables and t-test for continuous variables , as appropriate . Written informed consent was required from the patients at the moment of the execution of the screening test . The study protocol was approved by the Ethics Committee ( Comitato Etico della Provincia di Verona ) on February 19th 2013 .
During the 9–year period , 332 patients were included in this study . Characteristics of the patients are described in table 1 . The majority of the patients were women ( 73 . 9% ) and the mean age was 41 . 8 years ( range 11–71 ) . Sixty-one per cent of the patients reported living in rural areas and 73 . 2% in mud houses , while 6 . 9% of them reported history of blood transfusion in endemic countries . Ninety-seven percent of the patients came from rural high-prevalence Bolivian environments , especially from Santa Cruz and Cochabamba Departments . Four patients were under immunosuppressant therapies: two for rheumatoid arthritis , one for psoriasis , one for systemic lupus erithematosus ( SLE ) . One patient had acquired immunodeficiency syndrome ( AIDS ) . According to the findings of instrumental exams , 226 ( 68 . 1% ) subjects were classified as Indeterminate Chagas , 37 ( 11 . 1% ) as Cardiac Chagas , 62 ( 18 . 7% ) as Digestive Chagas , 7 ( 2 . 1% ) as Mixed Form ( Fig . 1 ) . Cardiac involvement is reported in table 2 . In 288 patients ( 86 . 8% ) ECG and echocardiogram resulted normal . Among patients with abnormal findings , the majority were classified in stage A ( 9% ) or B1 ( 2% ) ( table 3 shows the ECG alterations found ) . The patient with AIDS showed abnormal ECG findings , abnormal echocardiogram findings with refractory CHF and needed cardiac transplantation . Four patients already had pacemaker at the time of diagnosis , while two needed pacemaker implantation at the time of the diagnosis or during the follow –up . Gastrointestinal involvement is reported in table 4 . At barium enema , dolichocolon was found in 33 patients ( 9 . 9% ) and megacolon in 30 patients ( 9 . 0% ) . Oesophagogram resulted slightly pathological ( group I ) in 3 patients ( 0 . 9% ) . No patient was classified in group II and III . One patient was classified in group IV , because he underwent surgery for dolicho-megaoesophagus in Bolivia . One patient underwent multiple operations because of colonic volvulus . Two other patients presented mega-colon without toxic signs , but refused surgical operation and are followed with symptomatic treatment . Three hundred twenty-one patients ( 96 . 7% ) were treated with benznidazole . Eleven patients ( 3 . 3% ) were not treated because of refusal of treatment , or previous treatment in the country of origin . Patients who presented at least one adverse effect were 89/321 ( 27 . 7% ) . In particular , 70 patients ( 21 . 8% ) reported mild adverse effects , such as mild allergic skin reaction ( 54/70 patients ) , gastrointestinal adverse events ( 5/70 ) , neurological or general adverse effects ( headache , somnolence , weakness and arthromyalgia ) ( 8/70 ) , transaminase elevation ( 3/70 ) ; nineteen patients ( 5 . 9% ) reported severe adverse effects , such as severe allergic skin reactions ( 7/19 ) , peripheral neuropathy ( 6/19 ) , leucopoenia ( WBC <2500 cells/µL ) ( 6/19 ) . Patients who completed the treatment were 267/321 ( 83 . 2% ) . Among the 54 patients who had to stop the treatment , 19 had reported severe adverse effects , 26 mild allergic skin reactions , 3 gastrointestinal adverse effects , 5 neurological adverse effects , one had transaminase elevation . Two patients received nifurtimox as a second choice: one patient completed the therapy , the other one had to stop because of toxicity .
CD is an example of a new challenge of global health and should be no longer perceived as an exotic disease [30] , but one that is currently present in Italy . Public health policies ( with the support of anthropologists ) to reach migrants from Latin America are urgently needed in order to plan appropriate screening strategies to facilitate contacts with the communities of immigrants [30] , test risk groups and offer the treatment to the patients with the infection . Official European guidelines for CD management are still lacking ( the Spanish ones are commonly followed [9] , [13] ) . Moreover , different classifications ( for cardiac and digestive involvement ) are being used internationally . Efforts are necessary in order to uniform management strategies and classification criteria . Moreover , up to now , we do not have any evidence on the disease evolution in non endemic countries where vectorial re-infection is not possible and where there are different life styles and comorbidities . | Chagas disease is endemic in Central and South America , Mexico and part of the United States . However , migration flows permitted a diffusion outside those borders , so nowadays there are infected people living in Europe , mostly in Spain , Italy and the United Kingdom . This manuscript describes the patients with Chagas disease attended at a referral center in Italy between 2005 and 2013 . They were classified into different categories according to the presence of possible cardiac and/or gastrointestinal involvement: one third of patients presented signs of organ damage . Ninety-seven percent of the infected patients were treated with benznidazole , 16% had to stop the treatment due to adverse events . | [
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] | 2014 | Profile of Trypanosoma cruzi Infection in a Tropical Medicine Reference Center, Northern Italy |
Human adenovirus serotypes Ad3 , Ad7 , Ad11 , and Ad14 use the epithelial junction protein desmoglein 2 ( DSG2 ) as a receptor for infection . During Ad infection , the fiber and penton base capsid proteins are produced in vast excess and form hetero-oligomers , called pentons . It has been shown for Ad3 that pentons self-assemble into penton-dodecahedra ( PtDd ) . Our previous studies with recombinant purified Ad3 PtDd ( produced in insect cells ) showed that PtDd bind to DSG2 and trigger intracellular signaling resulting in the transient opening of junctions between epithelial cells . So far , a definitive proof for a function of Ad3 PtDd in the viral life cycle is elusive . Based on the recently published 3D structure of recombinant Ad3 PtDd , we generated a penton base mutant Ad3 vector ( mu-Ad3GFP ) . mu-Ad3GFP is identical to its wild-type counterpart ( wt-Ad3GFP ) in the efficiency of progeny virus production; however , it is disabled in the production of PtDd . For infection studies we used polarized epithelial cancer cells or cell spheroids . We showed that in wt-Ad3GFP infected cultures , PtDd were released from cells before viral cytolysis and triggered the restructuring of epithelial junctions . This in turn facilitated lateral viral spread of de novo produced virions . These events were nearly absent in mu-Ad3GFP infected cultures . Our in vitro findings were consolidated in mice carrying xenograft tumors derived from human epithelial cancer cells . Furthermore , we provide first evidence that PtDd are also formed by another DSG2-interacting Ad serotype , the newly emerged , highly pathogenic Ad14 strain ( Ad14p1 ) . The central finding of this study is that a subgroup of Ads has evolved to generate PtDd as a strategy to achieve penetration into and dissemination in epithelial tissues . Our findings are relevant for basic and applied virology , specifically for cancer virotherapy .
The main structural proteins of the icosahedral capsids of adenoviruses ( Ads ) are the hexon and penton base . The penton base forms pentamers located at the 12 vertices of the Ad particle . Each pentamer anchors one copy of a trimeric fiber protein . The C-terminal part of the fibers , the fiber knob , mediates the high affinity binding to a cellular receptor , while the RGD containing loops within the penton base interact with cellular integrins , a step that mediates cell entry of virions , except species B Ads . Most human Ad serotypes use CAR as a primary attachment receptor . Species B Ad serotypes use either CD46 or DSG2 . Among DSG2-targeting viruses is serotype Ad3 . Recently , we have shown that complete inhibition of Ad3 binding and infection requires the physical linkage and , most likely , a specific spatial constellation of at least two fiber knobs [1] . This specific mode of Ad3-fiber knob-DSG2 interaction is functionally relevant for opening of junctions between epithelial cells [1] , [2] . Binding of Ad3 to DSG2 triggers the autocatalytic cleavage of DSG2 and activation of pathways that are reminiscent of an epithelial-to-mesenchymal transition ( EMT ) , including the phosphorylation of MAP kinases and the downregulation of junction proteins [2] , [3] , [4] . The ability to open epithelial junctions appears to be important for Ad3 penetration into and spread within epithelial tissues [1] , [2] , [3] . During Ad infection , the penton base and fiber proteins are produced in excess and assemble in the cytosol to form fiber-penton base hetero-oligomers called pentons [5] , [6] . In the case of Ad3 , twelve pentons self-assemble into dodecamers with a diameter of ∼30 nm [7] . Penton-dodecahedra ( PtDd ) also form in insect cells during overexpression of Ad3 penton base and fiber [8] . Western blot analysis did not indicate differences in post-translational modification of PtDd produced from baculovirus vectors in insect cells and PtDd produced from Ad3 in infected HeLa cells ( Figure S1 ) . The crystal structure of recombinant penton base dodecahedra has recently been delineated at 3 . 8 Å resolution , which allowed for the elucidation of the mechanisms of Ad3 PtDd formation [9] . PtDd self-assembly is initiated through relative weak salt bridges involving residues D100 and R425 . Subsequently , an N-terminal strand exchange occurs between neighboring pentons that leads to a stable PtDd particle . Notably , strand-swapping can occur only in the context of PtDd and not in the context of the viral capsid where individual penton pentamers are separated by hexons . During Ad3 replication , PtDd are formed at an excess of 5 . 5×106 PtDd per infectious virus [7] . The massive production of PtDd strongly suggests that they have a role in virus infection . Notably , the main natural target for Ad3 infection is the airway epithelium . Characteristic features of airway epithelial cells are an apical-basal polarization of their cell membranes and cytoskeleton as well as tight and adherens junctions that seal the paracellular space between adjacent cells and thereby provide a barrier to pathogens . Several lines of research indicate that PtDd facilitate the lateral spread of de novo produced Ad3 virions in epithelial cells . i ) During Ad3 infection PtDd are released from infected cells , prior to the release of progeny virus , and bind to neighboring cells via DSG2 [10] . ii ) Incubation of epithelial cells with recombinant PtDd produced in insect cells results in clustering of DSG2 , which in turn triggers signaling pathways that are reminicent of EMT and leads to transient opening of tight junctions between epithelial cells [2] . iii ) Efficient binding to DSG2 and triggering of junction opening requires multiple Ad3 fiber knobs in a specific spatial constellation , which is present in PtDd , implying that dodecamerization is functionally important [1] . While these studies performed with recombinant PtDd are indicative , they do not prove a role of PtDd produced de novo during Ad3 infection of epithelial cells . To provide such proof we generated an Ad3 virus that was greatly disabled in the formation of PtDd and tested its effect on viral spread in epithelial cells in vitro and in vivo in comparison to a wild-type Ad3 virus .
A previous study using recombinant penton base dodecahedra showed that the deletion of the first 60 amino acids or the substitution of amino acid residues 58-SELS-61 to 58-SDVA-61 prevented dodecamerization . Furthermore , penton base mutants 100-D→R or 425-R→E drastically reduced the yield and stability of penton base dodecamers [9] . We therefore attempted to generate Ad3 penton base mutants that contained the SELS→SDVA mutation , the 100-D→R mutation , and the 425-R→E mutation individually or in combination ( Figures 1A–C ) . The mutations were introduced into a vectorized Ad3 genome , where the parental Ad3 vector ( wt-Ad3GFP ) contained a CMV promoter-driven GFP gene inserted into the E3 region [2] . Mutations were introduced into the penton base sequence of wt-Ad3GFP . The correctness of the penton base gene modifications was confirmed by PCR and DNA sequencing ( Figure S2 ) . The recombinant viral genomes were transfected into 293 cells for virus rescue . Single plaques were then amplified and viral genomes sequenced . We could only rescue viruses that contained the D100R and R425E mutations individually or in combination . In our further studies , we used the Ad3 mutant that contained both the D100R and R425E mutations ( mu-Ad3GFP ) ( Figure 1D ) . Progeny virus yields during the virus amplification were comparable between wt-Ad3GFP and mu-Ad3GFP ( Figure S3 ) . To ensure that the introduced mutations did not affect viral entry , DNA replication , assembly , and release of progeny virus , we performed virus growth curve assays in 293 cells ( Figure 2A ) and T84 cells that were cultured at relative low cell density to avoid the formation of intercellular junctions ( Figure 2B ) . These studies showed that the kinetics and yields of progeny virus production did not significantly differ for wt-Ad3GFP and mu-Ad3GFP . This is further supported by the analysis of protein levels of Ad3 penton base , fiber , and hexon in infected cells by Western blot , which did not show differences between wt-Ad3GFP and mu-Ad3GFP at 36 hours after infection ( Figure 2C ) . To optimize conditions to measure PtDd production , HeLa cells were infected with wt-Ad3GFP at MOIs ranging from 500 to 2000 vp/cell . At 24 , 36 , and 48 hours post-infection , cell lysates were subjected to ultracentrifugation in a 15–40% sucrose step gradient . Fractions with different densities were then analyzed by Western blot using polyclonal antibodies raised against purified PtDd that reacted with both Ad3 penton base and fiber ( Figures S4 and S5 ) . Trace amounts of free viral penton base that was not incorporated into PtDd was found in all fractions , while PtDd concentrated in the range of 27–32% sucrose , and complete viral particles were found at the bottom of the tube in fractions 38 and 40% sucrose . The relative amount of hexon-containing defective viral particles in high density sucrose fractions was higher at 48 hours than at 24 hours and also increased with increasing MOIs . For further analysis of PtDd , we infected cells at 500 vp/cell and collected cells at 36 hours to minimize the contamination of PtDd with defective viral particles ( Figure 3 ) . A direct comparison of lysates from mu-Ad3GFP and wt-Ad3GFP infected cells showed markedly weaker penton base and fiber signals in 27–32% sucrose fractions for mu-Ad3GFP . Using purified viral particles and recombinant PtDd as references , we determined that the normalized PtDd signals for penton base and fibers were tenfold less intense in cell lysated from mu-Ad3GFP infected cells compared to wt-Ad3GFP infected cells ( Figure S4C ) . We therefore concluded that PtDd production is severly inhibited for mu-Ad3GFP . Since the natural target of Ad3 is polarized epithelial tissue , we then studied the spread of wt- and mu-Ad3GFP in epithelial cells . To model this , we cultured epithelial cancer cell lines under conditions that would allow for cell polarization . Colon cancer T84 cells were cultured in transwell chambers for 2–3 weeks until the trans-epithelial electrical resistance ( TEER ) between the inner and outer chambers was constant , i . e . mature intercellular junctions had formed . At this time , cells were exposed from the apical side with material contained in 30% sucrose fractions from wt-Ad3GFP infected cells . This lead to a decrease of the TEER within 1 . 5 hours after addition which indicates junction opening ( Figure S6A ) . In contrast , the corresponding fraction from mu-Ad3GFP-infected cells had no significant effect on the TEER . The decrease in TEER was absent when the 30% sucrose fraction material was mixed with recombinant soluble DSG2 . This suggests that the material in the fraction bound to DSG2 and therefore most likely represented PtDd ( Figure S6B ) . Notably , our previous studies showed that recombinant Ad3 fiber knob ( without a dimerization domain ) did not significanlty affect the TEER , making it unlikely that TEER increase by the 30% sucrose fraction is caused by soluble Ad3 fiber . Confocal immunofluorescence microscopy analysis of T84 cells for the epithelial junction marker E-cadherin and the desmosomal protein DSG2 shows the “chicken-wire” staining of cell membranes that is typical for epithelial cells ( Figure 4A , left panel ) , with the paracellular space sealed by junction proteins ( Figure 4A , right panel ) . Most of the DSG2 molecules are trapped in lateral junctions and only rare T84 cells display DSG2 on the apical cell surface . This implies that only few T84 cells can be infected if Ad3 vectors are applied to the apical surface , Ad infection can be visualized based on GFP expression . GFP-positive cells co-stained with Ad3 PtDd ( i . e . penton/fiber ) -specific antibodies ( Figure 4B , red signals ) resulting in yellow signals . The yellow staining pattern suggests the de novo production of Ad3 virions . For wt-Ad3GFP-infected cells , PtDd-specific ( red ) signals can be seen in the junctions of surrounding cells ( Figure 4B , left panel ) . This indicates release of PtDd before virus-mediated cytolysis . The latter is also supported by studies on wt-Ad3 infected HeLa cells , which do not form epithelial junctions ( Figure S7 ) . PtDd-specific signals were detectable in infected ( GFP-postive ) cells as well as in neighboring non-infected cells , indicating uptake of released PtDd by neighboring cells . Importantly , PtDd signals are visibly less pronounced outside cells in mu-Ad3GFP infected T84 cell cultures ( Figure 4B , right panel ) . Furthermore , while junctions ( visualized with anti-E-cadherin antibodies ) are mostly absent in and around wt-AdGFP-infected ( GFP-positive ) cells , these junctions are preserved between mu-Ad3GFP infected cells ( Figure 4C ) . Image morphometry for E-cadherin signals in the center of transwell cultures showed that the area of E-cadherin was 3 . 4 ( +/−2 . 3 ) % and 17 . 3+/−5 . 8% ( based on mm2 E-cadherin per mm2 ) for wt-Ad3GFP- and mu-Ad3GFP injected mice , respectively . Taken together , our data imply that PtDd production and subsequent junction opening is impaired in mu-Ad3GFP infected cells . Transwell cultures of polarized T84 cells form layers with an average thickness of five cells . Addition of wt-Ad3GFP and mu-Ad3GFP vectors resulted in the infection of the top cell layer with comparable efficiency . Confocal microscopy performed 5 days after infection revealed transduction of cells in deeper cell layers for wt-AdGFP but not for mu-Ad3GFP ( Figure 5A ) . This suggests that wt-Ad3GFP produced de novo in primarily infected cells was able to penetrate deeper into the cell layer . Over time , progeny virus should completely spread through the multi-cell layer and be detectable at the basal side , i . e . the outer chamber of the transwell cultures . We therefore measured the titer of infectious units in the inner and outer chamber at different time points . Virus became detectable in the outer chamber only at day 9 after infection ( Figure 5B ) . In the outer chamber , the titers of mu-Ad3GFP were significantly lower than those of wt-Ad3GFP . In contrast , the titers of both viruses in the inner chamber were comparable , indicating that release of de novo produced virus from initially infected cells in the top cell layer is not impaired for mu-Ad3GFP . The process of transepithelial spread was similar at MOI 2 and MOI 50 , indicating that the cells with accessible DSG2 on the apical side were already saturated with virus at MOI 2 . To further prove a role of PtDd in viral spread we added recombinant PtDd to the inner chamber of T84 cells at days 3 , 5 , and 7 after infection ( Figure 5C ) . As in the previous experiment , less mu-Ad3GFP progeny virus was found in the outer chamber at day 8 and 10 after infection indicating that mu-Ad3GFP is impaired in viral spread . Importantly , while “exogenous” recombinant PtDd had no significant effect on the spread of wt-Ad3GFP through the layer of T84 cells , spread of mu-Ad3GFP was significantly increased . This indicates that recombinant PtDd , to some degree , compensates for the reduced PtDd production from mu-Ad3GFP . As a second model for studying viral spread , we used tumor cell spheroids that form when T84 cells are cultured in a spinner flask , which prevents their attachment to the glass surface . Culturing the cells under these conditions resulted in spheroids with ∼100 cells linked together through junctions as indicated by E-cadherin staining ( Figure S8 ) . wt- and mu-Ad3GFP were added to the culture for 2 hours and spheroids were analyzed by confocal microspcopy 4 days later . Transduction of peripheral cells was comparable for both viruses , while transduction of deeper cell layers was impaired for mu-Ad3GFP ( Figure 6A , Figure S9 ) . More efficient spread to , and the subsequent replication in neighboring cells should result in higher yields of do novo produced viruses in polarized T84 spheroids . We therefore measured the viral titer in the culture medium from infected T84 cell spheroids . For both viruses , titers were comparable at day 3 post-infection . Analysis at a later time point , when replication was detectable in cells based on hexon expression ( data not shown ) , showed significantly more progeny virus was found in the culture medium of wt-Ad3GFP infected spheroids compared to mu-Ad3GFP ( Figure 6B ) . More efficient dissemination of wt-Ad3GFP was also demonstrated in a second cell culture model . Human lung cancer A549 cells polarize in culture to a certain extent but do not form mature tight junctions . Viral growth kinetics in A549 cells was comparable for wt-Ad3GFP and mu-Ad3-GFP ( Figure S10A ) . There was visibly less release of PtDd from mu-Ad3GFP infected cells ( Figure S10B ) . wt- and mu-Ad3GFP infection of confluent A549 cells at an MOI of 0 . 01 vp/cell resulted in the formation of GFP expressing foci that increased over time due to viral spread . The number of foci with a diameter larger than 2 mm was significantly greater in wt-Ad3GFP infected cultures compared to mu-Ad3GFP ( Figures S10C and S10D ) . T84 and A549 cells form xenograft tumors in immunodeficient mice ( Figure S11 ) . These tumors resemble the histology of epithelial tumors in humans . Most solid tumors are of epithelial origin and , although malignant cells are dedifferentiated , they maintain intercellular junctions both in the primary tumor as well as in metastatic lesions [11] . Unlike normal epithelial tissues , in epithelial tumors such as T84 and A549 xenografts , not all of the DSG2 molecules are trapped in epithelial junctions . DSG2-targeting Ad3 based vectors therefore efficiently transduce tumor cells after intravenous injection into mice [2] , [12] . We studied viral spread of wt-Ad3GFP and mu-Ad3GFP after intravenous injection into mice with pre-established subcutaneous T84 and A549 tumors ( Figure 7A ) . Upon injection , viruses were allowed to replicate in tumors for two weeks . Sections of T84 tumors from mice injected with wt-Ad3GFP showed larger foci with central necrosis and peripheral GFP expression than tumors from mu-Ad3GFP injected animals ( Figure 7B ) . To quantitate the percentage of GFP expressing cells in tumors , we generated single tumor cell suspensions by protease digestion of tumors and analyzed them by flow cytometry . We found ∼3-fold more GFP-positive cells in tumors from wt-Ad3GFP injected mice compared to mu-Ad3GFP ( Figure 7C , upper panel ) . Interestingly , the mean GFP fluorescence intensity was significantly higher in mu-Ad3GFP transduced cells ( Figure 7C , lower panel ) . This indicates that mu-Ad3GFP is confined to the initially infected and directly adjacent cells due to the lack of PtDd production and the subsequent PtDd-mediated junction opening . In contrast , wt-Ad3GFP progeny can penetrate farther from the initial infection site due to its ability to open epithelial junctions . Similar results were obtained in the A549 xenograft model . Staining of A549 tumor sections for E-cadherin suggests that junctions around wt-Ad3GFP infected cells are absent while they are visibly present in areas of mu-Ad3GFP transduced cells ( Figure 7D ) . The latter is supported by morphometry of E-cadherin signals on tumor sections . The area of E-cadherin was 6 . 5 ( +/−1 . 3 ) % and 13 . 3+/−1 . 8% ( based on mm2 E-cadherin per mm2 tumor section ) for wt-Ad3GFP- and mu-Ad3GFP injected mice respectively . So far , we have worked with Ad3-based vectors . The subgroup of DSG2-interacting Ad serotypes comprises Ad3 , Ad7 , Ad14 and Ad11 . We hypothesized that these serotypes have evolved a similar strategy for viral dissemination in epithelial cells that involve the formation of PtDd . To test this , we infected HeLa cells with wild-type Ad14 virus and analyzed PtDd production in comparison to wild-type Ad3 and Ad5 virus ( Figure 8 , Figure S12 ) . In this study , we capitalized on the fact that the anti-serum raised against purified Ad3 PtDd crossreacted with penton base from other serotypes . As seen before with wt-Ad3GFP , wild-type Ad3 PtDd appeared in sucrose fractions 30–40% . The signals in fractions with lower sucrose concentrations originated from free capsid proteins and defective particles . Importantly , PtDd signals were detectable for Ad14 but not for Ad5 . The latter is in agreement with earlier studies demonstrating that Ad2 and Ad5 are unable to form stable pentondodecahedra [9] .
During Ad3 infection , PtDd are produced in vast excess and released early in infection by a still unknown , non-lytic mechanism . We asked the question why Ad3 produces PtDd . In this study , we demonstrate that PtDd trigger the opening of epithelial junctions and thus support the lateral spread of Ad3 progeny virus in epithelial tissue . In the past , most basic adenovirology studies have been done with the CAR-interacting serotypes 2 and 5 . During the replication of these serotypes , penton base and fiber are also produced in excess and only a fraction is incorporated into virions . It has been reported that Ad2 penton base and fiber self-assemble into pentons . However , Ad2 pentons do not spontaneously form dodecahedra . It is thought that Ad2 pentons can form salt bridges but the penton N-termini do not rearrange as is the case for Ad3 . Ad2 penton dodecamerization is only possible under artificial conditions , such as in the presence of dioxane and ammonium sulphate [9] . This raises the question why Ad3 pentons form dodecamers . The answer lies in the different mechanisms that Ad5 and Ad3 use to bind to their corresponding receptors , CAR and DSG2 . High affinity Ad5 attachment involves one Ad5 fiber knob monomer and one CAR monomer , implying that one trimeric Ad5 fiber knob binds to three CAR molecules [13] . Ad5 attachment can be completely blocked by an excess of recombinant soluble fiber knob . This is not the case for Ad3 . Trimeric Ad3 fiber knob was unable to block Ad3 virus binding . We showed that the efficient binding of Ad3 to DSG2 requires multiple fibers in a spatial constellation present in virions or PtDd [1] . This mode of binding triggers DSG2 clustering and subsequent intracellular signaling that results in opening of epithelial junctions [2] . Therefore for Ad3 pentons to exert a function they have to assemble into PtDd . Furthermore , the release of Ad3 PtDd from infected cells before disruption of infected cells by de novo produced virions appears to be important for its function in supporting viral spread . At this time , the mechanism of PtDd release is unclear . Ad2 and Ad5 pentons are released from infected cells through a non-lytical export mechanism that does not involve Golgi vesicles or lysosomes [5] . Previous studies showed that released PtDd remain cell membrane-associated and appear to pass along inside the paracellular space [10] . Along this line , we found that after infection of HeLa cells with wt-Ad3GFP , PtDd were not efficiently released into the culture medium ( Figure S13 ) . This is in agreement with studies on Ad2 , which failed to recover free penton base or pentons from the extracellular medium [5] . Potentially , the release of Ad3 PtDd ( and Ad2 penton ) involves mechanism used by proteins of other viruses to exit cells . For example , the small HIV TAT protein is released from cells by interaction of an alpha helical region and negatively charged heparan sulfate proteoglycans or phospholipids in the cell membrane [14] . It is also conceivable that Ad3 PtDd interact with DSG2 or integrins present in the endoplasmatic reticulum and that this mediates export . As a tool to prove our hypothesis that Ad3 PtDd facilitate viral spread , we generated Ad3 penton base mutants that are disabled for the production of PtDd . Previous studies indicated that penton base mutagenesis can negatively affect protein expression and folding [15] . Based on the recently published 3D structure of Ad3 PtDd , we therefore focused on the substitution of selected amino acid residues that appeared to be critical in PtDd formation and stability . We discovered that the SELS→SDVA substitution , that would prevent the critical N-terminal strand swap , interfered with virus production . However , the double mutations ( D100R and R425E ) that would break up the salt bridge between two neighboring pentons had no effect on Ad3 production . The central finding of this study is that a key function of PtDd during Ad3 virus infection is the facilitation of lateral viral spread through interaction with DSG2 and the opening of epithelial junctions . The latter is supported by our previous studies with recombinant PtDd and the recombinant dimeric Ad3 fiber knob protein JO-1 [1] , [2] , [3] , [4] , [16] . The fact that efficient junction opening can be achieved with JO-1 , a protein that does not contain a penton base , argues against a role of PtDd interaction with cellular integrins in junction opening . It appears that within PtDd , the penton base has the function to bring fiber knobs into an optimal constellation for DSG2 binding and , potentially , mediate the release of PtDd from infected cells . Notably , the interaction of penton base within the complete virion with integrins is essential for virus entry into cells . As outlined above , CAR-interacting Ads have evolved another mechanism to support viral dissemination in cells where the target receptor is trapped in tight junctions and not readily accessible to Ad particles entering the body through the respiratory tract [17] , [18] . A number of studies have demonstrated that during replication of Ad5 , excess production of fiber or fiber/penton base complexes results in the disruption of epithelial junctions either by interfering with CAR dimerization or by triggering intracellular signaling that leads to reorganization of intercellular junctions [18] , [19] . It is also noteworthy , that similar to CAR , DSG2 appears to be primarily a receptor involved in lateral spread of Ad in polarized cultures . In normal lung epithelium , DSG2 is trapped in junctions and not accessible to Ad from the apical site [2] , [3] . Epithelial cancer cells ( such as T84 ) have lost this strict polarization in vitro and in vivo , which allows for some degree of Ad3 transduction [3] . Ad3 PtDd could theoretically have a number of other functions in Ad3 infection of epithelial tissues . i ) PtDd interaction with DSG2 and/or integrins in surrounding cells could prepare these cells for viral DNA replication , e . g . by activating the cell cycle which is particularly important in the case of non-dividing cells . ii ) PtDd released from infected cells could mask DSG2 on infected and the neighboring cells and thus help viral dissemination to more distant cells . iii ) As suggested by Trotman et al . [5] , another function of excess production and release of PtDd could be the trapping of anti-Ad3 antibodies locally at the site of infection , but also systemically , thus avoiding the neutralization of de novo produced virions . PtDd could also form decoys for defensins , proteins of the immune system that suppress viral and bacterial infections . Defensins , specifically human alpha defensin 5 , recognize residues/structures within the penton base and , it is thought that this interaction blocks the endosome release of adenoviral particles during infection [20] . All of the hypotheses listed above remain to be tested . Most of our studies were performed with T84 cells . This cell line forms tight junctions in vitro and has therefore been widely used in studies on the interaction of Ad5 with the tight junction protein CAR [18] . Clearly , additional studies with polarized human airway epithelia have to be conducted to consolidate our findings . Our findings on the function of Ad3 PtDd are relevant for basic and applied virology . We provide first evidence that PtDd are also formed by another DSG2-interacting Ad serotype , Ad14 . In our studies , we used a newly emerged Ad14 strain ( Ad14p1 ) [21] . Compared to the parental strain ( Ad14-deWit ) , Ad14p1 is more pathogenic/virulent [22] , [23] , [24] . Understanding how Ad spreads and penetrates through the airway epithelium also might explain how Ad3 establishes viremia and infects other tissues such as the gastro-intestinal tract . After cough and shortness of breath , the main symptoms associated with a recent Ad14p1 outbreak in the USA were vomiting and diarrhea [25] . There is a series of independent reports from different Asian , African , and South American countries stating that Ad3 infection in children is associated with acute gastroenteritis [22] , [26] . We speculate that the other DSG2-targeting Ads ( Ad7 , Ad11 ) also produce PtDd . As outlined above , dodecamerization of pentons is functionally critical to trigger opening of epithelial junctions upon binding to DSG2 . The theoretical basis for cancer therapy by oncolytic adenoviruses is that a virus that specifically replicates in tumor cells spreads throughout the tumor , thus eliminating it . In reality , viral spread is blunted by anti-virus immune responses and physical barriers such as epithelial junctions , which are a hallmark of most epithelial tumors . Understanding how Ad3 as well as a number of other parasites that have evolved mechanisms to efficiently breach the epithelial barriers [27] is relevant for the development of more efficient oncolytic adenoviruses . In summary , our study contributes to a better understanding of Ad3 infection and pathology . It also has implications for Ad-mediated gene transfer into epithelial tissues and tumors .
Recombinant Ad3 penton-dodecahedra ( PtDd ) were produced in insect cells and purified as described previously [8] . The following antibodies were used for immunofluorescence or immunoblot studies: polyclonal goat anti-DSG2 ( R&D Systems , Inc , Minneapolis , MN ) , polyclonal goat anti-human E-Cadherin ( cat . AF648 , R&D Systems , Inc , Minneapolis , MN ) , mouse mAb anti-DSG2 ( clone 6D8 ) ( Cell Sciences , Canton , MA ) , anti-fiber primary antibody ( Clone 4D2 ) ( Thermo Fisher Scientific , Fremont , CA , USA ) , polyclonal goat anti-Ad2 ( cat . 1401 , Virostat , Maine , USA ) , monoclonal anti-actin ( Sigma , St . Louis , MO ) , FITC conjugated goat anti-adenovirus ( Millipore Billerica , MA ) , and PE conjugated rabbit anti-E-cadherin ( BD Biosciences ) . The following secondary antibodies were used: anti-rabbit-AF488 , anti-rabbit-AF568 , anti-mouse-AF488 , anti-mouse- AF568 , anti-goat-AF568 ( Invitrogen/Molecular Probes , Eugene , OR ) . Polyclonal rabbit antibodies against purified recombinant Ad3 knob were produced by PickCell Laboratories B . V . ( Amsterdam , The Netherlands ) . The polyclonal rabbit antibody against purified recombinant Ad3 PtDd was described earlier [7] . Rhodamine labeled Concanavalin A ( cat . RL-1002 ) and vectorshield mounting medium with DAPI ( cat . H-1200 ) were purchased from vector laboratories ( Burlingame , CA , USA ) . 293 ( Microbix , Toronto , Ontario , Canada ) , HeLa and A549 ( American Type Culture Collection [ATCC] ) cells were cultured in Dulbecco's modified essential medium ( DMEM ) supplemented with 10% fetal calf serum ( FCS ) , 2 mmol/liter L-glutamine ( Glu ) , 100 U/ml penicillin , and 100 µg/ml streptomycin ( pen-strep ) . Colon cancer T84 cells ( ATCC CCL-248 ) were cultured in a 1∶1 mixture of Ham's F12 medium and DMEM , 10% FBS , Glu , and pen-strep . To achieve cell polarization , 2×105 T84 cells were cultured in 6 . 5-mm Transwell inserts ( 0 . 4 µm pore size ) ( Costar Transwell Clears ) for more than 14 days until transepithelial resistance was stable . Culture medium was changed every 2–3 days . Propagation and purification of Ads were performed as described elsewhere [28] . Wild-type Ad5 was rescued from pFG140 plasmid transfected 293 cells ( Microbix , Toronto , Ontario , Canada ) . Ad14p1 ( strain Portland 2971/2007 ) was provided by the Center for Disease Control and Prevention ( Atlanta , GA ) [21] . wt-Ad3GFP is a wild-type Ad3-based vector containing a CMV-GFP expression cassette inserted into the E3 region [2] . Penton base-mutated Ad3-GFP ( mu-Ad3GFP ) was constructed as following: wt-Ad3GFP plasmid ( pWEA-Ad3GFP ) was digested with AscI , and the short fragment ( 7772 bp ) was ligated to an AscI-digested kanamycin resistant gene-pBR322 origin containing fragment , which was amplified from pShuttle-CMV plasmid ( Stratagene , La Jolla , CA ) with primers 1210KAscI01 and 1210KAscI02 , to generate pKPenton3AscI ( see Table 1 ) . To introduce D100R mutation to pKPenton3AscI , PCR was performed with primers 1210PentonM01 and 1210PentonM02 using pKPenton3AscI as the template . PCR product ( 1233 bp ) was excised with BamHI and NheI , and inserted into the corresponding sites of pKPenton3AscI to generate pKPenton3R . Similarly , PCR was conducted using pKPenton3AscI as the template with primers 1210PentonM03 and 04 , and with primers 1210PentonM05 and 06 . The two PCR products ( 344 and 1247 bp ) were united using overlap extension PCR with primers 1210PentonM03 and 1210PentonM06 . The recovered fragment ( 1545 bp ) were digested with XbaI and XmaI , and substituted for the XbaI-XmaI region in pKPenton3R to generate pKPenton3RE . The D100R and R425E mutations in pKPenton3RE were confirmed by sequencing . pKPenton3RE was further digested with AscI , and penton-mutated fragment was inserted back to pWEA-Ad3GFP to generate a new adenovirus plasmid pWEA-mu-Ad3GFP . pWEA-mu-Ad3GFP was linearized with FseI , and then used to transfect 293 cells . The rescued viruses were propagated in HeLa cells and purified by standard methods . We have sequenced the genome of both viruses and did not find additional mutations . Whole genome sequencing was performed by Eurofins MWG Operon ( Huntsville , AL ) . Ad particle ( viral particle [VP] ) concentrations were determined spectrophotometrically by measuring the optical density at 260 nm ( OD260 ) . Infectious titers ( IU/ml ) of progeny viruses were determined by limiting dilution assay on 293 cells . Briefly , tenfold serially diluted virus was used to infect exponentially growing 293 cells in 96-well plates . At 48 hours postinfection , GFP-positive cells were counted under a fluorescence microscope . The infectious titer was calculated as the average number of GFP-positive cells per well×dilution factor/volume of virus suspension per well . The multiplicity of infection ( MOI ) was calculated from particle titer . The VP/IU ratio was 20∶1 for all virus preparations . Hela cells in three 15-cm dishes were infected with virus ( wt-Ad3GFP , mu-Ad3GFP , Ad5 or Ad14 ) in 20 ml medium/dish . 6–12 hours later , virus containing medium was discarded , and 15 ml fresh medium plus 2% FBS was added to each dish . 24–48 hours post infection , culture medium was aspirated to a 50-ml tube . The cells were washed once with 7 ml PBS/dish , scraped down in 4 . 5 ml PBS ( 3 dishes ) , and subjected to 3 rounds of freeze and thaw . Cell debris was removed by spinning at 16 100 g for 4 minutes , and the lysate supernatant was laid on the sucrose gradients . In some indicated cases , the proteins in culture medium were precipitated by addition of ammonium sulfate to a saturation of 65% , dialyzed against ultracentrifugation buffer ( 10 mM Tris-Cl , 150 mM NaCl , 10% glycerol , pH 7 . 6 ) , and laid on sucrose gradients . After 4 hours of ultracentrifugation ( SW41Ti rotor , 35 , 000 rpm , 200 , 000 g ) , the ultra-clear thin tube was taken out and held vertically on a clamp stand . A syringe needle ( size: 18G ) was used to penetrate the tube from the side above the 40% sucrose layer . Gradient fractions were collected from the needle and fractioned into tubes . The fractions were mixed with 2×SDS loading buffer , boiled for 5 minutes , loaded on 4–15% SDS-PAGE at 20 µl/well for cell-associated PtDd or 35 µl/well for released PtDd . Western blots were performed with anti-Ad3 PtDd primary antibody ( dilute 1∶2000 for cell-associated PtDd or 1∶1000 for released PtDd ) and anti-rabbit secondary antibody ( 1∶2000 ) . T84 cells were cultured in 0 . 4 µm transwell insert for 16 days , and then infected with mu-Ad3GFP or wt-Ad3GFP at MOIs of 2 , 10 and 50 vp/cell for 2 hours . Supernatants were collected from the inner chambers at days 4 , 7 , 9 and 10 post infection , and from the outer chambers at days 9 and 10 . Progeny virus in the collected culture media was titrated on 293 cells . Cells were washed once with PBS , fixed with 4% PFA for 15 minutes at room temperature , permeabilized with 1% BSA-PBS plus 0 . 3% triton X-100 , and hybridized with primary antibody and fluorescein labeled secondary antibody . The cellular nuclei were counterstained by mounting samples with vectorshield mounting medium containing DAPI . Sample slides were observed under a Zeiss LSM 510 META confocal microscope . Generally , three color signals were collected with multitracking mode , and Z-stacks with a slice thickness of 1 or 2 µm were acquired . Images of E-cadherin staining on T-84 transwell cultures and A549 tumor sections were taken using a Leica DFC300FX digital camera with a Leica DMLB microscope . Ten 20×-magnification pictures per T84 transwell ( five sections per well , collected at a distance of 50 µm , 2 infected wells ) and fifteen 20×-magnification pictures per A549 tumor ( five sections per tumor , collected at a distance of 100 µm , 3 tumors ) were analyzed using the Image-Pro Plus program ( Media Cybernetics , Bethesda , MD ) and the E-cadherin-positive area ( % E-cadherin area ) was calculated based on mm2 E-cadherin per mm2 tumor section . Technical details for morphometry have been described earlier [29] . Polarized T84 cells cultured in transwell chambers were exposed to cell lysates or gradient fractions ( obtained 24 hours after infection ) ( 20 µg total protein/ml ) in adhesion medium ( DMEM , 1% FBS , 2 mM MgCl2 , 20 mM HEPES ) for 15 min at room temperature and TEER was measured and calculated as described elsewhere [18] . All experiments involving animals were conducted in accordance with the institutional guidelines set forth by the University of Washington . The University of Washington is an AALAC ( Association for the Assessment and Accreditation of Laboratory Animal Care International ) accredited research institution and all live animal work conducted at this university is in accordance with the Office of Laboratory Animal Welfare ( OLAW ) Public Health Assurance ( PHS ) policy , USDA Animal Welfare Act and Regulations , the Guide for the Care and Use of Laboratory Animals and the University of Washington's Institutional Animal Care and Use Committee ( IACUC ) policies . The studies were approved by the University of Washington IACUC ( protocol # 3108-01 ) . Mice were housed in specific-pathogen-free facilities . T84 cell and A549 xenograft tumors were established by subcutaneous injection of 2×106 cells . Ad3 virus ( 2×109 IU/mouse ) was intravenously injected . Tumors were harvested 10 days later . Half of the tumor was used for cryosection . The other half was digested with collagenase/displays to generate single cell suspensions as described earlier [30] . GFP expression in tumor cell suspensions was analyzed by flow cytometry . Ad3 penton base structure ( PDB 4AQQ ) was imaged using Pymol software . Polar contact between monomers of two adjacent penton bases was calculated by Pymol software . In the green overall Bs-Dd structure , the salt bridges were highlighted by drawing D100 and R425 in red and blue spheres respectively . All results are expressed as mean +/− SD . 2-Way ANOVA for multiple testing was applied . Animal numbers and P values are indicated in the figure legends . | We have recently reported that a group of human Ads uses DSG2 as a receptor for infection . Among the DSG2-interacting Ads is serotype 3 , which is widely distributed in the human population . During Ad3 infection , subviral particles ( PtDd ) formed by two capsid proteins are produced in vast excess and released early in infection . In this study , we demonstrate that PtDd trigger the opening of epithelial junctions and thus support the lateral spread of Ad3 progeny virus in epithelial tissues . Our study contributes to a better understanding of Ad3 infection and pathology . It also has implications for Ad-mediated gene transfer into epithelial tissues and tumors . | [
"Abstract",
"Introduction",
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"Methods"
] | [] | 2013 | Penton-Dodecahedral Particles Trigger Opening of Intercellular Junctions and Facilitate Viral Spread during Adenovirus Serotype 3 Infection of Epithelial Cells |
Bmal1 is an essential transcriptional activator within the mammalian circadian clock . We report here that the suprachiasmatic nucleus ( SCN ) of Bmal1-null mutant mice , unexpectedly , generates stochastic oscillations with periods that overlap the circadian range . Dissociated SCN neurons expressed fluctuating levels of PER2 detected by bioluminescence imaging but could not generate circadian oscillations intrinsically . Inhibition of intercellular communication or cyclic-AMP signaling in SCN slices , which provide a positive feed-forward signal to drive the intracellular negative feedback loop , abolished the stochastic oscillations . Propagation of this feed-forward signal between SCN neurons then promotes quasi-circadian oscillations that arise as an emergent property of the SCN network . Experimental analysis and mathematical modeling argue that both intercellular coupling and molecular noise are required for the stochastic rhythms , providing a novel biological example of noise-induced oscillations . The emergence of stochastic circadian oscillations from the SCN network in the absence of cell-autonomous circadian oscillatory function highlights a previously unrecognized level of circadian organization .
Mammalian circadian clocks are cell autonomous and self-sustained within the central pacemaker , the suprachiasmatic nucleus ( SCN ) , as well as within peripheral tissues and fibroblasts [1]–[4] . Circadian rhythms are generated at the molecular level by an autoregulatory transcriptional and translational feedback loop [5] , [6] . The bHLH-PAS proteins , CLOCK ( Clock RefSeq: NM_007715 ) and BMAL1 ( Arntl RefSeq: NM_007489 ) , form an activator complex that drives transcription of the Per ( RefSeq: Per1 NM_011065 and Per2 NM_011066 ) and Cry ( RefSeq: Cry1 NM_007771 and Cry2 NM_009963 ) genes [7]–[9] . PER and CRY proteins then form repressor complexes and translocate back to the nucleus to inhibit their own transcription [10] , [11] . Previous work has demonstrated that Bmal1 plays an essential role in normal circadian clock function—its inactivation leads to a loss of circadian rhythmicity at the behavioral level [7] . To understand the role of Bmal1 at a more mechanistic level , we have analyzed the effects of a Bmal1 loss-of-function mutation ( Bmal1−/− ) on cell- and tissue-autonomous circadian rhythms in the SCN and peripheral tissues . We report here an unexpected stochastic rhythmicity in the SCN of Bmal1-deficient mice , which appears as an emergent network property of the SCN . The roles of intercellular coupling and molecular noise in circadian clock function have been analyzed using mathematical models [12]–[15] . Previous work on modeling the role of coupling within the SCN [16]–[22] and in populations of coupled oscillators [23]–[25] highlights two key ideas: ( 1 ) Intercellular coupling can induce rhythmicity in a population of damped oscillators [26]—a prediction that has recently been validated in some [20] , [21] but not all [27] modeling studies , and ( 2 ) intercellular coupling can synchronize and improve the precision of noisy intracellular oscillators [27]–[30] . More recent modeling studies have emphasized the importance of molecular noise [30]–[35] and its contribution to the generation of single-cell rhythmicity [31] , [32] , [34] , [36] . Importantly , the combination of both intercellular coupling and molecular noise has not previously been shown to generate oscillations in the absence of component oscillatory elements . In the SCN , the nature and dynamics of the coupling network itself remain largely undefined . Vasoactive intestinal polypeptide ( VIP ) ( Vip RefSeq: NM_011702 ) and its receptor Vipr2 ( RefSeq: NM_009511 ) are critical signaling elements within the coupling pathway in the SCN [37] , [38] . However , it is not known whether the coupling pathway itself may contribute to temporal dynamics and , if so , what its kinetics might be . We report here a mathematical model that incorporates both intercellular coupling and molecular noise , and we show that the interplay between the two can generate oscillations in the SCN neuronal network , even in the absence of a functional circadian clock in individual cells . Our results suggest that the SCN network itself in conjunction with the acute induction pathways for Per1 and Per2 can generate quasi-circadian oscillations .
Bmal1 is the only known clock gene whose loss-of-function leads to complete loss of circadian rhythmicity in wheel-running behavior [7] . In addition , circadian rhythms of Per1 and Per2 mRNA expression are disrupted in the SCN of Bmal1−/− mice [7] . To analyze the role of Bmal1 in cell- and tissue-autonomous circadian properties in central and peripheral oscillators , we measured PER2::LUC bioluminescence rhythms in various tissue explants from wild type ( WT; Bmal1+/+ ) and homozygous Bmal1 mutant ( Bmal1−/− ) mice . Surprisingly , a persistent , yet highly variable , rhythmicity emerged in Bmal1−/− SCN explants ( Figure 1 and Video S1 ) , whereas all peripheral tissues failed to generate PER2::LUC circadian rhythms ( Figure 1A ) . WT and Bmal1−/− SCN explants show persistent PER2::LUC bioluminescence rhythms for more than 35 d in culture ( Figure 1B and 1C; also see Datasets S1 and S2 ) . Fast Fourier transform ( FFT ) spectrograms of the records show a consistent frequency ( about one cycle per day ) for the WT SCN; however , more variable frequency components are observed in the Bmal1−/− SCN ( Figure 1B and 1C , middle ) . Double-plotted raster plots illustrate the stable PER2::LUC rhythmicity in the WT SCN and the unstable rhythms in Bmal1−/− SCN ( Figure 1B and 1C , right ) . FFT analysis shows that the dominant periodicity in WT SCN is tightly clustered around 24 h , while periodicity is much more variable , ranging from about 14 to 30 h , in Bmal1−/− SCN ( Figure 1D ) . Because of the highly variable periods and instability of the rhythmicity from Bmal1−/− SCN , we refer to these fluctuations as stochastic . All tissues from WT littermates , including the SCN , pituitary , liver , lung , and cornea , displayed normal circadian rhythms similar to those observed in previous studies ( Figure 1A ) [4] , [28] . Due to the stochastic nature of the rhythms in the Bmal1−/− SCN , period estimates based on averages of long time series do not adequately describe the cycle-to-cycle variability . Hence , we measured individual peak-to-peak intervals of the PER2::LUC expression patterns from each SCN explant ( Figure 1E , left ) . The mean inter-peak intervals observed in the WT SCN explants were circadian at about 24 h ( 24 . 3±1 . 03 SD h ) . These values were similar to the periods estimated by Levenberg-Marquardt ( LM ) curve fitting of the entire time series using the LumiCycle Analysis Program ( WT = 24 . 04±0 . 09 SD h , n = 23 ) . By contrast , the Bmal1−/− SCN showed a significantly shorter average inter-peak interval length of 17 . 8 h with a much greater variance ( SD = 5 . 54 h , n = 19 ) than WT SCN explants . Using the inter-peak interval data , we determined the Serial Correlation Coefficient of successive intervals , rs ( Figure 1E , right ) . A negative serial correlation reflects the likelihood that a long cycle will be followed by a short cycle , or vice versa , which is a characteristic feature of a functional pacemaker-driven system [39] . The average serial correlation coefficients for WT SCN explants were negative ( mean rs = −0 . 17±0 . 0606 SEM , t-test , p<0 . 01 ) as would be expected from a circadian pacemaker-driven process ( Figure 1E , right ) . However , the coefficient for the Bmal1−/− SCN was slightly positive and marginally significant ( mean rs = 0 . 07±0 . 0336 SEM , t-test , p<0 . 05 ) . This weak serial correlation coefficient differs from the pacemaker-driven processes seen in WT SCN explants and is consistent with either an oscillator with a highly labile period or a “random walk” process [40] . To determine whether the stochastic PER2::LUC rhythms from Bmal1−/− SCN explants are cell autonomous , we studied PER2::LUC bioluminescence at the single-cell level ( Figure 2 ) . We first imaged the overt bioluminescence expression patterns from Bmal1−/− SCN explants ( Figure 2A , Video S1 , Datasets S3 and S4 ) and analyzed bioluminescence from individual neurons ( Figure 2B and 2C ) . The SCN cells in an intact organotypic slice were tightly synchronized and exhibited stochastic rhythms comparable to those seen in the Bmal1−/− SCN explants using luminometry . Separately , we cultured dissociated SCN neurons and imaged bioluminescence from individual cells . In contrast to the cells in an intact organotypic slice , dissociated Bmal1−/− neurons did not express detectable circadian rhythms ( Figure 2D , 2E , 2F , Video S2 , Datasets S5 and S6 ) . A total of 243 out of 243 Bmal1−/− SCN cells were equal to or below a threshold FFT amplitude for rhythmicity independently derived from WT SCN cells . Although Bmal1−/− SCN cells did not generate obvious circadian oscillations , they did express fluctuating levels of PER2::LUC—likely a reflection of Per2 transcriptional and post-transcriptional noise ( Figure 2F ) . The lack of rhythmicity in dissociated SCN cells indicates that the stochastic rhythmicity observed in Bmal1−/− SCN explants is not a cell-autonomous property and likely arises from intercellular network interactions ( to be addressed later ) . Thus , the integrity of the cellular anatomy of the SCN is an important factor for stochastic rhythm generation in Bmal1−/− SCN explants . To explore possible origins of the stochastic rhythmicity in the Bmal1−/− SCN , we used mathematical modeling of circadian oscillators to examine the role of intercellular coupling and molecular noise . The key clock components in our mathematical model and their possible interactions within a single SCN cell are depicted in Figure 3A . This model is similar to the Forger-Peskin stochastic model [34] but with three refinements . In the current simulation , we ( 1 ) explicitly modeled the binding of CLOCK:BMAL to regulatory regions of the Per1 , Per2 , Cry1 , and Cry2 genes; ( 2 ) explicitly modeled the interaction of BMAL with CRY1 or CRY2 and its subsequent effects on transcriptional regulation; and ( 3 ) updated the rates of degradation of mRNAs and proteins using measurements from recent experiments [41] . An explanation of these changes , as well as a list of rate constants , can be found in Figure S7 ( also see Protocols S1 and S2 ) . We accounted for the heterogeneity of circadian period of SCN neurons by choosing all rate constants from Gaussian distributions . When no Gaussian variation is introduced to the rate constants ( Figure 3B , “0%” ) , the standard deviation for period of the population of oscillators is 1 . 03 h , which reflects variability in period due to the stochastic model ( i . e . , no rate constant variability ) . As the standard deviation of the rate constants is increased , the standard deviation of the circadian period from a population of oscillators increases further ( Figure 3B ) . When we chose the standard deviation of these Gaussians to be within 5% of their mean values , the resulting period of the model simulations had a standard deviation ( SD = 1 . 54 h ) similar to the experimental data obtained from dispersed WT SCN neurons , which have a standard deviation of 1 . 32 h for circadian period ( Figure 3C ) . When these individual neurons were coupled in a network of cells ( see below ) , the total mRNA and protein concentrations followed time courses similar to those found experimentally ( Figure 3D ) . This stochastic model was then used to simulate Bmal1−/− SCN isolated cells and the Bmal1−/− coupled SCN network , respectively . As a first step , we examined the effects of reducing the total BMAL activator concentration on the generation of rhythms in simulations of isolated cells . As the BMAL activator abundance was gradually decreased , the distribution of periods widened and the mean period shortened until a complete loss of rhythms was observed below 20% BMAL activator abundance in a population of uncoupled cells ( Figure 4A and 4B ) . This behavior is consistent with bifurcation diagrams of single cell simulations ( Figure 4C ) that show the existence of a Hopf bifurcation at approximately 22% of total BMAL . Above this percentage , oscillations can be shown to emerge in our simulations . At 0% total BMAL activator abundance no element in the model could initiate transcription; thus , some level of residual BMAL activator must be present in the model in order for clock gene expression to occur . In Bmal1 null mutants , other functional E-box activator proteins may be present . The most likely candidate for “residual BMAL” activity in Bmal1 mutants is BMAL2 ( also known as MOP9; RefSeq: Arntl2 NM_172309 ) , a paralog of BMAL1 , which is regionally co-expressed in the SCN and forms transcriptionally active complexes with CLOCK [7] . BMAL1 and BMAL2 show similar sensitivity to CRY1-mediated transcriptional repression ( Figure S1; for methods , see Text S1 ) , which suggests that the core negative feedback loop of the circadian oscillator could remain functional in the presence of BMAL2 . Using quantitative PCR methods that normalize the amplification of Bmal1 and Bmal2 RNA , we estimate that Bmal2 mRNA levels are approximately 10% of the level of Bmal1 and that Bmal2 expression levels are unaffected by Bmal1 loss-of-function ( i . e . , no compensation of Bmal2 in Bmal1−/− mutants ) ( Figure S2; for methods , see Text S1 ) . Indeed recent work has shown that Bmal2 can substitute for Bmal1 if ectopically driven at high levels in Bmal1−/− mice [42] . Since BMAL1 and BMAL2 appear to be functionally equivalent , we use the term “BMAL” in our simulations to represent the sum total of BMAL1 and BMAL2 . On the basis of the expression analysis , we chose the 10% BMAL activator concentration to simulate the Bmal1−/− SCN neuron because Bmal2 represents approximately 10% of the wild-type level of Bmal1; and in Bmal1−/− cells , the remaining Bmal2 would contribute about 10% of the total BMAL1 and BMAL2 activity . Our simulated and experimental isolated cell rhythms show remarkable agreement at the cell autonomous level . In Figure 4D , we show representative experimental records from dissociated WT SCN cells ( first row ) along with their associated spectrograms ( second row ) . Similarly , the third and fourth rows in parallel display the computational simulations for the isolated WT SCN neurons with their associated spectrograms . Comparable traces are shown for isolated Bmal1−/− cells in Figure 4E . Thus , the 10% BMAL activator simulations faithfully capture the stochastic behavior of PER2::LUC expression seen in dissociated Bmal1−/− SCN neurons . Although there are many mathematical models of the SCN that incorporate coupling of a population of circadian oscillators [16] , [19]–[21] , very few models have studied coupling in a population of oscillators in which the individual oscillators are stochastic in nature [26] , [27] . The results in this paper represent the most detailed stochastic simulation of coupled SCN oscillators to date . To model a population of stochastic oscillators in the SCN , we utilized a group of 100 “cell autonomous” stochastic oscillators as described in the previous section and coupled the population of oscillators ( see Figures 5 , S3 , and S4 ) . For the coupling pathway , we used a model of the VIP signaling pathway in the SCN , whereby a rhythmic coupling agent ( CA ) is released by one SCN cell and affects neighboring SCN cells by activating an adenosine 3′ , 5′-monophosphate ( cAMP ) signaling pathway , which ultimately activates cAMP response element binding ( CREB ) protein and cAMP response elements ( CRE ) on the Per1 and Per2 promoters to induce PER1 and PER2 proteins . In our proposed model , the intercellular coupling is all-to-all . We assumed that CRE activation of Per1 and Per2 could occur only when repressors were not bound as suggested in previous experiments [43] . Thus , this is the equivalent of allowing CRY to repress CREB-mediated PER production . When coupling is included in a population of WT single-cell models , this coupled cellular network was able to produce coherent ∼24-h rhythms ( Figure 5B , top , and 5C , bottom ) as seen in WT SCN explants and in other coupled oscillator models of the SCN [16] , [19]–[21] . Remarkably , as seen in experiments , rhythmicity also emerged when a population of nonrhythmic Bmal1−/− SCN simulated neurons was coupled together ( Figure 5C , top and middle ) . To compare these rhythms to those found experimentally , we calculated the peak-to-peak intervals of the rhythms from the SCN network with 100% , 20% , and 10% BMAL activator concentrations ( Figure 5B ) . The distribution of these inter-peak intervals is similar to experimental measurements from WT and Bmal1−/− explant rhythms ( Figure 5B; compare with Figure 1E ) . The observed decrease in period in the simulated SCN explants with lower activator concentrations is similar to that observed in simulated single cells , implying that the short period of the Bmal1−/− explant may be attributable to a shortened intracellular , single-cell periodicity . However , given the general absence of rhythms in individual uncoupled Bmal1−/− cells , it is interesting that coupling of these intrinsically nonrhythmic cells ( 10% BMAL ) can generate stochastic rhythms . While only three sets of simulations are shown in Figure 5B and 5C , we systematically varied the coupling strength and BMAL concentration to determine the amplitude of rhythms and synchrony of the network in Figure S3 . This shows that the stochastic rhythmicity can be seen over a wide range of values for the coupling strength . In addition , we note that the relative amplitudes of the Bmal1−/− simulations are reduced when compared with those found experimentally . Figure S3 shows that the amplitude of these rhythms depends greatly on the coupling strength and mechanism . In particular as the coupling becomes nonlinear , the amplitude increases . Including other nonlinear aspects of the coupling ( e . g . , electrical activity ) could also increase the amplitude . In addition , there are likely to be other transcriptional inputs to the Per and Cry genes that could affect amplitude and that were not included in the model . Since the emergent rhythms in the Bmal1−/− SCN explants were stochastic , we hypothesized that the molecular noise , inherent in all biochemical reactions and included in our current model through our use of the Gillespie stochastic simulation algorithm , could play an important role . To test this hypothesis , we developed a deterministic version of our proposed model to understand the implications of the noise . The deterministic model represents a version of our proposed model without any noise . Each cell in the deterministic model had its parameters chosen from a distribution as was done in the stochastic model . Once the parameter was chosen it remained fixed for the rest of the simulation . For example , the bifurcation analysis in Figure 4C used the deterministic model with its mean parameters . The stochastic and deterministic models produced equivalent results when the number of each molecule species ( Figure 5D ) in the stochastic model was increased . This equivalence serves as a check on the accuracy of both the stochastic and deterministic simulation approaches . The output of 100 simulated cells is shown using both the stochastic and deterministic models in Figure 5C and 5E . The total BMAL concentration was varied from 10% to 20% and finally 100% of the activator level in WT SCN , and the ensemble averages from each simulation are plotted in yellow . Strikingly , when a population of cells using the deterministic model was coupled , rhythmicity could not be recovered at 10% BMAL activator levels and only damped rhythms could be seen at 20% BMAL activator levels ( Figure 5E ) . The discrepancy between the stochastic and deterministic models clearly indicates that noise is a necessary but not sufficient condition for the rhythmicity observed in the Bmal1−/− SCN explants . We reach this conclusion because the deterministic model fails to show any sustained rhythmicity at the 10% or 20% level of BMAL activator concentration . Our analysis indicates that noise alone could not restore the rhythmicity in individual cells , nor is the coupling mechanism alone sufficient to induce oscillations in a population of cells without noise . Thus , what is necessary to induce rhythms in a population is a joint requirement for both molecular noise and intercellular coupling . The coupled stochastic model of the Bmal1−/− SCN predicts that both noise and coupling are required to generate stochastic rhythms . To test the role of coupling in SCN explants , we used a variety of agents previously reported to uncouple SCN neurons . We first used tetrodotoxin ( TTX ) treatment on Bmal1−/− SCN rhythms ( Figure 6 ) . TTX prevents action potentials by selectively and reversibly blocking voltage-dependent Na+ channels . TTX application has been shown to desynchronize neurons within an intact SCN [44] , suggesting that action potentials and/or consequent neuronal transmission are required for maintaining SCN synchrony . Under continuous TTX administration , WT SCN tissue showed persistent PER2::LUC rhythms . However , the amplitude of the rhythm gradually diminished cycle by cycle ( damping ) , which is attributable to intercellular desynchrony and a reduction in amplitude at the individual oscillator level [44] . In contrast , the Bmal1−/− SCN exhibited an immediate loss of stochastic rhythmicity of PER2::LUC output when treated with TTX . The PER2::LUC rhythms returned immediately upon removal of TTX in both WT and Bmal1−/− SCN explants ( Figures 6B and S5 ) . To determine whether the loss of stochastic rhythmicity was due to desynchrony of individual cellular rhythms , we used bioluminescence imaging to measure single-cell behavior during TTX treatment ( Figures 6C and S5 ) . Uncoupling cells by TTX treatment within an organotypic Bmal1−/− SCN slice caused a loss of stochastic rhythmicity at the single-cell level with PER2::LUC patterns similar to those of SCN neurons in dispersed culture . Thus , TTX treatment abolished stochastic oscillations by preventing the sustainment of rhythmicity at the single-cell level . Using the coupled stochastic model of the SCN , we mimicked the experiments that blocked coupling via TTX and then restored the coupling by removing TTX ( Figure 6D ) . Our simulation results reproduce the experimental data for both the WT and Bmal1−/− SCNs; these results demonstrate that abolition of coupling alone in the model can account for the observed patterns of PER2::LUC bioluminescence from Bmal1−/− SCN when blocked with TTX . Similar findings were observed when other pharmacological agents were used to uncouple the SCN neurons ( Figure S6 ) . For example , pertussis toxin treatment , which blocks Gi/o-mediated signal transduction , and bicuculline , which blocks GABAA-receptors , both abolished PER2::LUC stochastic rhythmicity—these agents have previously been shown to uncouple SCN cells [45] , [46] . A prominent pathway for coupling SCN neurons involves the VIP receptor-signaling pathway that activates cAMP and CREB-mediated induction of Per1 and Per2 transcription [37] , [38] , [43] , [47] , [48] . To test the role of the cAMP signaling pathway in coupling in SCN explants , we used MDL-12 , 330A ( MDL ) , a potent inhibitor of adenylyl cyclase , and H-89 , an inhibitor of cAMP-activated protein kinase ( protein kinase A; PKA ) ( Figure 7 ) . Previous studies have shown that MDL reduces cAMP concentrations to basal levels in SCN cells [47] , and H-89 prevents VIP- or calcium-induced circadian transcription in SCN cells [49] . Similar to the effect of TTX treatment , WT SCN explants showed damped rhythms with prolonged exposure to these inhibitors of cAMP-mediated signal transduction ( Figure 7B and 7C , left ) , and the Bmal1−/− SCN explants showed an immediate loss of stochastic rhythmicity ( Figure 7B and 7C , right ) . Thus , the cAMP pathway appears to be necessary for the generation of stochastic oscillations . In the circadian clock mechanism , the half-life of the PER proteins contributes to the determination of circadian period , which is under the control of CK1ε/δ-mediated PER phosphorylation [50]–[52] . Modeling studies have also suggested that the Per2 feedback loop , in particular , may have a dominant role in setting the period of circadian oscillation [53] . The VIP/cAMP signaling pathway ultimately converges on Per induction , and thus the periodicity of stochastic rhythms in Bmal1-null mutant SCN may also depend upon PER . To test a role for PER proteins in determining the period of stochastic rhythms , we examined the effects of agents that inhibit CK1ε/δ to lengthen circadian period . We treated SCN explants with a potent protein kinase inhibitor , SP600125 , which has been shown to lengthen circadian period and to inhibit CK1ε kinase activity [51] . SP600125 treatment lengthened WT SCN circadian period to over 30 h ( Figure 8A , top and 8B , left ) as shown previously . Interestingly , SP600125 also significantly lengthened the period ( average inter-peak intervals ) of the stochastic rhythms from Bmal1−/− SCN explants ( Figure 8A , bottom and 8B , right ) . Modeling experiments in which the phosphorylation rate of PER by CK1 was reduced also lengthened the periodicity of WT and Bmal1-mutant SCN simulations in a similar fashion ( Figure 8C ) . Thus , the kinetics of the quasi-circadian periodicity that emerges from the SCN network are regulated by a PER-dependent process in which the phosphorylation rate of PER by CK1 can determine the periodicity of the emergent network oscillation .
The circadian pacemaker in the SCN plays a dominant role in the generation and control of circadian behavioral rhythms in mammals [54] , [55] . While it is well established that the generation of circadian rhythmicity is a cell-autonomous property of SCN neurons [1] and that coupling plays an important role in enhancing the precision of circadian oscillations [56] , it has not previously been demonstrated that the SCN network itself , in the absence of cell autonomous oscillatory function , can generate quasi-circadian oscillations . The experiments reported here argue strongly that a PER-dependent neural coupling mechanism in the SCN can provide a feed-forward signal to drive the circadian network and propagate quasi-circadian oscillations under conditions when cell-autonomous circadian oscillations have been abolished by the loss of Bmal1 function . Mathematical modeling experiments comparing stochastic and deterministic models of the coupled SCN network strongly suggest that both molecular noise and intercellular coupling are essential for the generation of “emergent” oscillations from the network . While it is clear that coupling is essential for normal SCN function [37] , [38] , [44] and is responsible for the robustness of SCN oscillations [28] , [56] , there are no precedents demonstrating that coupling per se can generate oscillations in the circadian domain . In the field of central pattern generators , rhythmic outputs can be generated by circuits that are devoid of intrinsically rhythmic pacemaker neurons [57] . In such cases , rhythmicity is an emergent property of the network . The time course of central pattern generators , however , is orders of magnitude faster than circadian oscillations . Thus , it seems likely that the mechanisms leading to oscillations in these two time domains will differ . This indeed appears to be the case since transcriptional rather than synaptic mechanisms underlie the long time constants in the circadian range . Because the mammalian circadian clock mechanism involves gene expression , and gene expression is affected by molecular noise [58]–[60] , noise is expected to influence circadian clocks . Indeed molecular noise is a key contributor to the stochastic nature of intracellular rhythmicity [61] , and overcoming molecular noise has been proposed as a key principle in the design of circadian clocks [35] . However , in these previous examples , reduction rather than enhancement of noise has been the key factor . A novel finding of the present study is that molecular noise can be an integral part of the functional SCN network . Molecular noise is amplified by a sensitive coupling mechanism among SCN neurons and can “kick start” oscillations within the network . Future modeling work , using more detailed models of electrical [62] , [63] and chemical [16] , [21] , [64] signaling in the SCN , can be used to identify the specific nonlinearities that can cause noise-induced oscillations [17] . The highly sensitive nature of the coupling mechanism allows for the amplification of not only the oscillatory signals but also the noise intrinsic in the overall network . This noise amplification can explain why the rhythms in Bmal1−/− SCNs are highly variable . Therefore , sensitive transcriptional regulation mechanisms that play a crucial role in promoting rhythmicity are also likely to amplify the effects of molecular noise . This finding is consistent with previous studies , indicating how certain choices of transcriptional regulatory mechanisms can greatly amplify the effects of molecular noise thereby leading to noise-induced oscillations [31]–[35] . Our simulations were able to compare mathematical models with and without noise , something that cannot be easily accomplished experimentally at this time . Molecular noise depends on the number of reactions that take place in a system . Although future experimental techniques may be able to address this issue , they do not exist for the larger mammalian genetic networks [65] . Also , stochastic bifurcation theory could be applied to understand our numerical results mathematically . Classic work has shown that noise can induce oscillations [66] . In addition , there is a growing body of literature that demonstrates how molecular noise can enhance the behavior of cellular networks within many organisms ( reviewed in [58] ) . Similar theoretical and experimental studies of the somite segmentation clock in vertebrate development have shown how coupling can be used to reduce the effects of noise [67] , [68] . Another recent modeling paper has shown how coupling and noise can synergistically enhance calcium oscillations in two coupled cells [69] . This does not mean that noise is beneficial in all circumstances . However , we demonstrate here that the effects of molecular noise can propagate in a large network of coupled cells and may even be beneficial . This result is counterintuitive since previous work indicates that coupling diminishes the effects of noise as one increases the number of cells in the network [17] . Thus , our work establishes the importance of molecular noise in the functioning of intercellular networks . The emergence of stochastic circadian oscillations from the SCN network in the absence of cell-autonomous circadian oscillatory function highlights a previously unrecognized level of circadian organization . While we acknowledge that it is difficult to assess the role of such noise-induced oscillations in the wild-type animal in which oscillations from the cell autonomous oscillators would be expected to dominate the ensemble output from the SCN network , the resonance in the coupling network highlighted here could contribute to the robustness of the mammalian circadian system . In addition , network oscillations of the type reported here could also underlie other rhythmic phenomena such as the food-entrainable oscillator or the methamphetamine-inducible oscillator , which are thought to be independent of the circadian pacemaking system in the SCN [70]–[74] . These normally occult oscillators appear to require either restricted food availability or psychostimulant drugs to activate or reveal their presence . Perhaps feed-forward inputs from feeding signals or psychostimulants can generate quasi-circadian oscillations in behavior in a manner analogous to the stochastic SCN network oscillations reported here .
All animal care and experimental treatments were approved and performed in strict accordance with Northwestern University and University of Texas Southwestern Medical Center guidelines for animal care and use . Mice were bred from PER2::LUC ( http://jaxmice . jax . org/strain/006852 . html ) homozygous parents [4] mated with Bmal1+/− mice [4] , [7] . From the first generation of pups , mice carrying a copy of the Per2::luc ( luc/+ ) and heterozygous for the mutation in Bmal1 were then crossed to obtain Bmal1+/+ , Bmal1+/− , and Bmal1−/− mutants harboring the PER2::LUC reporter . At approximately 8–10 wk of age , mice were placed in individual running wheel cages , and activity was recorded using the ClockLab data collection system ( Actimetrics , Wilmette , IL ) [75] . After 2 wk in LD 12∶12 , the mice were released into constant darkness ( DD ) for an additional 4 wk . Animals were then returned to LD 12∶12 for at least 2 wk before their tissues were harvested for bioluminescence experiments . Animals were euthanized by cervical dislocation between ZT 11–13 . The tissues were removed immediately and put in Hank balanced salt solution ( HBSS; with 10 mM HEPES , 25 units/ml penicillin , and 25 µg/ml streptomycin ) on ice . Brain slices containing the SCN were sectioned at 300 µm using a vibratome followed by scalpel dissection of the SCN , resulting in a piece of tissue about 1 mm×1 mm in size . The peripheral tissues were dissected into pieces approximately 1 mm3 in size , with the exception of the pituitary , which was cultured as a whole . Each dissected tissue was cultured on a Millicell culture membrane ( PICMORG50 , Millipore ) with 1 . 2 ml DMEM medium ( Cellgro ) , supplemented with 10 mM HEPES ( pH 7 . 2 ) , 2% B27 ( Invitrogen ) , 25 units/ml penicillin , 25 µg/ml streptomycin , and 0 . 1 mM luciferin ( Promega ) as described previously [76] . Medium changes were performed by lifting the Millicell culture membrane and placing it into a new culture dish prepared with fresh medium . For peripheral tissues , forskolin ( 10 µM ) was administered for ∼30 min to synchronize the cells before placement into fresh medium . For uncoupling experiments , TTX ( 1 µM ) , BIC ( 200 µM ) , PTX , ( 5 ng/ml ) , MDL ( 1 µM ) , and H-89 ( 10 µM ) were used . To test the role of PER2 in intercellular coupling and period determination , the protein kinase inhibitor , SP600125 ( 25 µM ) , was used . Drugs were added during medium change and were left undisturbed until replacement with fresh medium . All reagents were purchased from Sigma-Aldrich . Explant cultures were maintained at 36 °C either in an incubator or in a temperature-controlled room . The bioluminescence was continuously monitored with LumiCycle photomultiplier tube ( PMT ) detector systems ( Actimetrics , Wilmette , IL ) . Dark counts from the PMT were measured with luciferin-containing medium alone and subtracted from overall bioluminescence . Bioluminescence was measured immediately upon placement in culture , continuing without interruption for >7 d . Bioluminescence analyses were performed using the LumiCycle Analysis Program ( Actimetrics , Wilmette , IL ) . Raw data were baseline fitted . Then peak-to-peak durations ( inter-peak intervals ) were measured by manually identifying individual peaks . Serial correlation coefficient estimates were calculated for 7 to 10 cycle epochs using methods described previously [39] . Bmal1−/− SCN slices used for imaging were dissected from 4–10-d-old pups , cut by tissue chopper ( Stoelting ) to a thickness of 400 µm , and cultured on Millicell-CM membrane inserts ( PICMORG50 ) . SCN slices were maintained in culture for 2–3 wk before imaging . For TTX experiments , adult SCN explants were dissected as described in the previous section . For preparation of dispersed SCN neurons , we used 2–5-d-old Bmal1−/− pups ( or WT controls from the same heterozygous breeding line ) . Cylindrical punches of unilateral SCN were made from 400 µm coronal sections using a 20-gauge needle . Punches from 2–6 mice were pooled in each preparation , and the experiment was performed twice for each genotype . Cells were dissociated using papain and cultured as previously described [1] , except that medium contained 5% FBS instead of rat serum . SCN neurons were maintained in culture for 2–7 wk before imaging . Bioluminescence imaging was performed as previously reported [3] , [28] , [77] . Just before imaging , medium was changed to fresh explant medium containing 1 mM luciferin . Culture dishes were sealed and placed on the stage of an inverted microscope ( Olympus IX71 ) in a dark room . A heated lucite chamber around the microscope stage ( Solent Scientific , UK ) kept the cells at a constant 36°C . For experiments presented in Figure 2 and Videos S1 and S2 , images were collected using an Olympus 4× XLFLUOR ( NA 0 . 28 ) or UPlanSApo ( NA 0 . 16 ) objective and transmitted to a CCD camera ( Spectral Instruments SI800 , Tucson , AZ ) cooled to −92°C . For dispersed neurons , signal-to-noise ratio was improved by 2×2 binning of pixels . Images of 29 . 8 min exposure duration were collected at 30 min intervals for 7 d . SCN neuron viability was assessed by cell morphology and stability of average daily bioluminescence , and no differences were observed between WT and Bmal1−/− cells . For TTX experiments , adult SCN slice images were collected with a dual microchannel plate intensified gallium arsenide phosphide XR/MEGA-10Z CCD camera ( Stanford Photonics , Inc . , Palo Alto , CA ) cooled to −20°C . Bioluminescence was analyzed using MetaMorph ( Molecular Devices ) as previously described [3] , [28] , [77] . For TTX experiments , CellCycle single-cell analysis software ( Actimetrics , Wilmette , IL ) was used . Cells that were clearly discriminable from adjacent cells and that remained bioluminescent for the entire experiment were selected for analysis . Bioluminescence time series were first imported into LumiCycle Analysis v . 2 . 31 ( Actimetrics ) . A linear baseline was subtracted from raw data ( polynomial order = 1 ) . Due to high initial transients of luminescence in some cases , the first 12 h of data were excluded from analysis in all cells . Maximum spectral power value was determined using FFT-Relative Power , which was defined as relative spectral power density at the peak within the range of 0–36 h , i . e . , proportion of total variance within a 0 . 14 cycles/day window centered at highest point within the range of 0–36 h . A cell was considered to show significant circadian periodicity when the spectral analysis indicated a peak in the circadian range ( 20–36 h ) large enough such that a 0 . 14 cycles/day window centered on the peak accounted for at least 10% of the total variance in the record ( FFT power spectrum , Blackman-Harris windowing , peak amplitude ≥0 . 1 ) as described previously [78] . This is the same criterion used in our previous study of mutant SCN neurons [28] , chosen so as to include all clearly rhythmic WT cells . Comparisons significant by ANOVA ( p<0 . 05 ) were further explored by pairwise t-tests . Period was defined as the period of the best-fit sine wave as estimated by a Levenberg-Marquardt algorithm . Simulations used a revised version of the Forger-Peskin stochastic model of the mammalian circadian clock [14] , [34] . We explicitly modeled the binding and unbinding of CRY1 or CRY2 ( and any other bound proteins ) with CLOCK:BMAL ( which has the same biochemical properties as the CLOCK:BMAL1 or CLOCK:BMAL2 complexes , which were treated equivalently ) . Based on experimental evidence [28] , [79] , we assumed that the concentration of these protein complexes was constant ( 1 , 600 molecules per cell for the WT case ) . The original Forger-Peskin model [14] contained a binding event of the CRY proteins to activator on the promoter . This was replaced by a binding event of CLOCK:BMAL to the promoter . Consistent with fitting of experimental data [41] we assumed that there was just one active E-box on the promoters of Per1 , Per2 , Cry1 , and Cry2 . Reaction rates can be found in Figure S7 . We also modeled a CRE element where a factor ( e . g . , CREB ) can bind and activate transcription of Per1 and Per2 or a secreted CA . The “coupling” factor is equivalent to AVP or VIP and mediates signaling between SCN neurons ( see Figure 5 ) . Since medium changes can stimulate transcription , presumably through this CRE element , we assumed that there was a constant low level of this factor in the network ( one molecule per cell ) . Removing this constitutive activation had little effect on our simulations ( unpublished data ) . Due to the revised transcription mechanism , new rates of transcription were chosen to give qualitative agreement of the uncoupled model with protein concentration levels determined experimentally by Lee et al . ( 2001 ) [11] . Other parameters were fit to published experimental data on the mRNA and protein time courses used in previous models [14] , [34] . Degradation rates for Per1/2 and Cry1/2 mRNAs and CRY1 and PER2 proteins were obtained directly from published experimental data [41] . This yielded a model with a slightly short period ( 22 . 9 h ) that was scaled to give a 24-h period . Simulations were performed using the Gillespie Algorithm as in Forger and Peskin ( 2005 ) [34] or MATLAB's ode15s with a maximum time step of 0 . 05 h . In the limit of large numbers of molecules , these simulations matched , as did a version of the model coded in Mathematica . Simulations typically lasted 500 h; the first 50–100 h were discarded to remove the effects of initial transients . Using MATLAB , spectrograms of the experimental and simulated single cell and slice data were produced . Specifically , the “spectrogram” command was used , with window size 128 , overlap set to 100 , and sampling set to 48 cycles per day . Simulated data were collected and analyzed in the same manner as the experimental data ( Figure 4D , 4E ) . Network simulations contained 100 cells with mean field coupling . We then calculated the normalized amplitude and the order parameter defined by Garcia-Ojalvo and colleagues [80] . Codes written in the C programming language are available as Protocol S1 . | The suprachiasmatic nucleus ( SCN ) is the master circadian pacemaker in mammals that controls and coordinates physiological processes in a daily manner . The SCN is composed of a network of cells , with each cell acting as an autonomous oscillator . In isolated individual cells , timekeeping is not precise because of the inherent randomness in the biochemical reactions within each cell , involving its core clock components . However , in the SCN network , precise rhythms can emerge because of intercellular coupling . In this article , we study a loss-of-function mutation of BMAL1 , a core clock component , which eliminates timekeeping in isolated cells . Surprisingly , in both experiments and mathematical simulations , we find that noisy rhythms emerge from the SCN network even in the presence of this BMAL1 mutation . This random yet coordinated timekeeping has not been observed in previous modeling and experimental work and indicates that a network of cells can utilize noise to help compensate for loss of a physiological function . In normal function , the SCN network mitigates any variability observed in individual cellular rhythms and produces a precise and rhythmic network timekeeping signal . When the individual cells are no longer rhythmic , the coupling pathways within the SCN network can propagate stochastic rhythms that are a reflection of both feed-forward coupling mechanisms and intracellular noise . Thus , in a manner analogous to central pattern generators in neural circuits , rhythmicity can arise as an emergent property of the network in the absence of component pacemaker or oscillator cells . | [
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"neuroscience/neural",
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] | 2010 | Emergence of Noise-Induced Oscillations in the Central Circadian Pacemaker |
Human herpesvirus 8 ( HHV-8 ) is causally related to human malignancies . HHV-8 latent viral FLICE-inhibitory protein ( vFLIP ) is a viral oncoprotein that is linked to pathogenesis , but how its expression is regulated is largely unknown . In an attempt to understand the role of the mitochondrial antiviral signaling ( MAVS ) adaptor in HHV-8 infection , we discovered that vFLIP expression was post-translationally up-regulated by the MAVS signaling complex on peroxisomes . Furthermore , we demonstrated that vFLIP could be targeted to the peroxisomes , where it was oncogenically active , in a PEX19-dependent manner . Targeted disruption of vFLIP and MAVS interaction resulted in a decrease in vFLIP expression and selectively promoted death of latently HHV-8-infected cells , providing therapeutic potential for treating HHV-8 diseases . Collectively , our experimental results suggest novel involvement of peroxisomes and MAVS in the stabilization of vFLIP and thereby in the establishment or maintenance of HHV-8 latency and associated pathogenesis .
In response to virus infection , a host innate immune response is activated to restrict virus replication and dissemination . Activation of innate immune antiviral responses relies on signaling pathways relaying viral nucleic acid recognition to the transcriptional machinery . Upon RNA virus infection , viral RNA is recognized by a class of specialized pattern recognition receptors ( PRRs ) ; cytosolic viral RNA is sensed by RIG-I-like receptors ( RLRs ) including RIG-I and MDA5 [1 , 2] , and viral RNA in endosomes is sensed by Toll-like receptors ( TLRs ) including TLR3 and TLR7 [3 , 4] . Upon recognition of these RNA species , RLRs and TLRs recruit specific intracellular adaptor proteins to initiate signaling pathways culminating in activation of transcription factors , nuclear factor-κB ( NF-κB ) and interferon ( IFN ) regulatory factors ( IRFs ) , that upregulate expression of antiviral cytokines . On the other hand , there is accumulating evidence that RIG-I has a role in the detection of several DNA viruses including adenovirus , herpes simplex virus 1 , hepatitis B virus , Epstein-Barr virus ( EBV ) , and human herpesvirus 8 ( HHV-8 , also called Kaposi sarcoma-associated herpesvirus ( KSHV ) ) [5–9] , in some cases by recognizing intermediate RNA species that are generated by RNA polymerase II or III [5 , 10] . In the RLR signaling pathway , the mitochondrial protein MAVS ( also known as IPS-1 , CARDIF , and VISA ) plays a pivotal role as an adaptor . Once activated by the viral RNA-RLR complex , MAVS polymerizes into prion-like filament structures [11] , which nucleate the assembly of a large signalosome complex consisting of the tumor necrosis factor ( TNF ) receptor-associated factor ( TRAF ) proteins [12 , 13] , the TNF receptor type 1-associated death domain protein ( TRADD ) ternary complex [14] , the IκB kinase ( IKK ) complex ( IKKα , IKKβ , and IKKγ ) , and IKK-related kinases TBK1 and IKKε [15] . The MAVS signalosome promotes the activation of IRF3 and/or IRF7 and NF-κB , which then induce the transcriptional activation of type I IFN , proinflammatory cytokines , and a slew of IFN-stimulated genes ( ISGs ) to establish an antiviral milieu in infected and uninfected host cells . MAVS can also localize to peroxisomes where it initiates downstream signaling that induces rapid IFN-independent ISG expression , and preferential expression of type III IFN [16 , 17] , although there was a conflicting report that mitochondria-localized MAVS can induce a robust type III IFN response in virus-infected hepatoma cells [18] . In addition , MAVS has been implicated in mediating virus-induced apoptotic cell death . This antiviral activity of MAVS appears to be dependent on its localization to mitochondria rather than peroxisomes [19 , 20] , inducing mitochondrial dysfunction and oxidative stress by interacting with cellular proapoptotic effector proteins including SARM1 [21] , VDAC1 [20] , MKK7/JNK2 [22] , and caspase-8 [23] . HHV-8 is the etiological agent of Kaposi sarcoma and two lymphoproliferative diseases , primary effusion lymphoma ( PEL ) and multicentric Castleman’s disease [24 , 25] . Like other herpesviruses , HHV-8 exhibits two distinct phases of infection: latency and lytic ( productive ) replication . We and another group reported that MAVS may function as a negative regulator to suppress HHV-8 productive replication [7 , 26] . However , the precise role of MAVS in HHV-8 infection remains unknown . In this study , we discovered a novel and essential function of MAVS in successful maintenance of HHV-8 latent infection by stabilizing HHV-8 viral FLICE-inhibitory protein ( vFLIP ) on peroxisomes . Furthermore , we propose that targeted modulation of the vFLIP-MAVS interaction may have therapeutic potential for treatment of HHV-8 diseases .
To examine the effect of MAVS on HHV-8 infection , the expression of MAVS was genetically ablated by CRISPR/Cas9-mediated mutagenesis in BCBL-1 , an HHV-8-infected PEL cell line , and control HHV-8-negative BJAB B-cell lymphoma cells . For subsequent studies , we used two MAVS knockout ( KO ) BCBL-1 cell lines ( 1A4 and 3B11 ) that were generated by two respective small guide RNAs ( gRNAs ) 1 and 3 ( S1 Table ) , and two control cell lines ( C3 and C6 , hereafter referred to as wild-type ( WT ) BCBL-1 cells ) that harbor empty transfer vector and Cas9 . Consistent with a previous report [7] , expression of lytic genes including MIR-2 ( K5 ) and K8 . 1 was highly up-regulated in MAVS KO BCBL-1 cells after reactivation ( S1A and S1B Fig ) . However , reactivation-induced expression of IFN-α and IFN-λ was not affected by MAVS deficiency in BCBL-1 cells ( S1C and S1G Fig ) . Of note , IFN-β expression was not induced by reactivation in WT and MAVS KO BCBL-1 cells ( S1E Fig ) . On the other hand , latent MAVS KO BCBL-1 cell lines displayed retarded growth under normal culture conditions ( Fig 1A ) ; however , growth of BJAB cells was not affected by MAVS deficiency ( Fig 1B ) , suggesting that MAVS is necessary for proliferation of HHV-8-infected cells . To further test this idea , WT and MAVS KO BJAB cells were infected by the HHV-8 bacterial artificial chromosome 16 ( BAC16 ) virus . The infection efficiency was comparable between the isogenic cell lines , as evidenced by green fluorescent protein ( GFP ) expression encoded by BAC16 , albeit there was slightly less expression in the MAVS KO cells ( Fig 1B ) . Notably , the growth of BAC16-infected MAVS KO BJAB cells was significantly retarded compared to control BJAB-BAC16 cells ( Fig 1B ) . Retarded cell growth could be an indication of cell death . Therefore , we examined whether the growth delay of HHV-8-infected MAVS KO cells was due to cell death . Cells were seeded at two different densities ( 5x104 and 2x105 cells/ml ) , cultured for 2 days , and analyzed by flow cytometry with FITC-annexin V and 7-AAD staining . While necrotic cells ( annexin V–/7-AAD+ ) were barely detectable , early ( annexin V+/7-AAD– ) and late ( annexin V+/7-AAD+ ) apoptotic populations increased about three-fold in both low- and high-density cultures of MAVS KO BCBL-1 cells compared to those of the WT cells ( Fig 1C ) . High-density culture promoted an increase in the number of early and late apoptotic cells by at least two-fold compared to low-density culture ( Fig 1C ) , resulting in a higher percentage of cell death in MAVS KO BCBL-1 ( 40% ) than WT cells ( 15% ) . Interestingly , a pan caspase inhibitor , zVAD-fmk , only partially blocked early and late apoptosis in MAVS KO BCBL-1 cells ( S2A Fig ) , suggesting that caspase-independent death may also be induced in MAVS KO HHV-8-infected cells . Moreover , it is conceivable that the increased death of MAVS KO BCBL-1 cells might be caused by enhanced virus replication , which is associated with increased apoptosis [27] . However , expression of lytic genes such as MIR-2 and K8 . 1 was not induced in latent MAVS KO BCBL-1 cells ( S1A and S1B Fig ) and there was no difference in spontaneous virus replication between MAVS WT and KO BCBL-1 cells under high-density culture conditions ( S2B Fig ) . In high-density culture , cells may readily undergo cell death due to deprivation of nutrients or the presence of toxic metabolites . Autophagy is a cytoprotective response to cellular stressors including nutrient and energy starvation [28] , but it has also been implicated in a form of caspase-independent cell death , termed autophagic cell death [29] under certain conditions such as starvation [30 , 31] . Thus , we hypothesized that HHV-8-infected MAVS KO cells cultured at high density may be susceptible to autophagic cell death , thus leading to retarded growth ( Fig 1A ) . To examine this notion , the cells at low density were treated with rapamycin to induce autophagy . Indeed , rapamycin induced an increase in the percentage of annexin V-positive MAVS KO BCBL-1 cells to a similar degree as the high-density culture ( Fig 1D; compare with Fig 1C ) . This result suggests that MAVS may be involved in the protection of HHV-8-infected cells from autophagic cell death . In addition , MAVS KO BCBL-1 cells rapidly lost their viability when incubated in Earle’s Balanced Salt Solution ( EBSS ) , an autophagy inducer , for 6 h ( S2C Fig ) . Intriguingly , the MAVS KO cells were also susceptible to death induced by TNF-related apoptosis-inducing ligand ( TRAIL ) , but not by other cell death-inducing drugs including staurosporine and mitochondria-damaging drugs ( S2C Fig ) . These results indicate that MAVS may confer selective protection to HHV-8-infected cells against death induced by autophagy and death receptors . Upon autophagy induction , LC3-I , a cytosolic form of LC3 ( one of mammalian ATG8 orthologs ) , is cleaved , lipidated , and inserted as LC3-II into autophagosome membranes , and the selective autophagy receptor p62 ( also termed SQSTM1 ) is degraded . Thus , autophagy can be assessed by immunoblotting for the faster migrating LC3-II and p62 levels [29] . Intriguingly , our data showed that high-density culture led to a decrease in LC3B-II level in the WT and MAVS KO cells ( Fig 2A ) . This might reflect an increase in autophagic flux and lysosomal degradation of LC3B-II in a MAVS-independent manner . Rapamycin promoted a pronounced increase in the relative levels of LC3B-II under both low- and high-density cultures , but there was no difference between the isogenic cell lines ( Fig 2A ) . Nonetheless , p62 degradation was highly induced by high-density culture and/or rapamycin in MAVS KO BCBL-1 cells ( Fig 2A ) . By contrast , this did not occur in MAVS KO BJAB and EBV-infected AKATA cells ( S3 Fig ) . These results suggest that MAVS may be involved in the inhibition of autophagy and associated cell death induced by HHV-8 infection . HHV-8 vFLIP was proposed to function as a key regulator of autophagy and associated cell death [32]; therefore , we examined the expression level of vFLIP . The result showed that the level of vFLIP protein , but not mRNA , was reduced by high-density culture and/or rapamycin in MAVS KO BCBL-1 cells in an analogous manner to p62 ( Fig 2A and 2B ) . Thus , autophagy and cell death induced in MAVS KO BCBL-1 cells may be related to reduced levels of vFLIP . Levels of vFLIP were increased in the presence of autophagy inhibitors , in particular bafilomycin A1 ( Baf A1 ) , as well as a proteasome inhibitor MG132 , to a lesser extent , in vFLIP-transfected 293T and BCBL-1 cells under high-density culture ( Fig 2C and 2D , respectively ) . Importantly , the inhibitors restored vFLIP expression in MAVS KO BCBL-1 cells ( Fig 2D ) , indicating that MAVS deficiency-induced vFLIP degradation is mediated by autophagy and the ubiquitin-proteasome pathway . Of note , the effect of MG132 was relatively weaker than that of Baf A1 , indicating that autophagy is the main mechanism of vFLIP degradation with a minor contribution from the proteasome pathway . These results suggest that MAVS may be involved in the protection of vFLIP from degradation mediated by autophagy and proteasomes . We next examined if overexpression of MAVS has an influence on vFLIP expression in 293T cells . The results indicated that vFLIP , expressed alone , was barely detected , but its level was significantly increased when MAVS was co-expressed ( Fig 3A , compare lanes 1 and 2 ) . To facilitate the specific detection of vFLIP in co-immunoprecipitation ( co-IP ) and immunostaining assays , we generated vFLIP vectors for expression of vFLIP fused to various N-terminally placed epitope tags . Expression of the vFLIP proteins was tested in the absence and presence of MAVS . Surprisingly , basal expression levels of the tagged vFLIP proteins , except for V5-tagged vFLIP ( V5-vFLIP ) ( Fig 3A , lanes 3 and 4 ) , were elevated relative to native vFLIP and not significantly increased by MAVS co-expression ( S4 Fig ) . Therefore , we used V5-vFLIP for subsequent experiments . We next examined the effect of MAVS on expression of other viral and cellular FLIPs including rhesus monkey rhadinovirus ( RRV ) vFLIP , poxvirus molluscum contagiosum MC159 , and cellular FLIP long form ( cFLIP-L ) and short form ( cFLIP-S ) . Intriguingly , MAVS increased the expression of the viral FLIPs but not the cellular FLIPs ( Fig 3A , lanes 5 to 12 ) , suggesting that MAVS selectively regulates expression of viral FLIP proteins . We next examined whether MAVS overexpression can inhibit autophagy-induced vFLIP degradation . As expected , EBSS-induced autophagy highly promoted V5-vFLIP degradation in WT and MAVS KO 293T cells [26] ( Fig 3B , compare lanes 1 and 5 with lanes 2 and 6 , respectively ) . However , the EBSS-induced vFLIP degradation was significantly blocked by MAVS overexpression in WT and MAVS KO cells ( Fig 3B , compare lanes 3 and 7 with lanes 4 and 8 , respectively ) . While the mRNA levels of transfected V5-vFLIP were comparable between the isogenic cell lines , MAVS overexpression slightly increased the mRNA levels to ~1 . 2 fold ( Fig 3C ) . Thus , we performed a cycloheximide ( CHX ) chase experiment to verify that MAVS increases the expression level of V5-vFLIP protein by stabilization rather than by enhanced mRNA translation or abundance . The result showed that V5-vFLIP protein was highly unstable in MAVS-deficient cells but stabilized by endogenous MAVS and even more so by transfected MAVS ( Fig 3D ) . Using the HSP90 inhibitor PU-H71 , it was recently demonstrated that this heat-shock protein plays a crucial role in stabilization of vFLIP [33] . PU-H71-induced vFLIP degradation indeed occurred in 293T cells but was inhibited by MAVS ( Fig 3E ) . However , MAVS could not block PU-H71-induced degradation of NEMO/IKKγ , another client of HSP90 ( Fig 3E ) . Collectively , these results suggest that MAVS selectively stabilizes vFLIP . To identify the region ( s ) of MAVS required for vFLIP stabilization , three truncated forms of MAVS lacking the caspase activation and recruitment domain ( CARD ) -like domain ( residues 10 to 77 , ΔCARD ) , the proline-rich domain ( residues 103 to 152 , ΔPD ) , or the transmembrane domain ( residues 514 to 535 , ΔTM ) were used ( Fig 4A ) as previously described [26] . Expression of both non-tagged vFLIP and V5-vFLIP were increased by full-length ( FL ) and ΔPD MAVS , but not by ΔCARD and ΔTM MAVS ( Fig 4B ) , indicating that vFLIP stabilization requires the CARD and TM regions . Since the CARD and TM regions are essential for MAVS downstream signaling [34] and TNF receptor associated factor 6 ( TRAF6 ) plays a role in regulation of protein stability [35] , we reasoned that the MAVS-TRAF signaling complex may be involved in vFLIP stabilization . MAVS contains multiple TRAF-interacting motifs ( TIMs ) for recruiting TRAF2 , TRAF3 , TRAF5 , and TRAF6 [12 , 36] ( Fig 4C ) : the PVQET ( residues 143–147 ) motif for TRAF2 and TRAF5 , the PGENSE ( residues 153–158 ) and PEENEY ( residues 455–460 ) motifs for TRAF6 , and the second TRAF6-binding motif also for TRAF3 . MAVS mutations that selectively disrupt its binding to specific TRAFs were generated and used for vFLIP stabilization assays . Single or double mutations of TRAF2/5 ( Q145N ) , TRAF6 ( E155D , E457D , or both ( 2ED ) ) , TRAF3/6 ( E457D ) binding sites had no or little effect on vFLIP stabilization , but mutations of all the TIMs ( QN2ED ) abolished the ability of MAVS to stabilize vFLIP ( Fig 4D ) . The CARD region is essential for MAVS polymerization , which is required for recruitment of TRAFs [12] . A polymerization-null CARD mutation ( R64/65A ) abolished the ability of MAVS to stabilize vFLIP ( Fig 4D ) . In addition , reconstitution of WT but not the mutants ( 2ED and QN2ED ) of MAVSRg1 , which is a codon-degenerated version of MAVS that is resistant to the guide RNA1 , into BCBL-1 MAVS KO 1A4 led to stabilization of endogenous vFLIP ( Fig 4E ) . These results suggest that TRAFs , in a redundant manner , are critically involved in MAVS-induced vFLIP stabilization . We next examined the effect of each individual TRAF on vFLIP expression in 293T cells . The results showed that TRAF3 , TRAF5 , and TRAF6 , but not TRAF2 , increased vFLIP expression ( Fig 5A ) . TRAF proteins are ubiquitin ligases containing a highly conserved N-terminal RING domain forming the catalytic site; TRAF1 is the exception in that it does not contain this conserved domain . To further examine whether the catalytic activity of the TRAF proteins is required for vFLIP stabilization , we used TRAF3 ( C68A/H70A ) and TRAF6 ( C70A ) RING domain mutants that lack E3 ligase activity [37 , 38] . Indeed , the TRAF mutants completely lost the ability to enhance vFLIP expression ( Fig 5B ) , suggesting that vFLIP may be posttranslationally modified by TRAF-mediated K63-linked polyubiquitination on a specific lysine residue ( s ) for stabilization . To next examine if MAVS mediates TRAF-induced vFLIP expression , WT and MAVS KO 293T cells were co-transfected with V5-vFLIP and TRAF6 expression vectors . TRAF6 promoted the modification of V5-vFLIP as observed by the slower migrating bands , possibly K63-linked polyubiquitinated products , but this effect was significantly diminished in the MAVS KO cells ( Fig 5C ) . Transfection efficiency was comparable between the isogenic cell lines as evidenced by the expression of Flag-TRAF6 . In addition , RT-qPCR analysis showed that there was no difference in the mRNA levels of V5-vFLIP between WT and MAVS KO cells co-transfected with TRAF6 , while TRAF6 overexpression modestly increased the mRNA levels of V5-vFLIP ( S5 Fig ) . This increase in the level of V5-vFLIP mRNA may contribute to induction of V5-vFLIP protein in the MAVS KO cells despite the apparent absence of post-translational modifications . Taken together , these results indicate that MAVS promotes TRAF-induced post-translational modification of vFLIP for stabilization . To better understand the molecular mechanism by which MAVS promotes TRAF6-induced vFLIP stabilization , we examined if MAVS mediates an interaction of vFLIP and TRAF6 . Indeed , our co-IP assay showed that V5-vFLIP was co-precipitated with Flag-TRAF6 in the presence of MAVS and the interaction was diminished in MAVS KO cells ( Fig 5C ) . Importantly , TRAF6 interaction with the modified V5-vFLIP proteins was readily apparent in control cells but not in MAVS KO cells ( Fig 5C ) , suggesting that MAVS mediates TRAF6-induced polyubiquitination of vFLIP . It has been reported that vFLIP contains a TRAF-interacting motif ( TIM ) at the N-terminal region of the death effector domain 2 ( DED2 ) ( Fig 5D ) and binds preferentially to TRAF2 via the motif [39] . Thus , it is conceivable that direct binding of TRAF proteins also contributes to vFLIP stabilization and modification . To examine this possibility , we generated a variant of vFLIP ( TIMX ) in which the consensus residues , proline 93 ( P93 ) and glutamine 95 ( Q95 ) , of the TIM were substituted with leucine ( L ) and arginine ( R ) , respectively ( Fig 5D ) . Then , we examined if vFLIP TIMX indeed loses the ability to bind to TRAF proteins using a co-IP assay . Contrary to our expectation , however , the result showed that the TIMX variant could bind to TRAF3 and TRAF5 , and to a lesser extent to TRAF2 and TRAF6 ( Fig 5E ) . In addition , MAVS-induced vFLIP stabilization was not significantly affected by the mutation ( Fig 5F ) . These results suggest that vFLIP contains other unknown TIM motif ( s ) and/or binds indirectly to the TRAF proteins via an adaptor such as MAVS ( Fig 5C ) . Taken together , our results suggest that TRAFs promote vFLIP stabilization via MAVS rather than by direct binding to vFLIP . However , the precise molecular mechanism by which MAVS utilizes TRAF proteins for vFLIP stabilization remains to be determined . vFLIP contains three lysine residues at positions 13 , 46 , and 118 . To test the involvement of these lysines in MAVS-mediated vFLIP stabilization , we generated single or double lysine mutants of vFLIP by substituting lysine ( K ) for arginine ( R ) ( Fig 6A ) : R13 ( K13R ) , R46 ( K46R ) , R118 ( K118R ) , K13-only ( K46R/K118R ) , K46-only ( K13R/K118R ) , and K118-only ( K13R/K46R ) . We first compared the expression levels of vFLIP WT and lysine mutants in control and MAVS KO 293T cells . Basal expression of vFLIP was significantly reduced in the MAVS KO cells ( Fig 6B , compare lanes 1 and 8 ) . K118-only vFLIP exhibited the highest expression ( Fig 6B , lane 7 ) , but R118 vFLIP showed the lowest expression in control 293T cells ( Fig 6B , lane 4 ) , suggesting that K118 may be centrally involved in vFLIP stabilization . Nonetheless , K118-only vFLIP was still readily detected in the MAVS-deficient cells ( Fig 6B; compare lanes 8 and 14 ) , suggesting the possibility that either ( 1 ) there is an alternative MAVS-independent pathway for vFLIP stabilization via K118; or ( 2 ) the other lysines , K13 and K46 , mediate proteasomal degradation of vFLIP via K48-linked polyubiquitination , thus their mutation may result in stabilization of K118-only vFLIP independently of MAVS . Indeed , R13 and R46 vFLIP proteins were more highly expressed compared to vFLIP in the absence or presence of MAVS ( Fig 6B; compare lanes 2 and 3 with lane 1 and lanes 9 and 10 with lane 8 ) . We next performed ubiquitination assays to examine how MAVS modulates K48- and K63-linked polyubiquitination of vFLIP . MG132 was included in the assays to prevent vFLIP degradation . In line with our hypothesis , MAVS could strongly induce K63-linked polyubiquitination of vFLIP at K118 , but not at K13 and K46 residues ( Fig 6C ) . Compared to K118-only vFLIP , vFLIP exhibited less K63-linked polyubiquitination , implying that K13 and K46 might be involved in the repression of K63-linked polyubiquitination at K118 . Intriguingly , K48-linked polyubiquitinated vFLIP bands above ~150 kDa disappeared when MAVS was overexpressed; instead , the area was replaced by K63-linked polyubiquitinated vFLIP proteins ( Fig 6C , compare lanes 1 and 2 ) . Overall , these results suggest that MAVS can stabilize vFLIP by promoting K63-linked polyubiquitination , mainly at K118 . Selective regulation of vFLIP stability by MAVS is likely to be mediated by their physical interaction . We first performed a co-IP assay to examine intracellular interaction between endogenous vFLIP and MAVS proteins in BCBL-1 cells . Indeed , vFLIP was detected in the MAVS-IP complex but not in normal immunoglobulin ( nIgG ) -IP ( Fig 7A ) . To next identify the region of MAVS required for vFLIP binding , we co-transfected 293T with full-length MAVS and the three deletion variants used in Fig 4B together with vFLIP . Co-IP analysis showed that vFLIP interacted with full-length , ΔCARD , ΔPD MAVS , but not with ΔTM MAVS ( Fig 7B ) . Thus , the interaction with vFLIP specifically required the TM domain , but not the CARD and PD regions , of MAVS . However , a pull-down assay revealed that purified GST-vFLIP , but not GST or GST-cFLIP-S , still bound to the purified recombinant MAVS ( residues 1–513 ) lacking the TM domain ( Fig 7C ) . GST-TRAF6 was used as a positive control for MAVS binding . These results suggest that vFLIP may bind directly to a region other than the CARD , PD , and TM regions of a membrane-bound form of MAVS . MAVS is known to localize on the surfaces of mitochondria and peroxisomes [16 , 18]; ~80% of MAVS localizes to mitochondria and 3–20% to peroxisomes . On the other hand , mitochondrial or peroxisomal localization of vFLIP has not been reported . To test if vFLIP binds to mitochondrial or peroxisomal MAVS , we performed a co-IP assay using MAVS KO 293T cells co-transfected with V5-vFLIP and MAVS , mitochondria-targeting MAVS ( MAVS-Mito ) , or peroxisome-targeting MAVS ( MAVS-Pex ) ( Fig 7D ) , which were described in a previous study [16] . Interestingly , MAVS-Pex as well as MAVS were readily detected in the V5-vFLIP-IP complex , but MAVS-Mito was barely detected ( Fig 7E ) . Although the failure to detect a strong interaction of vFLIP with MAVS-Mito may be due in part to a lower expression of vFLIP ( Fig 7E ) , our results raise the intriguing possibility that vFLIP interacts with and is stabilized preferentially by peroxisome-localized MAVS . The low level of endogenous vFLIP detected in the co-IP assay of Fig 7A may be due to the relatively low amount of peroxisomal MAVS . To further test this possibility , we reconstituted MAVS KO BCBL-1 cells with MAVSRg1-Pex and performed a co-IP experiment . As expected , the result indicated that more vFLIP was detected in MAVSRg1-Pex-IP than MAVSRg1-IP ( Fig 7F ) . Furthermore , an immunostaining assay showed that vFLIP was readily detected in MAVS-Pex-reconstituted cells and co-localized with MAVS-Pex , but not with MAVS-Mito in reconstituted cells ( Fig 7G ) . In line with these findings , quantitative immunoblotting analysis indicated that MAVS-Pex increased V5-vFLIP expression by more than 4-fold compared to MAVS , whereas MAVS-Mito diminished V5-vFLIP expression to 20% of WT MAVS levels ( Fig 7H ) . As loading controls , peroxisomal and mitochondrial markers PMP70 and TOM20 exhibited consistent expression . Furthermore , MAVS-Pex could protect vFLIP from autophagy-induced degradation ( Fig 7I ) . Since TRAF6 is implicated in MAVS-induced vFLIP stabilization ( Fig 5 ) , we examined if TRAF6 can localize to peroxisomes using immunostaining . The results showed that TRAF6 localized in part to peroxisomes in control 293T cells , and less so in MAVS KO cells ( S6 Fig ) . However , when co-transfected with MAVS-Pex , TRAF6 was highly detected in peroxisomes together with MAVS-Pex ( S6 Fig ) , indicating that peroxisomal targeting of TRAF6 may be dependent on peroxisome-localized MAVS . Peroxisome-specific stabilization of vFLIP by MAVS might be explained by selective targeting of vFLIP to peroxisomes . Our inspection of the vFLIP primary structure using the Blocks-Based Tools [40] revealed a region homologous to a putative PEX19 binding site ( PEX19BS ) of yeast Pex8 ( Fig 8A ) . PEX19 is a peroxisomal biogenesis factor that delivers cargo proteins to the peroxisomal membrane by binding to the PEX19BS motif , also termed the membrane peroxisomal targeting signal ( mPTS ) sequence , of cargo [41] . To determine whether the predicted vFLIP mPTS motif can bind to PEX19 , we performed a pull-down assay using GST-fused WT and mutant ( mPTSX ) vFLIPs , the latter containing alanine substitutions of conserved hydrophobic residues I39 , L42 , and L45 ( Fig 8B ) . Indeed , GST-vFLIP , but not GST or the vFLIP mutant , bound to HA-PEX19 derived from transfected 293T cells ( Fig 8B ) . Next , we examined the effect of MAVS on the expression of vFLIP mPTSX . MAVS KO 293T cells were transfected with V5-vFLIP WT and mPTSX along with different amounts of MAVS-Pex vector . The result showed that MAVS-Pex stabilization of mPTSX was diminished relative to its effect on vFLIP ( Fig 8C ) . Consistent with this result , vFLIP mPTSX binding to endogenous MAVS was significantly diminished ( Fig 8D ) . To further examine the effect of PEX19 on vFLIP expression , we generated two PEX19 KO 293A cell lines , B11 and C8 , using CRISPR/Cas9-mediated gene editing . Indeed , basal and MAVS-Pex-induced expression of vFLIP were significantly reduced in the PEX19-deficient cells even though MAVS-Pex expression was increased up to 50% in PEX19-deficient cells compared to its expression in the WT cells ( Fig 8E ) . The expression of endogenous MAVS was not significantly affected by PEX19 deficiency . The transfection efficiency was comparable among the isogenic cell lines as evidenced by the expression of co-transfected GST ( Fig 8E ) . Intriguingly , MAVS-Pex still marginally increased the expression of V5-vFLIP in PEX19 KO cells ( Fig 8E ) . This might be attributed to incomplete knockout of peroxisomes in PEX19 KO cells . Of note , our immunostaining results revealed that PMP70 , a cargo molecule of PEX19 , was largely , but not completely , depleted in PEX19 KO cells ( S7 Fig ) . Consistent with this , V5-vFLIP WT and mPTSX were not readily detected in PEX19 KO cells , even in the presence of MAVS-Pex; however , V5-vFLIP , but not mPTSX , was readily detected in peroxisomes of control cells when co-transfected with MAVS-Pex ( S7 Fig ) . Taken together , these data indicate , for the first time , that vFLIP can be targeted to peroxisomes where it is stabilized by MAVS . One of the best characterized functions of vFLIP is its activation of NF-κB . To determine if peroxisome-localized vFLIP can activate NF-κB , we generated a chimeric vFLIP-Pex construct in which the human PEX13 PTS sequences ( residues 145–233 ) were fused to the C-terminus of vFLIP ( Fig 8F ) . We first examined peroxisomal localization of the vFLIP protein in 293A cells by immunostaining; V5-vFLIP-Pex was strongly co-localized with PMP70 while co-localization or juxtaposition of V5-vFLIP and PMP70 was readily detected only in the presence of MG132 ( Fig 8F ) . Using a luciferase-based reporter assay for NF-κB signaling , we determined that V5-vFLIP-Pex can activate NF-κB in 293T cells ( Fig 8G ) . However , this result does not necessarily indicate that peroxisomes are a unique site for vFLIP activation of NF-κB [42]; indeed , equivalent levels of NF-κB activation by V5-vFLIP and V5-vFLIP-Pex , despite higher expression of the latter , may indicate that this is not the case ( Fig 8G ) . To further demonstrate the ability of peroxisomal vFLIP to activate NF-κB , we examined if vFLIP-Pex interacts with NEMO/IKKγ , which is essential for vFLIP-induced NF-κB activation [43 , 44] , using a co-IP assay . Indeed , vFLIP-Pex interacted with NEMO/IKKγ , but to a lesser extent than vFLIP ( Fig 8H ) . Thus , our results demonstrate a novel and essential role of peroxisomes as platforms for vFLIP stabilization and that peroxisome-localized vFLIP can activate NF-κB signaling . To develop a reagent to specifically disrupt the vFLIP-MAVS interaction , which could potentially provide a basis for therapeutic targeting of latently infected cells , we first wanted to identify the vFLIP region essential for MAVS binding . Co-IP assays revealed that GST-vFLIP full-length , DED1 ( residues 1–90 ) , DED2 ( residues 85–188 ) , but not GST , were co-immunoprecipitated with MAVS ( Fig 9A ) , indicating that vFLIP contains multiple MAVS binding sites . vFLIP was predicted to contain eleven α-helices ( H ) : H1 to H6 in DED1 and H1 to H5 in DED2 [45 , 46] ( Fig 9B ) . To further define MAVS binding sites , eleven GST-vFLIP α-helices were generated and co-transfected with Flag-MAVS into 293T cells . Co-IP assays showed that GST-DED1-H1 ( 1H1 ) , GST-DED1-H6 ( 1H6 ) , and GST-DED2-H1 ( 2H1 ) appeared to bind to MAVS ( Fig 9B ) . Accordingly , MAVS-induced vFLIP stabilization was significantly inhibited by co-expression of the GST-1H1 , GST-1H6 , and GST-2H1 ( Fig 9C ) . Moreover , we tested the effects of a cell-penetrating version of the vFLIP helices ( TAT-1H1 , TAT-1H6 , and TAT-2H1; ( S8A Fig ) ) on MAVS-induced vFLIP stabilization in 293T cells . TAT-2H1 potently inhibited ( up to 97% ) vFLIP expression induced by MAVS while TAT-1H1 and 1H6 inhibited the expression up to ~60% compared to the TAT peptide ( S8B Fig ) . Importantly , TAT-2H1 abolished detectable endogenous vFLIP expression in BCBL-1 cells cultured at low density ( Fig 9D ) and increased cell death by more than 50% ( early and late apoptotic cells ) of WT and MAVS KO BCBL-1 cells ( Fig 9E ) . Surprisingly , there was no significant difference of TAT-2H1-induced cell death between WT and MAVS KO cells: WT ( clone C3 ) , 54 . 55%; WT ( clone C6 ) 53 . 05%; KO ( clone 1A4 ) , 56 . 02%; KO ( clone 3B11 ) , 58 . 13% ( Fig 9E ) . Considering that vFLIP degradation was highly induced by the 2H1 peptide ( Fig 9D ) compared to that of MAVS-deficient BCBL-1 cells cultured at low density ( Fig 2A ) , the 2H1 region-mediated vFLIP stabilization is likely to be achieved by some other mechanism ( s ) in addition to MAVS binding . Conversely , MAVS may be essential but not sufficient for vFLIP stabilization . On the other hand , cell viability assays revealed that TAT-2H1 promoted the death of other HHV-8-infected PEL cells including BC-2 and BCP-1 , but not BJAB ( HHV-8– ) and AKATA ( HHV-8–/EBV+ ) cells ( Fig 9F ) . TAT-2H1 had no effect on other MAVS functions including IFN-β induction , and NF-κB and JNK activation ( S9 Fig ) . Taken together , these results suggest that vFLIP peptide-mediated specific modulation of vFLIP protein stability may represent a useful therapeutic strategy for treatment of HHV-8 diseases . We next reconstituted MAVS KO BCBL-1 ( 1A4 ) cells with gRNA-resistant MAVSRg1 and K118-only vFLIP ( a stable version of vFLIP ) to examine whether vFLIP is a critical factor for proliferation of BCBL-1 cells . As expected , MAVS reconstitution restored the expression of endogenous vFLIP ( Fig 10A ) . Indeed , reconstitution of MAVS and K118-only vFLIP promoted the proliferation of MAVS KO BCBL-1 cells ( Fig 10B ) . K118-only vFLIP was also functional in an NF-κB reporter assay ( S10 Fig ) . To further delineate the functional significance of MAVS in vFLIP-mediated cell proliferation , we generated a recombinant BAC16 HHV-8 genome ( BAC16_ΔvFLIP ) , which is defective in vFLIP expression , using Red-mediated recombination ( see the Materials and Methods section for details ) , verified by DNA sequencing and AvrII digestion ( Fig 10C and 10D ) , and infected WT and MAVS KO BJAB cells with BAC16 and BAC16_ΔvFLIP viruses that were produced from stably transfected and reactivated iSLK cells . The results demonstrated clearly , similar to the 2H1 peptide-induced cell death ( Fig 9E ) , that genetic ablation of vFLIP expression resulted in reduced proliferation , to the same extent , of both WT and MAVS KO BJAB cells ( Fig 10E ) . Therefore , death and/or impaired proliferation caused by MAVS deficiency in HHV-8-infected cells can be attributed largely to reduced vFLIP expression . In summary , our results suggest that MAVS supports the survival and proliferation of HHV-8-infected PEL cells via vFLIP stabilization .
MAVS is known to function as a key adaptor that connects the RLR recognition of virus infection to innate immune responses , thereby limiting virus replication and dissemination in the host . In this study , we have found that MAVS plays a novel and crucial role in promoting the survival of latently HHV-8-infected cells by stabilizing the expression of the latent protein vFLIP on peroxisomes , thereby contributing to persistent HHV-8 infection ( latency ) . vFLIP is known to be involved in the inhibition of HHV-8 lytic reactivation [47 , 48] . Thus , MAVS-mediated vFLIP stabilization is likely also to contribute to suppression of HHV-8 lytic replication; however , TAT-2H1-mediated destabilization of vFLIP could not promote lytic gene expression and productive replication in BCBL-1 cells ( S11 Fig ) . Therefore , our results suggest that MAVS-mediated vFLIP stabilization contributes mainly to the survival of HHV-8 latently infected cells . A recent Integrated Systems Biology analysis revealed that peroxisome biogenesis is enhanced in HHV-8 latently-infected endothelial cells along with increased expression of several peroxisomal proteins including PEX19 [49] , suggesting a contribution of peroxisomes to the successful maintenance of HHV-8 latency . Here we demonstrated that vFLIP can be localized to peroxisomes in a PEX19-dependent manner where it is stabilized by MAVS . Furthermore , vFLIP expression was significantly reduced in PEX19-deficient cells , which are depleted of peroxisomal structures . Importantly , peroxisome-targeted vFLIP ( vFLIP-Pex ) retained NEMO/IKKγ binding activity and NF-κB activation . Thus , it is likely that an increase in PEX19 expression and peroxisome biogenesis in HHV-8 latently-infected cells contributes to MAVS-mediated stabilization of vFLIP and subsequent maintenance of latency . Our data indicate that vFLIP targeting to peroxisomes appears to be mediated by PEX19 . There are different models ascribing PEX19-dependent trafficking of the peroxisomal membrane proteins ( PMPs ) during peroxisome biogenesis . In the peroxisomal growth and division model , PEX19 serves as a chaperone that delivers nascent mPTS-containing PMPs from free cytosolic ribosomes to the peroxisomal membrane [41] . In the de novo peroxisomal biogenesis model , PEX19 shuttles the PMPs together with the membrane import receptors PEX3 and PEX16 through the generation of pre-peroxisomal vesicles that originate from the endoplasmic reticulum ( ER ) and mitochondria [50 , 51] . Our imaging studies detected vFLIP with a distinct speckled pattern in the cytoplasm that in part co-localized with and was juxtaposed to the peroxisomes , implying that vFLIP may be targeted to peroxisomes via the pre-peroxisomal vesicles rather than direct delivery from the cytosol . However , there is no evidence that vFLIP is localized on the membranes of the ER or mitochondria and contains a secondary structure required for membrane targeting . Using software tools such as TMbase and the Dense Alignment Surface method , we predicted a putative transmembrane helix of vFLIP encompassing residues 20 to 40 , which is positioned adjacent to the mPTS of vFLIP; however , it needs to be verified experimentally that the predicted region is indeed required for membrane targeting . In fact , cFLIP-L was reported to be localized at the ER and mitochondria-associated ER membranes [52] , despite the lack of a transmembrane domain . Thus , we speculate that vFLIP might be localized to the ER or mitochondria via its putative transmembrane domain or protein-protein interactions and then delivered to peroxisomes through PEX19-dependent pre-peroxisomal vesicles . vFLIP is a critical viral oncoprotein that is essential for the survival of HHV-8-associated tumors . Thus , there has been considerable interest in manipulating the function and stability of vFLIP for the development of drugs to treat HHV-8-related tumors . For example , two independent groups reported that the purine scaffold HSP90 inhibitors , PU-H71 and BIIB021 , suppressed tumor growth in mice xenografted with HHV-8+ PEL cells , potentially by promoting vFLIP degradation and inhibiting vFLIP-mediated NF-κB activation [33 , 53] . Another group showed that vFLIP-derived peptides that inhibit interaction of vFLIP and autophagy-related protein 3 ( ATG3 ) suppress tumor formation in NOD/SCID mice xenografted with PEL cells [32] . Nonetheless , targeted ablation of vFLIP protein may represent a more efficacious approach in the treatment of HHV-8-related malignancies . We found that the vFLIP-derived peptide 2H1 , that inhibits vFLIP interaction with and stabilization by MAVS , could abolish detectable expression of vFLIP in HHV-8-infected PEL cells and selectively induce the death of HHV-8+ PEL cells , indicating the potential of this approach to treat HHV-8 diseases . In summary , the data presented here identify molecular mechanisms and functional significance of MAVS-mediated vFLIP stabilization on peroxisomes . To our knowledge , this is the first report of MAVS and peroxisome function in the regulation of viral oncogenic protein stability and therefore our data have revealed a novel mechanism underlying the regulation of a viral protein essential for HHV-8 latency and associated pathogenesis .
BCBL-1 TRE-RTA ( a gift from Dr . Jae U . Jung ) , BCBL-1 , BC-2 and BCP-1 ( ATCC ) , and AKATA and BJAB ( a gift from Dr . Richard Ambinder ) cells were cultured in RPMI 1640 supplemented with 15% fetal bovine serum ( FBS ) , antibiotics including streptomycin and penicillin , and plasmocin prophylactic ( Invivogen ) . 293 , 293T , 293A ( Thermo Fisher Scientific ) , and iSLK and iSLK-BAC16 ( a gift from Dr . Jae U . Jung ) cells were cultured in DMEM supplemented with 10% FBS and antibiotics . Transient and stable transfections with plasmids were performed using GenJet version II ( SignaGen Laboratories ) following the manufacturer’s instruction . For generation of MAVS-deficient cells , BCBL-1 TRE/RTA , AKATA , and BJAB cells were lentivirally transduced with MAVS gRNAs in the presence of 5 μg/ml polybrene overnight and puromycin-resistant cells were selected , transferred individually into a 96-well plate , and expanded in the presence of puromycin for an additional month . MAVS deficiency in each clone was verified by immunoblotting using anti-MAVS antibody . For PEX19 KO 293A cells , cells were stably transfected with the lentiCRISPR V2 vector encoding PEX19 gRNA using GenJet reagent . After puromycin selection of single clones , PEX19 deficiency was verified by immunoblotting using anti-PEX19 antibody . BJAB cells were infected by BAC16 viruses , which were isolated from iSLK-BAC16 and iSLK-BAC16_ΔvFLIP cells reactivated by treatment with 1 μg/ml doxycycline and 1 mM sodium butyrate for 3 days , by spinoculation at 800 x g for 30 min at room temperature and then cultured in the presence of hygromycin ( 500 μg/ml ) for 3 days . Viable BAC16 virus-infected BJAB cells were isolated using Histopaque-1077 medium . All polymerase chain reaction ( PCR ) amplification and site-directed mutagenesis including point and deletion mutations were performed using Platinum Pfx DNA polymerase ( Thermo Fisher Scientific ) . Subcloning of an open reading frame ( ORF ) and its derivatives into expression vectors including pICE ( Addgene ) was performed using appropriate restriction enzyme sites ( S2 Table ) . For construction of vFLIP-Pex , the DNA fragment encompassing PEX13 residues 145–233 was amplified from MAVS-Pex and fused to the C-terminal end of vFLIP by overlap extension PCR , and the chimeric construct was cloned into pICE_V5 vector ( S2 Table ) . The small guide RNAs ( gRNAs ) with target sequences specific for MAVS and PEX19 ( S1 Table ) were cloned into lentiCRISPR V2-Puro vector ( Addgene ) . The MAVS ORF ( MAVSRg1 ) resistant to the gRNA1 was generated by site-directed mutagenesis using degenerated primers ( S1 Table ) . The BAC16 genome was edited using a two-step seamless Red recombination in the context of E . coli strain GS1783 ( a kind gift from Greg Smith ) as previously described [54] . Briefly , PCR amplification was performed to generate a linear DNA fragment containing a kanamycin resistance expression cassette , an I-SceI restriction enzyme site , and flanking sequences derived from HHV-8 genomic DNA , each of which includes a 43-bp copy of a duplication . The mutated codon of the translation initiation codon of vFLIP ( ATG > TAG ) was positioned in the middle of the duplication . This fragment was purified and then electroporated into GS1783 cells harboring BAC16 and transiently expressing gam , bet , and exo , which are expressed in a temperature-inducible manner from the lambda Red operon in the GS1783 chromosome . The integrated KanR/I-SceI cassette was cleaved by I-SceI enzyme that was inducibly expressed by treatment with 1% arabinose , resulting in a transiently linearized BAC16 . A second Red-mediated recombination between the duplicated sequences results in recircularization of the BAC DNA and seamless loss of the KanR/I-SceI cassette . Kanamycin-sensitive colonies were selected via replica plating . The BAC DNAs were purified using the NucleoBond BAC 100 kit ( Clontech ) . The recombination area was amplified by PCR and the mutation was verified by DNA sequencing of the PCR amplicon . Gross genomic integrity was verified using digestion with AvrII restriction enzyme , which additionally recognizes the mutated codon of vFLIP , and agarose gel analysis of digestion profiles . Antibodies used in the immunological assays including immunoblotting , immunoprecipitation , and immunostaining are listed in S3 Table . For the preparation of total cell extracts , cells were resuspended in RIPA buffer ( 50 mM Tris [pH 7 . 4] , 150 mM NaCl , 1% Igepal CA-630 , and 0 . 25% deoxycholate ) containing protease inhibitor cocktail and protein phosphatase inhibitors including 10 mM NaF and 5 mM Na3VO4 and sonicated using Bioruptor ( Diagenode , Denville , NJ ) for 5 min in ice water at a high-power setting ( 320 W ) . For immunoblotting , total cell extracts were separated by SDS-PAGE , transferred to nitrocellulose or PVDF membranes , and immunoblotted with appropriate antibodies diluted in SuperBlock ( phosphate-buffered saline [PBS] ) blocking buffer ( Thermo Fisher Scientific ) . The immunoreactive bands were detected by Clarity Western ECL reagents ( Bio-Rad ) on an ECL film . For immunoprecipitation ( IP ) , total cell extracts were incubated with specific primary antibody at 4°C overnight and incubated with protein G-agarose beads ( Cell Signaling Technology ) for an additional 3 h . Immunoprecipitates were washed with RIPA buffer , followed by elution of bound proteins with 1 . 5× SDS sample buffer or 3× Flag peptide ( Sigma , St . Louis , MO ) . For the IP of Flag- and V5-tagged proteins , anti-DYKDDDDK tag ( L5 ) affinity gel ( BioLegend ) and anti-V5-agarose ( Sigma ) were used , respectively . To avoid detection of IgG used in IP , Clean-Blot IP detection reagent ( Thermo Fisher Scientific ) was used . For ubiquitination assays , an extra wash was performed using RIPA buffer supplemented with 1 M urea before elution . For immunostaining , cells grown on a coverslip ( and transfected ) were fixed in 4% formaldehyde prepared in PBS and permeabilized in 0 . 5% Triton X-100 prepared in Dulbecco’s PBS ( DPBS ) . Following incubation with 3% bovine serum albumin in DPBS for 1 h at room temperature , coverslips were incubated with primary antibodies , washed with PBS , and then incubated with appropriate fluorescent dye-conjugated secondary antibodies . Stained cells were imaged on the Zeiss 700 confocal laser scanning microscope with a 40X oil-corrected objective and Zen software . For lentivirus production , 293T cells were co-transfected with the lentiviral transfer vector together with the packaging plasmid psPAX2 and the vesicular stomatitis virus G protein expression plasmid pVSV-G at a ratio of 5:4:1 using GenJet ( Ver . II ) transfection reagent ( SignaGen ) . Two days later , virus was collected from the medium by ultracentrifugation in an SW28 rotor at 25 , 000 rpm for 2 h at 4°C . Virus pellets were resuspended in an appropriate volume of PBS to achieve 100x concentration . The transduction unit of lentiviruses was determined in 293T cells in the presence of appropriate antibiotics . To produce infectious BAC16 viruses , iSLK-BAC16 cells were reactivated with 1 μg/ml doxycycline and 1 mM sodium butyrate for 3 days . Infectious titer of BAC16 virus in the culture supernatant was determined by spinoculation of 293A cells and 1 day later GFP-positive cells were quantified using flow cytometry . BJAB cells were infected with 1 infectious unit of BAC16 by spinoculation at 800 x g for 30 min at room temperature . For lentiviral transduction of GFP , MAVS rgRNA1 , and K118-only vFLIP ( in pDUET110 ) into BCBL-1 cells , cells were incubated with 1 transduction unit ( TU ) in the presence of 5 μg/ml polybrene for 1 day and washed in complete RPMI 1640 media , and further cultured for cell viability assays and immunoblotting assays . Recombinant GST and GST-fusion proteins were expressed in Rosetta cells and cleared lysates containing 1 μg GST and GST-fusion proteins were incubated with 20 μl bed volume of washed glutathione sepharose-4B beads for 1 h at room temperature . After washing in binding buffer , the protein-bead complexes were incubated with 1 μg purified recombinant human MAVS protein ( ProSpec , Pro-1351 ) or cell extracts containing HA-PEX19 protein at 4°C overnight , washed in binding buffer four times , and separated on SDS-PAGE and subjected to immunoblotting . 293T cells were plated in triplicate and the next day transfected with 100 ng of reporter plasmids and 400 ng of expression plasmids using GenJet Ver . II reagent . pRL-TK vector ( 5 ng ) was included in the transfection mixture to monitor the efficiency of the transfections . Cells were extracted with passive lysis buffer ( Promega ) , and Firefly and Renilla luciferase activities were determined using a Dual-Luciferase Reporter Assay kit ( Promega ) . The same cell extracts were used for immunoblotting analysis . Dead cells present prior to experimentation were removed using Histopaque-1077 ( Sigma ) or Dead Cell Removal kit ( Miltenyl Biotec ) . Freshly isolated intact cells were cultured at different cell densities ( 5x104 and 2x105 cells/ml ) or treated as indicated in the Figure Legends . Cells were then stained with FITC-annexin V and 7-amino-actinomycin D ( 7-AAD ) as described by the manufacturer ( BioLegend ) . Annexin V and 7-AAD were detected in FL1 and FL3 , respectively , using BD FACSCalibur flow cytometry . For cell viability assays , the CellTiter-Glo® Luminescence kit ( Promega ) was used . In brief , 50 μl of cell culture in triplicate was transferred to a 96-well white plate and 50 μl of the CellTiter Glo® reagents was added to the culture . After vigorous shaking at RT for 2 min , the mixtures were incubated for 30 min at room temperature and their luminescence was measured using the GloMax Microplate Luminometer ( Promega ) . Total RNAs were isolated from latent and reactivated BCBL-1 cells or transfected cells using the RNeasy mini kit ( Qiagen ) . First-strand cDNA was synthesized from 1 μg of total RNA using SuperScript II reverse transcriptase ( Invitrogen ) with random hexamers . RT-qPCR was performed using an ABI Prism 7500 system ( Applied Biosystem ) with the RT2Real-Time SYBR green/ROX master mix ( Qiagen ) . Reactions were performed in a total volume of 25 μl and contained 50 ng of reverse-transcribed RNA ( based on the initial RNA concentration ) and gene-specific primers . PCR conditions included an initial incubation step of 2 min at 50°C and an enzyme heat activation step of 10 min at 95°C , followed by 40 cycles of 15 seconds at 95°C for denaturing and 1 min at 60°C for annealing and extension . For determination of encapsidated HHV-8 genome copy number from the culture supernatant , viral DNA was purified using Quick-DNA™ Viral Kit ( Zymo Research ) following pretreatment of virus suspension with DNase I ( New England BioLabs ) at 37°C overnight . RT-qPCR was performed using ABI PRISM 7500 system as described above with the LANA primers ( S1 Table ) . The TAT and TAT-vFLIP peptides were custom-synthesized with purity of more than 95% ( Biomatik ) . Rapamycin and MG132 were purchased from Cell Signaling Technology . PU-H71 was purchased from Selleckchem . EBSS was purchased from Thermo Fisher Scientific . Recombinant TRAIL was purchased from BioLegend . Staurosporin , CCCP , 3-methyladenine , rotenone , bafilomycin A1 , chloroquine , and cycloheximide were purchased from Sigma . Band intensity of immunoblots was determined using ImageJ software . Statistical parameters including statistical analysis , statistical significance , and p value are stated in the Figure legends and Supplemental Figure legends . Statistical analyses were performed using Synergy software ( KaleidaGraph ) . For statistical comparison of cell viability assays , standard paired t-test was used . A value of p < 0 . 05 was considered significant . | HHV-8 , also known as Kaposi’s sarcoma associated herpesvirus ( KSHV ) , is the etiological agent of several malignancies occurring in individuals with severe immunosuppression , as occurs in acquired immunodeficiency syndrome and transplantation . The viral latent proteins , required for persistent infection , are believed to contribute to pathogenesis . Amongst them , vFLIP has an oncogenic potential through activation of NF-κB and suppression of cell death induced by apoptosis and autophagy . Despite its functional significance , how the stability of the protein is regulated remains elusive . Here , we found that vFLIP can localize to peroxisomes where it is stabilized by its association with the mitochondria antiviral signaling adaptor protein MAVS , which mediates K63-linked polyubiquitination of vFLIP by TNF receptor-associated factors ( TRAFs ) and renders it resistant to degradation induced by autophagy and the ubiquitin-proteasome pathway . In addition , our genetic studies using MAVS-deficient HHV-8-infected cells revealed that MAVS is essential for protection of HHV-8-infected cells from autophagic cell death via its stabilization of vFLIP . Furthermore , cell penetrating peptide-mediated disruption of the interaction between MAVS and vFLIP led to vFLIP degradation and death of HHV-8-infected tumor cells . Taken together , we have identified a novel proviral function of peroxisome-localized MAVS mediated through vFLIP stabilization and consequent promotion of HHV-8 latency . | [
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] | 2018 | Peroxisomes support human herpesvirus 8 latency by stabilizing the viral oncogenic protein vFLIP via the MAVS-TRAF complex |
Leishmaniasis is a complex parasitic disease from a taxonomic , clinical and epidemiological point of view . The role of genetic exchanges has been questioned for over twenty years and their recent experimental demonstration along with the identification of interspecific hybrids in natura has revived this debate . After arguing that genetic exchanges were exceptional and did not contribute to Leishmania evolution , it is currently proposed that interspecific exchanges could be a major driving force for rapid adaptation to new reservoirs and vectors , expansion into new parasitic cycles and adaptation to new life conditions . To assess the existence of gene flows between species during evolution we used MLSA-based ( MultiLocus Sequence Analysis ) approach to analyze 222 Leishmania strains from Africa and Eurasia to accurately represent the genetic diversity of this genus . We observed a remarkable congruence of the phylogenetic signal and identified seven genetic clusters that include mainly independent lineages which are accumulating divergences without any sign of recent interspecific recombination . From a taxonomic point of view , the strong genetic structuration of the different species does not question the current classification , except for species that cause visceral forms of leishmaniasis ( L . donovani , L . infantum and L . archibaldi ) . Although these taxa cause specific clinical forms of the disease and are maintained through different parasitic cycles , they are not clearly distinct and form a continuum , in line with the concept of species complex already suggested for this group thirty years ago . These results should have practical consequences concerning the molecular identification of parasites and the subsequent therapeutic management of the disease .
Protozoa of the Leishmania genus are part of the Trypanosomatids family , which also includes American and African Trypanosomes that cause Chagas disease and sleeping sickness . Compared to other human vector-borne protozoan parasitic diseases , such as malaria or trypanosomiases , leishmaniasis appears to be a complex parasitic disease not only from a clinical , but also from an epidemiological and taxonomic point of view . Leishmaniasis is endemic in 98 countries with 2 million cases reported each year , especially in the poorest regions . It is a polymorphic disease that can cause skin or mucosal injuries , or affect macrophages of the whole reticulo-endothelial system . This disseminated form ( visceral leishmaniasis ) is lethal if untreated [1] . Disease progression and therapeutic management depend not only on the host immunogenetic characteristics , but also on the parasitic species . Indeed , about 20 Leishmania species have been described worldwide as pathogenic for humans [2] . However , their identification can be ambiguous and controversial , thus complicating the therapeutic management of these affections . The epidemiology of leishmaniasis is also complex . Parasites can be transmitted through different zoonotic and anthropo-zoonotic cycles that involve domestic and wild mammalian reservoirs which belong to nine different orders ( rodents , canines , toothless mammals , marsupials… ) [3] . Leishmania parasites are transmitted to mammalian reservoirs by blood-sucking Diptera belonging to the genera Phlebotomus and Lutzomyia . At least 93 sandfly species are proven or probable Leishmania vectors worldwide [2] . Due to this diversity of reservoirs and potential vectors and the various host and vector-parasite specificities , many cycles probably remain to be identified . Finally , the taxonomy of the species belonging to the Leishmania genus is still debated [4] , [5] . The division of the Leishmania genus in two sub-genera ( i . e . , Leishmania and Viannia ) , which was originally based on the parasite position in the insect digestive tract , has been confirmed by all subsequent studies . Similarly , the definition of the taxonomic groups , which has been mainly based on the species isoenzymatic characteristics since the 80's , is generally in line with the epidemiological and clinical data . However , the increasing number of strains analyzed and the development of molecular techniques have led to question the identification of some groups as species ( e . g . , L . archibaldi ) . Moreover , a genetic continuum between some groups has been highlighted , leading some authors to suggest a reduction in the taxa number [5] . In addition , although the possibility of cell fusion between different Leishmania strains in the insect vector has been experimentally demonstrated , questions remain on their frequency in natura , the possibility of interspecific hybridizations , their impact on genomic evolution and the existence of gametes with meiotic reduction [6] , [7] . Although sequencing projects of entire genome of different Leishmania species will probably see the light in the coming years , only the genome of six species has been sequenced and made public so far [8]–[12] . Moreover , with the exception of a recent study on American Leishmania species , previous molecular analyses suffered from heterogeneity and usually concerned only one gene and a limited number of strains [13] , [ review in 14] . Therefore , we developed a MultiLocus Sequence Analysis ( MLSA ) -based approach to analyze systematically several genes in 222 Leishmania strains from Africa and Eurasia that should accurately represent the genetic diversity of this genus . The obtained data might help improving our knowledge on the genetic structuration and genomic evolution mechanisms of this genus . Practically , it should also facilitate the molecular identification of Leishmania strains and thus improve the therapeutic care and epidemiological understanding of this disease .
Initially , 40 coding DNA sequences ( CDS ) that correspond to housekeeping genes and are evenly spaced in the Leishmania genome were investigated . To identify only single copy genes , a systematic Blast analysis was performed against three complete Leishmania genomes deposited in GenBank ( L . infantum , L . major and L . braziliensis ) [9] . Then , the nucleotide sequences of these three species were compared to map polymorphic and conserved regions within each gene and to eliminate CDS containing indels . Finally , seven loci that are located in the central or telomeric region of six different chromosomes and are considered as independent genetic units were selected . To investigate a possible genetic linkage between loci located on the same chromosome , the loci 31 . 0280 and 31 . 2610 ( separated by 1 . 2 Mb ) on chromosome XXXI ( 2 . 7 Mb ) were chosen . The biological function of the housekeeping genes analyzed was not considered as a criterion for locus selection . In all , 222 Leishmania strains isolated in Eurasia and Africa were analyzed . These strains were selected among 6 , 000 Leishmania strains deposited in the collection of the French Reference Centre on Leishmaniasis ( Montpellier , France ) . They were mainly isolated from infected patients ( n = 176 , 79 . 3% ) , but also from mammalian reservoirs ( n = 38 , 17 . 1% ) and insect vectors ( n = 8 , 3 . 6% ) ( Table S1 ) . All samples taken from humans were anonymized . To be representative of the Leishmania genetic diversity , strains were selected on the basis of isoenzymatic and geographic criteria . For each strain , isoenzymatic data for 15 enzymatic systems were available and each zymodeme was represented by at least one strain . When a zymodeme was present in different countries , a strain from each of these countries was selected , if possible . The 222 strains originated from 43 countries and were representative of 110 zymodemes . According to the isoenzymatic-based taxonomy [15] , these strains were representative of the 10 different Leishmania species currently described in Eurasia and Africa: L . infantum ( n = 90 ) , L . major ( n = 42 ) L . donovani ( n = 29 ) , L . tropica ( n = 18 ) L . aethiopica ( n = 18 ) , L . archibaldi ( n = 9 ) , L . turanica ( n = 8 ) , L . gerbilli ( n = 4 ) , L . killicki ( n = 3 ) and L . arabica ( n = 1 ) . Although recent works have suggested the possibility of genetic exchanges in the Leishmania genus , including inter-specific hybrids , such hybrid strains were not included in our dataset [7] , [16]–[24] . In all , 1 , 554 double-strand sequences were aligned and visually checked using the CodonCode aligner software v . 4 . 0 . 4 ( CodonCode Co . , USA ) . Sequences were put in phase with the open reading frame . The locus size ranged from 486 to 810 bp and the concatenated sequence was 4 , 677 bp-long ( Table 1 ) . The 1554 sequences were deposited in GenBank under the following numbers: KC158588-KC160141 . As Leishmania is mainly considered to be a diploid organism , a special attention was paid to the heterozygous positions [10] . Chromatograms were examined visually in both directions and usually results were easily interpreted as heterozygous when two peaks in a chromatogram overlapped . No tri-allelic site was found . Only one strain ( L3538 ) was cloned to look for multi-clonal populations of parasites and the allelic profiles of the clones were identical . The MEGA version4 program was used to calculate the number of variable nucleotide sites , the nucleotide diversity ( average number of nucleotide differences per site between any two randomly selected sequences ) and the transition/transversion ratio ( R ) . Haplotype diversity ( Hd ) was calculated using the DnaSP software , version5 [25] . The possible selection pressure on these protein-coding sequences was checked using the dN/dS ratio test and the Z-test of selection based on the Nei-Gojobori method ( implemented in the DnaSP software , version5 , and in the MEGA 4 . 0 package , respectively ) [26] . Both the individual gene sequences and the concatenated sequences were analyzed . In each case , nucleotides were duplicated to avoid information loss due to ambiguous states ( e . g . , A to AA or Y to CT ) . To take into account the possible occurrence of genetic exchanges in our dataset , MLSA data were first analyzed using a network representation with the aim of replacing the bifurcating tree model with a “reticulating tree” model , in which the reticulations represent possible evolutionary processes other than lineal descent with modifications , such as horizontal gene transfers [27] . Maximum Likelihood ( ML ) trees were constructed using PhyML , version 3 . 0 [28] , [29] . The best-fitting model for nucleotide substitution was identified using the Corrected Akaike information Criterion ( AICc ) and Bayesian Information Criterion ( BIC ) implemented in JModelTest [28] , [30] . The General Time-Reversible model was chosen with a proportion of invariables sites ( I ) and gamma-distributed ( G ) rate variation across sites ( i . e . , GTR+I+G ) . For the MultiLocus Enzyme Electrophoresis ( MLEE ) data analysis , isoenzyme data were transformed to produce a binary matrix ( presence/absence of a band with a given mobility ) . Based on the hypothesis that Leishmania parasites are ‘mainly’ diploid , multiband patterns in starch gels were considered to be heterozygous and the electromorph values were duplicated . The Nei's index was calculated with the PhylTools package , version 1 . 32 , to construct a distance matrix [31] , [32] . This distance was preferred to other distance measures because it does not use the shared absence of a given allele as common characteristic [33] . Bootstrap values were collected from 1 , 000 replications of the bootstrap procedure using PylTools . The Neighbor and Consense programs of the Phylip package , version 3 . 6 , were used to obtain the final MLEE Neighbor Joining ( NJ ) tree [34] . To compare the MLSA and MLEE tree topologies , a NJ tree for the MLSA data was generated in MEGA , version4 , by using the Jukes-Cantor model and the same data set ( 222 strains , 110 zymodemes ) [26] . Split decomposition and Neighbor-Net ( NN ) analyses were performed with SplitsTree , version4 . 11 . 3 , by using p-distances and equal edge lengths [35] . The analysis was performed using both ambiguous nucleotide sites and duplicated nucleotide sites . All these phylogenetic analyses were done with 1 , 000 bootstrap ( BP ) replicates . Three species from South America belonging to the Viannia subgenus were used as an out-group for the ML phylogenetic analysis ( Table S1 ) . These distantly related strains were selected to prevent derived characters to be wrongly considered as common ancestral characters . Non-parametric Shimodaira-Hasegawa ( SH ) tests implemented in the PAUP*4 . 0b10 package were used to test the tree topology congruence [36] . For a given dataset , the SH test uses the difference in log likelihoods of competing topologies as the test statistic . The null distribution of the test statistic ( differences in log likelihoods ) was obtained by using 1 , 000 replicates of non-parametric bootstrapping of re-estimated log likelihoods . To avoid potential bias toward higher levels of significance due to small numbers of topologies , 100 random topologies where added to each test . Tree congruence was also assessed by using the topological supertree approach ( PhySIC_IST ) [37] . This non-plenary supertree , which does not necessarily contain all the taxa present in the source trees , discarding those position which greatly differed among the source trees or for which insufficient information was provided [37] . The informativeness of a supertree is estimated using a variation of the CIC ( Cladistic Information Content ) criterion that takes into account both the presence of multifurcations and the absence of some taxa . This is basically proportional to the number of complete binary trees that are compatible with the evaluated supertree . The Le and Gascuel ( LG ) replacement matrix that incorporates the rate variability across sites in likelihood calculations and the replacement rate estimations ( implemented in PhyML , version 3 . 0 ) was used with a proportion of invariables sites ( I ) and gamma-distributed ( G ) rate variations across sites ( i . e . , LG+I+G ) to infer the best amino acid replacement matrices for ML tree topology [38] . Evidence for recombination between different genotypes was assessed using several methods ( pairwise homoplasy index , substitution distribution methods and phylogenetic methods ) as analyses of simulated data showed that no single method is optimal , whereas multiple approaches may maximize the chances of detecting recombination events [39] . The pairwise homoplasy index ( PHI test ) implemented in SplitsTree , version 4 . 11 . 3 , calculates the mean of the refined incompatibility scores ( representing the minimum number of homoplasies that have occurred in the history of the aligned sequences between two sites ) obtained for nearby nucleotide sites along the sequences . Normal approximation of a permutation test was used to assess the significance of the PHI statistic for the presence of recombination ( p-values<0 . 05 indicate significant presence of recombination ) [40] . Substitution/distribution-based methods ( GENCONV , MaxChi and the 3Seq algorithms implemented in the RDP3 package ) test for significant clustering of substitutions within gene sequences , while phylogenetic methods ( RDP algorithm implemented in the RDP3 package ) search for significant variability in tree topologies among adjacent sequence fragments [41]–[45] . All these algorithms focus only on polymorphic sites within sequence triplets selected from a larger alignment . The major advantage of the substitution/distribution methods relative to pure phylogenetic and distance-based approaches is that they often allow the detection of recombination events that cannot , for example , be visualized as sequences “jumping” between clades of phylogenetic trees constructed using different alignment partitions [46] . Evidence for recombination was accepted if significant ( p<0 . 01 ) and detected by at least two tests .
In this study , 222 Leishmania strains isolated in Africa and Eurasia and that belong to 10 different Leishmania species ( identified by the traditional biochemical criteria ) were selected using both geographical and biochemical ( isoenzymatic ) criteria ( see M&M section and Table S1 ) . After a selection step ( see M&M section ) , seven single copy coding DNA sequences located on six different chromosomes were amplified and sequenced , giving a 4 , 677 bp-long concatenated sequence ( Table 1 ) . In all , 522 polymorphic sites including 412 informative parsimonious sites and 110 singletons were identified . Depending on the locus analyzed , the number of segregating sites ranged from 34 ( locus 31 . 2610 ) to 111 ( locus 31 . 0280 ) and the percentage of polymorphic sites from 7% to 13 . 7% ( mean = 11 . 2% ) ( Table 1 ) . This polymorphism frequency was similar to the one reported for the Leishmania Viannia sub-genus ( 8 . 25% ) and for Trypanosoma cruzi ( 6% ) [13] , [47] . The nucleotide diversity varied from π = 0 . 014 ( locus 10 . 0560 ) to π = 0 . 031 ( locus 31 . 0280 ) ( mean value: π = 0 . 025 ) and the haplotype diversity ranged from 0 . 82 ( locus 31 . 2610 ) to 0 . 96 ( locus 31 . 0280 ) . Overall , the number of genotypes per locus was between 24 and 61 ( median value = 35 , Table 1 ) and was similar to the results presented by Boité et al . on South American Leishmania strains belonging to the Viannia sub-genus ( 25 to 43 , median value = 33 ) [13] . Statistical analysis of the selection pressures acting on the seven loci indicated that both the dN/dS ratio and the transition/transversion ratio ( R ) were strongly biased toward synonymous mutations ( dN/dS ranged from 0 . 015 to 0 . 286 and R from 1 . 41 to 6 . 66; Table 1 ) . This was probably due to the counter-selection of deleterious mutations during the evolution of these genes . Such a strong purifying selection was expected for housekeeping genes that ensure the proper working of the basic molecular machinery of life . This result was confirmed by the codon-based test of neutrality that rejected the null hypothesis of strict-neutrality ( dN = dS ) in favor of purifying selection ( p<0 . 05 ) for the seven loci analyzed . The systematic comparison of the concatenated sequences of the 222 strains gave rise to 140 different genotypes of which 124 were unique . Each genotype was identified by a LST prefix ( Leishmania Sequence Type; see Table S1 ) . Fifteen genotypes were identified in two to six strains ( LST0002 to LST0016 ) and genotype LST0001 was the most common ( 52 strains ) ( Table S1 ) , probably due to oversampling of this ubiquitous genotype in the Mediterranean basin . The unrooted Neighbor-Net ( NN ) network of the concatenated duplicated sequences was well resolved ( least square fit 99 . 23% , Figure 1 ) . The NN network analysis based on concatenated non-duplicated sequences ( with ambiguous sites ) gave identical results ( Supplementary Figure S1 ) . Although several conflicting signal patterns ( box-like structures ) were detected , the prevailing structure of the network was tree-like . Seven highly supported clusters ( bootstrap percentage , BP = 99 to 100% ) were clearly identified ( I to VII , see Figure 1 ) . The Split-decomposition analysis ( fit index = 84 . 43% ) gave a similar pattern with seven strongly supported clusters ( BP = 98 to 100% , Figure S2 ) . Box-like structures were less conspicuous than in the NN analysis . Clusters I , III , IV , V and VI included strains belonging to the MLEE-based species L . aethiopica , L . arabica , L . turanica , L . gerbilli and L . major , respectively . Cluster II included the L . tropica and L . killicki strains and cluster VII was the largest and comprised strains from L . donovani , L . infantum and L . archibaldi . The precise demarcation of the species belonging to the seven clusters is discussed below . Although cluster III was represented by only one strain , this divergent genotype was considered as a distinct genetic entity and should be validated by additional strains analysis . Then the validity of the seven clusters was confirmed by Maximum-likelihood ( ML ) and Bayesian probabilistic approaches that gave very similar topological patterns ( Figure S3 ) . The congruence between the two trees was assessed using the SH test . When tested against 100 random trees , the difference of likelihood between the Bayesian and the ML tree was negligible ( −ln L = 51 . 25 ) in comparison to the difference between the random trees and the ML tree ( −ln L = 44123 to 54958; p<0 . 05 ) , indicating that the ML and Bayesian trees were significantly congruent ( Table S2 ) . In both the ML and Bayesian trees , seven genetic clusters were clearly discernible and highly supported ( BP = 100% and posterior probability , PP = 1 ) ( Figure S3 ) . These seven clusters were identical to those described in the network-based analysis , proving a phylogenetic signal agreement whatever the approach used . To assess the contribution of the seven loci to the main phylogenetic signal we compared the phylogenetic signal given by each locus to the overall signal using different methods . Overall , no major discrepancies were visually found in the seven individual ML trees ( GTR+I+G model , Figure S4A ) . In these single-locus trees , most of the seven genetic clusters ( I to VII ) were easily identified and supported by BP ( bootstrap percentage ) above 98% ( not shown ) . However , cluster II was not well supported in three of the seven tree loci ( i . e . , 10 . 0560 , 14 . 0130 and 31 . 2610 ) and clusters I and IV were weakly individualized in the analysis of locus 31 . 2610 , probably due to a low number of parsimony informative sites ( n = 27 ) ( Figure S4A ) . The consistency of the tree congruence was statistically tested using the SH test . All seven loci were significantly congruent , except the locus 31 . 2610 topology that was rejected ( p-value = 0 . 04 ) when compared to the data of locus 10 . 0560 ( Table 2 ) . The good overall congruence was confirmed by PhySIC_IST , a topological approach that combines different rooted tree topologies in a supertree ( Figure S4B ) [37] . The resulting supertree did not contain relationships that contradicted the source trees . The supertree included all 140 genotypes and had a CIC value of 0 . 7 ( i . e . , more than 70% of the supertree was resolved , indicating that the source trees were fairly congruent ) . However , the genotypes of clusters I and II were unresolved and appeared as a trifurcated branch . These two groups seemed to share common phylogenetic signals , suggesting possible ancestral genetic exchanges between them . To compare the phylogenetic signal of both nucleotide and amino acid ( translated ) sequences we first used an LG amino acid substitution model to build an ML tree from the 1 , 559 concatenated residues , including 152 non-synonymous changes ( Figure S5 ) . The seven clusters ( I to VII ) were highly supported ( BP up to 94% ) . Although the tree topologies of individual loci were apparently poorly resolved due to the low number of alleles ( except for locus 31 . 0280 ) , the main phylogenetic signal was clearly similar to the one deduced using nucleotide sequences . The topological congruence between these two trees was confirmed by the SH test ( Table S3 ) against 100 random trees ( −ln L = 941 . 24 vs . mean −ln L = 48971 . 54; p<0 . 05 for random tree topologies ) . After subtraction of the locus 31 . 0280 data , similar results were obtained , indicating that the prevailing phylogenetic signal was not due mainly to the contribution of this locus ( data not shown ) . Leishmania is currently considered as diploid organism although the occurrence of aneuploid chromosomes could be a frequent event [10] . In the present study , heterozygous sites were detected at all the loci analyzed , confirming that the Leishmania genome is at least partially diploid . Across the 4 , 677 bp analyzed , heterozygous sites were found in 32 . 9% of the selected strains and characterized 1 . 67% of the polymorphic sites ( 11 , 13 , 19 and 30 strains were heterozygous for 4 , 3 , 2 and 1 polymorphic site , respectively ) . Heterozygous strains were representative of the different Leishmania species under study . Most of the heterozygous sites were CT or AG transitions ( 58% Y , 21% R , 9 . 5% S , 7% M , 2% W and 1 . 5% K ) . Locus 31 . 0280 contained the largest number of heterozygous sites , whereas locus 14 . 0130 the smallest ( only 7% heterozygosity , Table 1 ) . This value was slightly higher than the one reported by Boité et al . for strains of the Leishmania Viannia sub-genus , possibly due to the smaller number of strains analyzed [13] . To further investigate the possibility of genetic exchanges among the strains under study , the seven ML tree topologies were visually checked systematically . If separated phylogenetic trees are constructed using sequences corresponding to the two tracts of a recombinant sequence inherited from different parents , the recombinant sequences are expected to apparently jump between clusters when two trees are compared [46] . Among the seven clusters analyzed , only cluster II exhibited such topological rearrangement . Within this cluster some genetic sub-groups were well conserved across the different loci analyzed . Generally , these sub-clusters were constituted of strains coming from one or more neighboring countries ( e . g . , LST59/LST65/LST69 from Morocco , LST68/LST74/LST77 from Kenya , LST16/LST121 from Tunisia and the whole cluster I from Ethiopia and Kenya ) . However , some genotypes ( LST0032 , LST0070 and LST0115 ) grouped in cluster II on loci 03 . 0980 , 14 . 0130 , 31 . 0280 and 31 . 2610 were reshuffled on loci 04 . 0580 , 10 . 0560 and 12 . 0010 ( Figure S6 ) . Similar clues of genotype recombination within cluster II were observed using the PHI-test after NN analysis of each individual locus ( Figure 2 ) and of the concatenated data set [40] . Although the resulting networks exhibited a tree-like structure and were well resolved ( least square fit >98 . 5% ) , significant PHI-test values in favor of recombination were detected for cluster II genotypes in locus 04 . 0580 ( p = 0 . 023 ) and for cluster II and cluster VII in the concatenated data ( p = 0 . 001 and p = 0 . 047 , respectively ) . Substitution/distribution-based methods implemented in RDP3 could not detect any intra-genic recombination ( highest p-value = 0 . 01 ) . When the seven loci were concatenated only one recombination signal involving three genotypes ( LST0032 , LST0045 and LST0115 ) was detected by two of the four tests ( MaxChi and 3Seq , p-value = 0 . 01 ) , although it was not possible to identify the parental genotypes . This result was congruent with the visual comparisons of the ML tree topologies described above for cluster II . The analysis of the data for each cluster identified 78 heterozygous positions in total and more than half of them were in cluster II ( n = 20 ) and VII ( n = 22 ) . Among these positions , 63% ( n = 42 ) could be explained by recombination of parental alleles present in the same cluster . However , linkage analysis ( RDP3 , PHI-test ) to test the possibility of recombination between neighboring alleles did not show any recombination signal , except between the genotypes LST0045 , LST0032 and LST0115 . Nevertheless , the signal was weak and was not possible to identify without ambiguity which of the three genotypes would result from the hybridization of the two others . Surprisingly , in 30 ( 38% ) of the remaining heterozygous positions , the second parental allele was not found in the same cluster or in the other six clusters . Overall , of the 78 heterozygous positions identified in our dataset , 29 ( 37% ) were orphan of one allele and therefore probably resulted from mutations that remained in the current populations rather than from recombination events . The 222 strains from Africa and Eurasia analyzed in this study belonged to 10 different Leishmania species , according to the current MLEE-based taxonomy . The major criterion for the Leishmania taxonomy has been based for up to 20 years on the systematic analysis of 13 to 15 isoenzymes [15] . However some recent studies based on different genetic markers have challenged the MLEE taxonomy and the species or sub-species status of some groups is currently under debate [4] , [5] . Especially , systematic sequencing of allozyme coding genes revealed some genotype-phenotype discrepancies: for instance , indistinguishable phenotypes could be due to distinct genotypes and , conversely , identical genotypes could produce distinct phenotypes [48] , [49] . To assess whether the MLEE phylogenetic signal ( 15 different enzymatic systems ) and the MLSA phylogenetic signal of the seven loci under study were similar , we compared the MLSA and MLEE tree topologies of the 222 strains ( Figure S7 ) . For clusters I , II , IV , V , VI and VII , both the MLSA and the corresponding MLEE-based NJ trees were well supported ( 100% for all the MLSA clusters and 100% , 55% , 100% , 100% , 98% and 100% for the MLEE groups ) . Cluster III was represented by a single genotype and the BP calculation was not relevant . Overall , the main MLEE-based taxonomic groups were clearly confirmed by the SH test ( p-value = 0 . 16 , random trees p-value<0 . 05; Table S4 ) . Cluster I to VII corresponded to the MLEE-based taxonomic groups L . aethiopica , L . tropica complex , L . arabica , L . turanica , L . gerbilli , L . major and L . donovani complex , respectively . Conversely , the intra-cluster supports were significantly higher for MLSA than for MLEE ( median BP from 37 . 5% to 95 . 5% and from 10 . 5% to 90 . 5% , respectively ) . Visually , the sub-cluster tree topologies were hardly comparable between MLSA and MLEE , although only branches supported by BP above 70% were used . This inconsistency was confirmed by the SH test ( p-value<0 . 05; Table S5 ) except for cluster II ( p-value = 0 . 28 ) . To assess whether the genetic structure within clusters was similar between MLSA and MLEE , the supertree approach was used to combine in a supertree all the MLSA and MLEE sub-trees corresponding to the main individual clusters . This method showed that the genetic structure within clusters was not consistent between the two classifications ( CIC values lower than 40% ) . Only cluster II ( L . tropica ) exhibited a well conserved genetic structure between MLSA and MLEE as indicated by the many sub-groups that were conserved in the supertree ( Figure S8; CIC value above 78% ) . The juxtaposition of the genetic data to the geographical data showed that the geographical origin of the genotypes was not randomly distributed ( Figure 3 ) . Although only the cluster II exhibited an evident correlation between the genetic structure and the geographical origin of the strains ( see Discussion section ) . On the other hand , merging the MLEE-based groups into the MLSA-based cluster VII network showed clear inconsistencies ( Figure 4 ) . The sub-networks VIIb and VIIc were partially matched with the L . donovani and L . infantum taxa , respectively . However , the genotypes LST44 , LST81 and LST140 , which were identified by MLEE as L . donovani , were grouped within the VIIc sub-network and LST109 within the VIIb sub-network . The sub-network VIIa appeared to be a mixture of the three MLEE-based taxa L . donovani , L . infantum and L . archibaldi .
One of the main results of this work is the remarkable congruence of the phylogenetic message of the analyzed loci . As shown in Figure 2 , the structure of the networks obtained for loci 03 . 0980 , 04 . 0580 , 12 . 0010 and 31 . 0280 are almost identical . The structures of loci 10 . 0560 , 14 . 0130 and 31 . 2610 are also quite similar to those of loci 03 . 0980 , 04 . 0580 , 12 . 0010 and 31 . 0280 , although they are less resolved especially for clusters I and II . This lower resolution could be explained by a smaller number of informative sites for each of these loci ( n = 41 , 46 and 27 , respectively; mean: 38 ) compared to loci 03 . 0980 , 04 . 0580 , 12 . 0010 and 31 . 0280 ( n = 67 , 71 , 75 and 85 , respectively; mean: 74 ) . Nevertheless , the SH test did not show any contradiction between the phylogenetic signals of each of the seven loci ( taken two by two ) and the signals of each locus compared to the signal produced by the concatenated sequences ( Figure 1 ) . This remarkable congruence was confirmed by both statistical ( non-parametric SH test ) and topological tests ( PhySIC_IST ) . This result is surprising as hybrids between different Leishmania species have been described in natura [16]–[24] . These observations suggested that gene-flows could occur between species during evolution and that hybridization could be an important evolutionary mechanism for the adaptation of parasites to a variety of life cycles and conditions [50] , [51] . The ensuing genome recombination should have given to each gene its own phylogenetic history . Conversely , our results seem to indicate that , although interspecific hybrids exist and can be stable over time , there is no obvious trace of genome recombination and allele introgression between different species . Due to the limited number of genes and strains analyzed in this study it is , however , not possible to exclude that genetic exchanges might act as an evolutionary driving force , particularly in restricted geographical area where true sympatric conditions may exist . It might well be that intra-specific genetic exchanges are much more frequent and much more difficult to detect than inter-specific exchanges . The analysis of alleles shared by clusters I and II , which are sometimes intermingled ( Figure S4B ) or incompletely resolved ( box-like structures in Figure 1 ) is in line with these conclusions . The genotype analysis in these two clusters identified 10 alleles that are shared by all strains and are specific to these two clusters , indicating a common phylogenetic origin of these alleles . However , surprisingly , 25 allelic positions segregated in cluster II but not in cluster I , and 10 positions segregated in cluster I but not in cluster II . Only positions 3491 and 3934 have alleles that segregate in both groups . Thus , although clusters I and II have a common phylogenetic origin and maybe a rather recent individualization , they do not seem to exchange alleles significantly . They might be in a divergent phase of evolution , although they can sometimes be found in sympatric association in Ethiopia and have a common vector ( Ph . Sergenti ) or reservoir ( Procavia capensis ) [52] . To our knowledge , comparable studies on other prokaryotic or eukaryotic organisms with proven existence of interspecific hybrids did not show such an important identity of the phylogenetic signal between a priori independent loci . To explain our results from a biological point of view , two hypotheses can be proposed: i ) the elective loss of one of the two parents' entire genome in hybrid strains , or ii ) the instability of hybrid genomes , which would only appear in exceptional conditions and would then be lost or non- adapted , unable to recombine or transmit new alleles through introgression in a variety of lineages . The experimental work carried out by Peacock et al . on the fate of Trypanosoma brucei hybrid cells during cycles in glossina and mice showing the elective loss of genetic material from one parent is in favor of the first hypothesis [53] . However these biases are not systematic , and considering the high number of interspecific hybrids described in Leishmania , this hypothesis seems little plausible . The second hypothesis is more interesting , but difficult to demonstrate . It supposes that a hybrid genome with a high nucleotide divergence between parental genomes might not persist in the long term . This could be due to disruptions in the expression of these allopolyploid genomes ( transgressive gene regulation , alteration by cell size increase , non-additive expression , etc . ) , or even to anarchic mitotic segregation [54] , [55] . We did not detect any recombination signal whatever the approach used , except between the genotypes LST0045 , LST0032 and LST0115 . Conversely , we identified heterozygous positions in which only one of the two parental alleles was detected in the dataset ( orphan allele ) , probably as a result of mutation rather than recombination events . Accumulation of independent mutations on each of the two homologous chromosomes has been observed in organisms with asexual reproduction , such as Bdelloids , and is usually described as the Meselson effect [56] . However this accumulation does not seem to be systematic and some intensive mechanisms of gene conversion could counteract this effect [57] , [58] . Our results could also be explained by insufficient sampling and the existence of population subdivisions ( Wahlund effect ) , particularly due to geographical or ecological isolation , as discussed below . The number of analyzed strains is probably not sufficient to investigate gene exchanges within Leishmania groups by classic population genetic approaches and the parental genotypes could have been lost in the natural population or misrepresented . This could explain the apparent contradiction between our MLSA-based results ( mainly independent lineages that accumulate divergences without any sign of recent recombination ) and the findings of many MLMT-based studies that often could define the structuration of various populations and showed exchanges or even fixed hybridization events [59]–[61] . In addition , some genotypes included in our dataset were identical , although the corresponding strains were collected over a long period of time . For example , the strains with the genotypes LST0001 , LST0002 , LST0010 , LST0008 , LST0005 and LST0007 were collected over 30 , 29 , 27 , 26 , 25 and 23 years respectively . This temporal stability is in agreement with results presented by Boité et al . on South American Leishmania strains belonging to the Viannia sub-genus and might reflect limited genetic recombination [13] . The relationship between geographical distance and genetic distance is a complex issue for parasites hosted and transmitted by many mammalian and sandfly species . The parasite maintenance depends on its capacity to survive in a complex cycle that involves at least one long-lasting reservoir and a competent vector ( adapted or permissive ) [62] . Accordingly , the parasite distribution should be influenced mainly by the geographical distribution of reservoirs and vectors . However , the parasite capacity to jump from one cycle to another could largely modify this relationship and explain the unexpected spread of some strains . The sample size of some clusters ( III , IV and V , considered as L . arabica , L . turanica and L . gerbilli , respectively ) was not big enough to detect reliably a geographical structuration . Seventeen of the 18 strains of cluster I ( considered as L . aethiopica ) come from a very restricted geographical area ( Ethiopia ) and are maintained in single cycles . They depend on reservoirs of the Procaviidae family , such as Rock Hyrax ( Procavia capensis ) and Bush Hyrax ( Heterohyrax brucei ) , and are transmitted by vectors that are endemic in this area ( Ph . longipes and Ph . pedifer ) [63] . On the contrary , clusters II , VI and VII have a wide geographical distribution and their geographical structuration can be reliably analyzed . Cluster II ( considered to include L . tropica and L . killicki ) is clearly genetically structured as previously reported [64] . This genetic sub-structuration is strongly correlated with the geographical origin for several groups of strains ( cf . Figure 3 ) . For instance , the related genotypes LST0068 , LST0074 and LST0077 in Kenya or LST0059 , LST0065 and LST0069 in Morocco have a strong genetic differentiation , suggesting that these groups of strains are diverging . An explanation would be that these parasites might depend on cycles involving wild animals living in very restricted biotopes , although humans have been considered as the reservoir of these parasites for a long time . Indeed , for genotypes LST0121 and LST0016 , which correspond to Tunisian strains , the role of a small rodent ( Ctenodactylus gundi ) that lives in stone desert areas has recently been confirmed [65] . Similarly , Procaviidae are proven reservoirs of these parasites in Israel and East Africa ( Kenya , Namibia ) and other wild reservoirs remain to be identified [see 66] . However , this strong correlation between geography and genetic structure only concerns part of the strains included in cluster II . Our results also show that some strains that are genetically closely related are scattered in very wide geographical areas . Thus , the almost identical genotypes LST0034 , LST0057 , LST0058 , LST0070 , LST0085 and LST0115 correspond to strains from Morocco , Israel , Jordan , Greece and Turkey . As humans are the reservoir of these strains , and possibly dogs in Morocco , these strains could have been spread through movements of human populations and much faster than strains that depend on wild reservoirs , which are often very localized and isolated [67] . In cluster VI ( considered as L . major ) , we observed a weak genetic structuration and a weak correlation with the geographical origin , except for Central Asian strains . This suggests that flows exist between the different foci . Indeed , identical genotypes are found in wide geographical areas . For instance , the genotype LST0002 comes from strains isolated in Senegal , Morocco , Algeria , Tunisia and Libya . This weak structuration is surprising because many rodent species that belong to at least nine different genera and with a variety of habitats and often a specific ecology have been described as reservoirs of parasites from this group [3] . However , some reservoirs with a wide distribution , such as the Libyan jird ( Meriones Libycus ) that is found from West Sahara to China , could facilitate the spread of parasites in different endemic areas [68] . Based on the analysis of coding sequences , Elfari and Al-Jawabreh defined three main populations corresponding to three geographical areas: Middle East , Africa and Central Asia ( a little diversified group ) [68] , [69] . Similarly , we identified an African subgroup and a Near East/Middle East ( Israel , Jordan , Egypt , Saudi Arabia ) subgroup . Genotypes from Central Asia ( LST0017 , LST0018 ) , which probably derived from Middle Eastern strains , are clearly diverging from cluster II . Accordingly , experimental studies ( crossed infections ) in which strains from Central Asia , Middle East or Africa were inoculated into reservoirs present in these different regions showed that Rhombomys opimus ( greater gerbil ) , the main reservoir in Central Asia ( present from the Caspian Sea to Mongolia ) , cannot be infected by strains from Africa or the Near/Middle East [68] . Our results also show that genotypes from Iran , Iraq and the Indian sub-continent are in an intermediary position between African and Near/Middle East groups . We do not have a convincing hypothesis to explain this distribution . Cluster VII ( L . donovani , L . infantum and L . archibaldi ) seems to have a poor genetic structure , but can be roughly divided in two major groups: one gathering strains mainly from Eastern Africa ( Kenya , Soudan , Ethiopia ) and the other including strains from countries around the Mediterranean basin . Some strains from India and China are clearly genetically different ( cf . Figure 3 ) . Many population genetics studies ( including comparisons of whole genomes ) that focused on strains belonging to this cluster , usually at a smaller geographical scale , reported often contrasting/different description of the geographical structuration of populations and sub-populations [review in 70] , [ 60] , [ 61] , [ 71] , [ 72] . For instance , using similar MLMT approaches , Downing et al . showed that almost all of the 168 Indian strains under study were genetically identical ( 108 completely identical to each other ) . Conversely , Gelanew et al . found in 63 Ethiopian strains as many genotypes as the strains analyzed [11] , [60] . Due to the insufficient number of polymorphisms ( as compared to MLMT and entire genome sequencing ) and the small number of samples from each country , our data cannot contribute to the debate at this smaller geographical scale . Conversely , at a larger geographical scale and notwithstanding possible sampling bias , our work shows two important points: i ) highly divergent genotypes can be present in the same country , ii ) the LST0001 and LST0003 genotypes are widespread ( in 21 and 5 countries , respectively ) and 56 of the 64 genotypes of cluster VII ( are limited to a single country ( Table S1 ) . These results seem to indicate that the spread of strains in different countries is not even . This could not be directly explained by the important mobility of the two main reservoirs ( humans in the Middle East and East Africa and dogs in the Mediterranean area ) of cluster VII strains . It can be hypothesized that genotypes LST0001 and LST0003 might have specific features that allow them to efficiently adapt to various ecological conditions and to spread . Our results also show that , in a given place , the parasitic cycle is possible with parasites that are genetically very different , and that genetic homogeneity is not necessarily a consequence of the adaptation to a very specific cycle . Since 1990 , the identification and classification of the Leishmania genus have mainly been based on the biochemical characterization of 15 isoenzyme systems ( MLEE ) and are generally well correlated with known epidemiological data on the different vectors and reservoirs [15] . However , several recent studies questioned the validity and practical interest of some Leishmania species [4] , [5] . This controversy has important practical implications , particularly for the harmonization and standardization of therapeutic care of patients with leishmaniasis . The question of the criteria used to define Leishmania species is complicated both by the existence of interspecific hybrids and by the difficulty to show genetic exchanges within species . This work was not carried out with the aim of proposing taxonomic changes . We chose i ) to analyze a priori genetic data without considering the species names to avoid interferences between methods , and ii ) to analyze the available isoenzymatic data for each strain to assess their correlation with the gathered genetic data . This correlation is on the whole very good . The analyzed Eurasian and African strains are related to 10 species according to the current biochemical classification and these 10 groups matched the seven genetic clusters defined by MLSA and are separated by large genetic distances ( cf . Figure 1 ) . Several molecular studies did not manage to clearly identify strains from cluster I/L . aethiopica and from cluster II/L . tropica [14] , [73] , [74] . Some of these results could be explained by an insufficient phylogenetic signal due to the small numbers of strains and/or markers . Our findings , and in particular the separated segregation of alleles between the seven clusters ( cf . supra ) would rather be in favor of a progressive genetic isolation between cluster I and II . Strains from cluster I/L . aethiopica might be distant descendants of ancestral populations that led to cluster II/L . tropica and that adapted to a very specific cycle ( see the “Geographical distance and genetic distance” section above ) and became confined to Ethiopia . From an ecological and epidemiological point of view , the localization of the vectors Ph . longipes and Ph . Pedifer , which are endemic in Ethiopian and Kenyan uplands ( usually at altitudes above 1700 meters ) , could reflect this restricted distribution , although the tightness of such geographical barriers can sometimes be questioned as a unique explanatory mechanism [18] , [52] . Similarly , the L . killicki species that was described by Rioux et al . based on biochemical and epidemiological criteria , is not clearly differentiated from other cluster II strains in our analysis [75] . However , although included in cluster II , strains that are described by biochemical criteria as L . killicki remained grouped in our analysis and could represent a branch located in Tunisia and Eastern Algeria and in the process of differentiating . The finding that their reservoir ( Ctenodactylus gundi ) is a small rodent living in mountainous and dry areas in these regions could partly explain the differentiation of this group [65] . However , this sub-cluster was genetically poorly differentiated and we would not consider L . killicki as a valid Leishmania species . Clusters III/L . arabica , IV/L . turanica and V/L . gerbilli are clearly individualized and concordant , although they are represented by a small number of strains . Cluster VI and L . major match perfectly . Clusters VII gathers strains belonging to three taxa ( L . donovani , L . infantum and L . archibaldi ) . Their biochemical identification by electrophoretic mobility is based on a single polymorphism ( 275Tyr/275Asp ) in the enzyme Glutamic oxaloacetic transaminase ( GOT ) ( relative mobility: 113 , 100 and 110 , respectively; 110 appears to be the mobility of the heterozygous 100+113 alleles , F . Pratlong pers . comm . ) . All the strains analyzed in this study were characterized by this technique . Such a taxonomical classification based on a single amino acid polymorphism is controversial and could be prone to homoplasic mutations ( see ref . 60 and Figure 4 ) [4] , [5] , [76] . In our analysis , cluster VII genotypes were roughly subdivided in three sub-networks . Sub-networks VIIa and VIIb almost exclusively included East African strains ( Soudan , Ethiopia , Kenya ) , whereas sub-network VIIc included North African and Western European strains . Some genotypes from the Far East ( China , India , Sri Lanka ) were not included in these sub-clusters and are characterized by large genetic distances ( i . e . LST0019 , LST0037 , LST00132 and LST0133 in Figure 4 ) . From a taxonomic point of view , sub-networks VIIb and VIIc partially match the taxa L . donovani and L . infantum , respectively . However , the genotypes LST0044 , LST0081 and LST0140 that correspond to strains from Turkey , Ukraine and Yemen identified as L . donovani by biochemical methods were included in our analysis among strains identified as L . infantum ( sub-network VIIc ) . This could be result of homoplasy at the GOT locus , as suggested by Jamjoon et al . to explain inconsistencies in the identification of East African strains [76] . The sub-network VIIa did not show any congruence with the biochemical classification . Strains identified as L . donovani , L . infantum or L . archibaldi are intermingled and are poorly differentiated from a genetic point of view . Piarroux et al . hypothesized that the taxon L . archibaldi might be a hybrid between L . infantum and L . donovani , based on the heterozygosity of the gene coding for the GOT enzyme [77] . Our results do not show a significantly high heterozygosity rate for the strains of this taxon . The validity of the taxon L . archibaldi was questioned by several genetic studies because it cannot be differentiated from L . donovani [review in 76] . Our results suggest that the biochemical characterization of strains included in this sub-network does not allow defining a taxon without ambiguity . This discordance could be due to homoplasy but also to the lack of robustness [49] , [71] , [76 and supra] . The L . infantum and L . donovani taxa , originally proposed to reflect different cycles and clinical manifestations , are in relative continuity and sometimes intermingled , in line with the term “L . donovani species complex” suggested by Lainson and Shaw 25 years ago [78] , [79] . This might be due to ongoing exchanges between these taxa or to progressive differentiation , but without taxonomic and geographical discontinuity . This seems to contradict results of many studies using more variable markers , such as MLMT , and showing that an obvious structuration exists in this population , even sometimes at a very small scale [review in 61] , [ 70] . Altogether , these data suggest that local barriers might be enough to maintain a detectable structuration; however , at a larger geographical and temporal scale , strains included in cluster VII might form a continuum . After more than 20 years of debate , the question of genetic exchanges between parasites of the Leishmania genus remains open . Although interspecific exchanges could be a possible source of plasticity and adaptation to new life conditions and they have been described in natura , they do not seem to have left detectable traces in the current genomes , at least in our dataset ( 222 Leishmania strains from Africa and Eurasia ) . Different Leishmania species might evolve through progressive divergence , by accumulating mutations and being structured through distinct parasitic cycles . Concomitantly , the dispersion of some genotypes in wide geographical areas also indicates a process of active diffusion , probably linked to the mobility of some reservoirs , such as humans or dogs , and to the parasite capacity to jump from a reservoir species to another . From a taxonomic point of view , the strong genetic structuration of the different species does not question the current classification , except for species causing visceral forms of leishmaniasis ( L . donovani , L . infantum and L . archibaldi ) for which the current classification does not seem valid . Although these taxa cause different clinical forms and are maintained in different cycles , they are not clearly distinct and form a continuum , in line with the concept of “L . donovani species complex” already suggested for this group a long time ago . Our data also suggest that L . killicki should not be considered as a valid Leishmania species . These results might have practical consequences regarding the molecular identification of Leishmania parasites and the subsequent therapeutic management . Finally , our system provides an improved resolution in comparison to MLEE and could contribute to both a novel MLSA/MLST system and future Leishmania subgenus whole genome sequencing projects . | The mechanisms of genomic and genetic evolution in the Leishmania order , a protozoan group that contains about twenty pathogenic species , are the focus of much debate . Although these parasites have been considered for years to reproduce clonally , recent works have demonstrated both experimental and in natura intra- and inter-specific hybrids . Interspecific exchanges should be sources of plasticity and adaptation to new parasitic cycles . In this work we used a MultiLocus Sequence Analysis approach to analyze 222 Leishmania strains that belong to different species and were isolated in African and Eurasian foci . This analysis classified the different strains in seven robust genetic clusters that showed remarkable congruence of the phylogenetic message between them . From a taxonomic point of view , the seven clusters overlapped with most of the biochemical taxonomic groups currently in use except for species causing visceral forms of leishmaniasis . Contrary to what expected , we did not detect traces of interspecific recombination and genetic exchanges between the different species . Finally , these results should have consequences on the taxonomy , on our understanding of the epidemiology and on the therapeutic management of these infections . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"genomics",
"genetics",
"microbiology",
"biology",
"evolutionary",
"biology"
] | 2013 | Genetic Structure and Evolution of the Leishmania Genus in Africa and Eurasia: What Does MLSA Tell Us |
Erythropoietin ( Epo ) -induced Stat5 phosphorylation ( p-Stat5 ) is essential for both basal erythropoiesis and for its acceleration during hypoxic stress . A key challenge lies in understanding how Stat5 signaling elicits distinct functions during basal and stress erythropoiesis . Here we asked whether these distinct functions might be specified by the dynamic behavior of the Stat5 signal . We used flow cytometry to analyze Stat5 phosphorylation dynamics in primary erythropoietic tissue in vivo and in vitro , identifying two signaling modalities . In later ( basophilic ) erythroblasts , Epo stimulation triggers a low intensity but decisive , binary ( digital ) p-Stat5 signal . In early erythroblasts the binary signal is superseded by a high-intensity graded ( analog ) p-Stat5 response . We elucidated the biological functions of binary and graded Stat5 signaling using the EpoR-HM mice , which express a “knocked-in” EpoR mutant lacking cytoplasmic phosphotyrosines . Strikingly , EpoR-HM mice are restricted to the binary signaling mode , which rescues these mice from fatal perinatal anemia by promoting binary survival decisions in erythroblasts . However , the absence of the graded p-Stat5 response in the EpoR-HM mice prevents them from accelerating red cell production in response to stress , including a failure to upregulate the transferrin receptor , which we show is a novel stress target . We found that Stat5 protein levels decline with erythroblast differentiation , governing the transition from high-intensity graded signaling in early erythroblasts to low-intensity binary signaling in later erythroblasts . Thus , using exogenous Stat5 , we converted later erythroblasts into high-intensity graded signal transducers capable of eliciting a downstream stress response . Unlike the Stat5 protein , EpoR expression in erythroblasts does not limit the Stat5 signaling response , a non-Michaelian paradigm with therapeutic implications in myeloproliferative disease . Our findings show how the binary and graded modalities combine to generate high-fidelity Stat5 signaling over the entire basal and stress Epo range . They suggest that dynamic behavior may encode information during STAT signal transduction .
Healthy individuals at sea level continuously generate red blood cells in a process known as “basal erythropoiesis” that is essential to life . Erythropoiesis increases by up to 10-fold its basal rate in response to hypoxic stress , as may occur at high altitude , or in response to anemia or hemorrhage . Erythropoietic rate is regulated by the hormone Erythropoietin ( Epo ) , whose concentration in blood spans a remarkable , three orders of magnitude range , from ≈0 . 01 U/ml in the basal state to 10 U/ml in extreme stress . Epo exerts its effects by binding to its receptor , EpoR , a transmembrane homodimer of the cytokine receptor superfamily expressed by erythroid progenitors [1] . Epo or EpoR-null mice die at mid-gestation as a result of complete absence of mature red cells [2] , and EpoR signaling is essential for both basal and stress erythropoiesis [3]–[7] . Binding and activation of the EpoR results in activation of the cytoplasmic tyrosine kinase Jak2 , and in phosphorylation of EpoR cytoplasmic-domain tyrosines that act as docking sites for signaling intermediates including Stat5 [8] . A key challenge lies in understanding how EpoR signaling might differ between stress and basal conditions . This challenge is of particular relevance to clinical practice , where Epo is widely used and where erythropoietic mimetics are under intense development to maximize benefit while reducing risk [9] , [10] . Here we addressed this question by studying Stat5 , which , as suggested by mouse genetic models , is a key mediator of both basal and stress erythropoiesis . Thus , Stat5-null mice die perinatally due to anemia , while mice hypomorphic for Stat5 survive , but are deficient in their response to erythropoietic stress [6] , [7] , [11] , [12] . Stat5 functions are due to two highly homologous proteins , Stat5a and Stat5b , of the Signal Transducers and Activators of Transcription ( STAT ) family . STAT proteins are latent cytoplasmic transcription factors that become activated by phosphorylation of a C-terminal tyrosine in response to a variety of extracellular signals [13] , [14] . Stat5 is a key mediator of cell survival in erythroblasts and other hematopoietic progenitors . In addition , it is frequently constitutively active in myeloproliferative disease and in hematological malignancies [6] , [7] , [11] , [12] . Here we asked whether the dynamic behavior of the Stat5 activation signal , namely , the way it varies with Epo concentration and with time , differs between stress and basal erythropoiesis . Previously , distinct dynamic forms of ERK or Ras signaling have been shown to specify distinct cellular responses [15] , [16] . The dynamic form of a signal , however , is often masked when measured in large populations of cells whose responses are inherently variable . Analysis of a signal's dynamic properties therefore requires measurement in single cells , with relatively few such studies to date . To address this , we analyzed Stat5 signaling using flow-cytometry , in primary murine erythroid progenitors , either in vivo or shortly following harvest ( <24 h ) . We combined two recent flow-cytometric assays , identifying differentiation-stage-specific erythroblasts in tissue using cell-surface markers [17]–[19] and measuring their Stat5 phosphorylation signal ( p-Stat5 ) using intracellular flow-cytometry [20] . We determined the time course and full dose-response curves of the p-Stat5 response to the entire basal and stress Epo concentration range , in freshly harvested fetal liver erythroblasts at five distinct stages of differentiation . We found that Stat5 signals through two modalities , binary ( digital ) and graded ( analog ) . We characterized these modalities using wild-type mice and an EpoR mutant mouse that we found to be restricted to the binary Stat5 signaling modality . We show that later erythroblasts generate a low intensity but decisive , binary “on” or “off” p-Stat5 signal that is both necessary and sufficient for mediating Stat5 functions in basal erythropoiesis . By contrast , in early erythroblasts Stat5 signaling is graded , reaching much higher signal intensities that are necessary for the stress response , including the upregulation of the transferrin receptor ( CD71 ) , a novel EpoR and Stat5 stress target . The orderly transition in the modality of Stat5 signaling from early to later erythroblasts is due to decreasing Stat5 protein levels with erythroid maturation . Stat5 protein levels determine both maximal p-Stat5 signal intensity and the steepness of the Stat5 signaling response . This contrasts with EpoR expression , which does not appear to impose a limit on the maximal p-Stat5 response . Our work shows that Stat5 signaling dynamics conveys information specifying the required functional outcome in erythroblasts . The unique combination of a steep , binary response to low Epo in the basal state , with a higher intensity graded signaling modality during stress , allows Stat5 to transduce Epo stimuli with high fidelity over its entire physiological and stress range .
Murine erythropoiesis takes place in fetal liver between embryonic days 12 ( E12 ) and 15 . To examine intracellular Stat5 activation by phosphorylation , we fixed and permeabilized fresh fetal liver cells , which we then labeled with an AlexaFluor647-conjugated antibody specific to the Stat5 C-terminal phosphotyrosine . In addition , we labeled the cells' surface with antibodies directed at CD71 and Ter119 , which may be used to stage erythroblast maturation [17] , [18] , [21] , [22] . We distinguished subsets S0 to S4 in the fixed fetal liver , with the earliest erythroid cells in S0 , maturing into increasingly differentiated erythroblasts in S1 through S4; S3 is further subdivided into earlier , large cells and more differentiated , small cells ( Figure 1A ) . Unless otherwise stated , “S3” below refers to “S3 large . ” All cells in subsets S1 to S3 are Epo-dependent erythroid precursors; S0 is composed of earlier , Epo-independent erythroid progenitors ( 70% ) and non-erythroid cells ( 30% , [18] ) . Following stimulation of freshly isolated fetal liver cells with Epo , we measured an Epo-dependent signal with the anti-phosphorylated-Stat5 ( p-Stat5 ) antibody ( Figure 1B ) . This signal was specific to the active , p-Stat5 , since it was obtained in wild-type , but not in Stat5−/− fetal liver ( Figure 1B , upper panels ) . Further , the p-Stat5 signal was lost if , following Epo stimulation , fixed cells were incubated with λ phosphatase ( Figure 1B , lower panels ) . Work below also confirmed , with the use of a Stat5 mutant , that the signal is specific to the C-terminal Y694 residue . We stimulated freshly isolated fetal liver cells with Epo and examined the resulting p-Stat5 response in each of the fetal liver subsets ( Figure S1A; Figure 1C–E ) . We measured three aspects of the p-Stat5 fluorescence signal ( Figure 1C ) . First , “total p-Stat5” corresponds to the p-Stat5 median fluorescence intensity ( MFI ) of the entire subset population; the total p-Stat5 MFI of all S3 subset cells in the red histogram , upper panel of Figure 1C , is 1 , 200 fluorescence units . This measure includes both signaling and non-signaling cells . Second , we measured the fraction of cells that are “p-Stat5 positive” ( p-Stat5+ ) , lying within the p-Stat5+ gate ( Figure 1C , lower panel ) , as an estimate of the fraction of signaling cells . The placement of the p-Stat5+ gate was determined by reference to the baseline , pre-stimulation histogram ( black histogram , Figure 1C , lower panel ) , which was closely similar to that of cells stained with an isotype-control antibody in place of the anti-p-Stat5 antibody . Last , we measured the “p-Stat5 in p-Stat5+ cells , ” which estimates the p-Stat5 MFI in signaling cells only ( Figure 1C , lower panel , where p-Stat5 in p-Stat5+ cells is 1 , 700 fluorescence units ) . Using these measures , we examined the p-Stat5 response to Epo at 15 min post-stimulation , when a peak response is attained ( see time course of activation , Figure S1B ) . The p-Stat5 signal intensity was highest in S1 , decreasing with erythroid maturation through subsets S2 and S3 ( Figure S1A ) . In the earliest , S0 subset , only ∼25% of cells responded to Epo , suggesting that the p-Stat5 response pathway becomes fully activated only with the onset of Epo dependence at the transition from S0 to S1 , when a number of key transcriptional and epigenetic changes take place in erythroid progenitors ( Figure S1A , C [18] , [19] ) . We contrasted the response of S1 and S3 cells to a range of Epo concentrations encountered in physiological ( <0 . 05 U/ml ) and hypoxic-stress conditions ( 0 . 05 to 10 U/ml; Figure 1D; each histogram in the left panels is represented as a data-point of the same color in the dose/response curves in the right panels ) . S1 cells generated a graded increase in total p-Stat5 in response to increasing Epo ( Figure 1D , right upper panel ) , which reflected a graded increase in both the number of signaling cells ( p-Stat5+ cells , Figure 1D , right middle panel ) and in the signal intensity of signaling cells ( Figure 1D , lower right panel ) . “S3 large” ( = “S3” ) cells attained a ≈4-fold lower signal than S1 cells . The S3 cell population also showed a graded increase in total p-Stat5 with increasing Epo stimulation ( Figure 1D , right upper panel ) . However , this was principally the result of an increase in the number of signaling cells with Epo concentrations ( Figure 1D , right middle panel ) ; the p-Stat5 signal intensity within signaling cells remained relatively constant ( Figure 1D , right lower panel ) . A summary of five independent experiments for all erythroid subsets shows that these dose/response characteristics are reproducible ( Figure 1E ) . In spite of wide variation in the number of responding cells , the p-Stat5 signal intensity of S3 cells with a positive p-Stat5 response ( p-Stat5 MFI in p-Stat5+ cells ) remained relatively constant ( Figure 1E , middle and lower panels ) . We found that maturation-stage equivalent cells in adult tissue behaved similarly ( “EryA” erythroblasts , Figure S2 ) . Together , these findings raised the possibility that S3 cells may be generating a binary p-Stat5 response . Under this hypothesis , p-Stat5 activation in individual S3 cells would be switch-like , with cells either expressing their maximal p-Stat5 levels regardless of Epo concentration ( and are “on” ) or failing to respond and remaining “off . ” In an idealized switch-like response , an infinitesimally small increase in stimulus in the region of the stimulus threshold can cause the response to increase from 0% to 100% . The Hill coefficient of this idealized step-like dose/response curve approaches infinity ( Figure 2A , gray step response ) . Switch-like responses in biology , however , are only approximations of this idealized case , in that the switch in response from 0% to 100% requires a small but finite increase in the stimulus . Goldbeter and Koshland called switch-like biological responses “ultrasensitive” and defined them as steeper than the graded hyperbolic Michaelis-Menten curve—that is , responses with a Hill coefficient ( nH ) larger than 1 [23] . In a graded response ( nH = 1 ) , the stimulus needs to increase 81-fold in order to increase the response form 10% to 90% of maximum . By contrast , in a more switch-like , steeper response , with nH = 3 , only a 4-fold increase in stimulus is required for a similar change in response ( Figure 2A ) . Examples of effective switch-like responses include the cooperative binding of oxygen to hemoglobin ( nH = 2 . 7 ) and the MAPK pathway in Xenopus oocytes ( nH = 4 or 5 ) [24] , [25] . A binary p-Stat5 response in single cells may sometimes appear to be graded when the signal is measured in a population of cells . This is illustrated in Figure 2B , which contrasts three hypothetical cases of signaling cells . In the first case ( Figure 2B , left panels ) , there is a graded increase in signal within individual cells in response to increasing Epo concentration , resulting in a graded increase in the total p-Stat5 MFI at the population level . The corresponding Epo dose/p-Stat5 response curve has a Hill coefficient of 1 ( Figure 2B , left lower panel ) . The two hypothetical cases of binary signaling ( Figure 2B , middle and right panels ) differ from each other only in the Epo threshold at which cells respond with a p-Stat5 signal . In the “variable threshold” example , individual cells vary substantially with respect to the Epo concentration at which they switch from “off” to “on . ” The measured p-Stat5 signal , which is the sum of the signals generated by a large population of cells , increases in a graded manner with increasing Epo concentration ( nH = 1 ) ( Figure 2B , lower middle panel ) . The flow-cytometry histograms for each Epo dose ( in color ) are a composite of two underlying histograms , of non-signaling cells ( in grey ) and signaling cells ( in black ) . Only the amplitudes of the black and grey histograms change with Epo concentration , while their MFI remains constant . However , the MFI of the composite , color histogram increases in a graded manner with Epo dose . By contrast , in the second binary signaling example ( Figure 2B , right panels ) , cells have a similar threshold to Epo stimulation , so that the entire cell population switches from “off” to “on” within a narrow Epo concentration range . This results in the population response resembling the binary responses of individual cells , with a much steeper Epo dose/p-Stat5 response curve that is characterized by a high Hill coefficient ( nH>1 ) ( Figure 2B , lower right panel ) . A graded p-Stat5 response in the S3 population ( Figure 1E , top panel ) does not therefore preclude the possibility that individual S3 cells have binary responses that are masked by a variable threshold to Epo ( as in Figure 2B , middle panel ) . We studied p-Stat5 signaling in the EpoR-H and EpoR-HM mouse strains , in which the respective EpoR truncation mutants are “knocked-in” at the wild-type EpoR locus , replacing wild-type EpoR ( Figure 3A , [3] ) . EpoR-H lacks seven of the eight cytoplasmic domain tyrosines . EpoR-HM is similarly truncated but in addition contains the Y343F mutation and therefore lacks tyrosine docking sites for Stat5 . S1 cells from EpoR-H fetal livers generated a p-Stat5 signal equivalent to that of wild-type cells , but had a high p-Stat5 background in the absence of Epo stimulation , consistent with a previously identified negative regulatory function for the EpoR carboxy-terminal domain ( Figure 3B , lower panel; [26] ) . S1 cells from EpoR-HM fetal liver , by contrast , generated only a low-intensity p-Stat5 response to Epo , consistent with previous studies ( Figure 3B , upper panel; [27] ) . A full Epo dose/p-Stat5 response analysis revealed that the maximal p-Stat5 signal generated by S1 cells in EpoR-HM was ≈3–4-fold lower than in wild-type S1 , resembling in intensity p-Stat5 signals generated by more mature , wild-type S3 cells ( Figure 3C–D , Figure S3A ) . Strikingly , in addition to their lower p-Stat5 intensity , the EpoR-HM S1 response was binary ( Figure 3C–E ) , resembling the hypothetical example of binary signaling in a population of cells with similar Epo thresholds ( Figure 2B , right panels ) . Thus , unlike wild-type S1 , the p-Stat5 fluorescence histograms in EpoR-HM S1 are in one of two clusters , either “off” or “on” ( Figure 3C , lower panel ) . The switch from “off” to “on” occurs at ∼0 . 3 U/ml ( see apparent Km values for the EpoR-HM dose/response curve , Figure S3B ) . This binary behavior was reflected in the steep Epo dose/p-Sta5 response curve for EpoR-HM S1 cells ( Figure 3D ) . In each of three independent experiments , the Hill coefficients found for each of the EpoR-HM fetal liver subsets were consistently higher than in wild-type littermate controls ( Figure 3E ) , with nH for S1 cells ranging between 2 and 3 . 5 . Taken together , S1 cells in EpoR-HM have lost the high-intensity graded signaling mode characteristic of this subset . The residual signal is of low intensity , similar to that of S3 cells , and is binary in nature . The S3 subset consists of a spectrum of erythroblast maturational stages with varying size and hemoglobin expression ( Figure 1A; [18] ) . Since variability between cells may mask binary signaling properties , we attempted to subdivide S3 cells into more uniform subsets . Maturation is associated with a decrease in cell size . We made use of this trend to digitally sub-divide the S3 population of cells in an E14 . 5 fetal liver into a series of smaller subsets , based on their forward scatter ( FSC ) parameter , which is a function of cell size ( Figure 3F ) . We confirmed that increasingly smaller cells were indeed increasingly mature by comparing Ter119 expression in each of the FSC gates . As expected , larger cells in FSC gate #6 expressed less Ter119 than smaller cells in FSC gate #3 ( Figure 3F ) . We proceeded to analyze the Epo dose/p-Stat5 response properties of cells in individual FSC gates , and found that signaling by smaller and more mature cells was binary , with high Hill coefficients; the steepness of the dose/response curve decreased progressively in less mature cells , while the p-Stat5 intensity increased . For comparison , the dose/response curve for the S1 subset was much less steep ( nH = 1 . 8 ) , similar to that found for the least mature cells within S3 ( Figure 3G ) . This analysis suggests that the most mature cells within S3 generate the lowest p-Stat5 signal intensity , and have the steepest dose/response curves , giving rise to an overall binary response pattern . We carried out a similar analysis on S3 cells from a younger , E12 . 5 embryo , in which the most mature cells within S3 had not yet developed ( Figure S4 ) . There were fewer cells in the low FSC gates of the E12 . 5 embryo , and these were less mature than in corresponding gates of the E14 . 5 embryos , as indicated by Ter119 expression ( Figure S4A , right panels ) . All dose/response curves in the E12 . 5 embryos had lower Hill coefficients ( nH∼1 . 2 to 1 . 8 ) and hence a more graded response ( Figure S4B ) . Of interest , cells in FSC gate #3 in the E12 . 5 fetal liver generated a similar p-Stat5max signal intensity to that of cells in FSC gate #4 of the E14 . 5 fetal liver . However , the steepness of the dose/response curve of the two cell types was markedly different ( nH = 1 . 6 and 5 , respectively ) ( Figure S4C ) , in line with their differing maturational state . This analysis suggests that , for a given maximal p-Stat5 signal intensity , more mature cells generate a steeper dose/response curve . We investigated factors that might account for the gradual decrease in the p-Stat5 response as cells mature ( Figure 1E ) . Differentiation of S1 into “S3 small” cells takes 24 to 48 h and entails large changes in gene expression [18] . We examined the potential role of two established Jak2 and Stat5 negative regulators , Shp1 ( Figure S5 ) and SOCS3 ( Figure S6 ) [28] . Shp1 mRNA expression decreases with maturation from S0 to S3 ( Figure S5A ) . There was no significant difference in either the time course of the p-Stat5 response to Epo or in the dose/response curve , between Shp1−/− ( C57BL/6J-Ptpn6me/J ) fetal liver and littermate controls ( Figure S5B–C ) . In contrast to Shp1 , SOCS3 mRNA expression increased with the transition from S1 to S3 ( Figure S6A ) . Knock-down of SOCS3 expression successfully prevented its induction following Epo stimulation ( Figure S6B ) . In S1 cells , SOCS3 knock-down had no effect on the initial p-Stat5 response , but it prevented the decline in p-Stat5 that was invariably detected by 2 h post-stimulation ( Figure S6C , left panels ) . This pattern is consistent with the known negative feedback role of SOCS3 in Stat5 signaling [29] , [30] . In contrast to S1 , knock-down of SOCS3 in S3 cells increased the peak p-Stat5 signal intensity at 15 min , suggesting that the lower p-Stat5 signal intensity in S3 is in part the result of their higher SOCS3 expression ( Figure S6C , right panels ) . We examined the potential role of changes in EpoR or Stat5 protein levels during erythroblast maturation . To this end we investigated embryos heterozygous for the null allele of either Stat5 or EpoR ( Figure 4 ) . An Epo dose/p-Stat5 response analysis in fetal liver cells from Stat5+/− embryos showed a clear decrease in the p-Stat5 signal across the entire Epo concentration range in all fetal liver subsets S1 to S3 , compared with wild-type controls ( see representative example in Figure 4A; a dataset of 7 Stat5+/− and 6 wild-type littermates embryos is summarized in Figure 4B ) . Fitting Hill curves to the dose/response data yielded three parameters: the apparent Km , the maximal p-Stat5 signal at high Epo concentrations , defined as “p-Stat5max , ” and the Hill coefficient , nH ( Figure S7A ) . In addition to the clear decrease in p-Stat5max in all subsets of the Stat5+/− fetal liver ( Figure 4A , B , Figure S7A ) , the p-Stat5 response curve was steeper , reflected in a higher Hill coefficient ( Figures 4B and S7A ) . There was also a shift to the right ( increase in the apparent Km ) in Stat5+/− S3 cells . The apparent Km reflects a number of separate sequential interactions: binding of Epo to the EpoR , Jak2 activation , Jak2 phosphorylation of the EpoR , binding of Stat5 to the phosphorylated EpoR , and phosphorylation of Stat5 . A change in the apparent Km can in principle be due to alterations anywhere in this pathway . Reduced expression of Stat5 in Stat5+/− embryos may affect recruitment of Stat5 to EpoR phosphotyrosines , potentially explaining the higher apparent Km . To assess the relation between Stat5 protein levels and the maximal p-Stat5 response more precisely , we measured Stat5 protein levels in individual cells within each of the Stat5+/− and wild-type embryos , using anti-Stat5 antibodies and flow cytometry , a method that we verified using the Stat5-null fetal livers ( Figure S7D ) . Stat5 protein levels in wild-type fetal liver decreased with maturation , being highest in S1 and 4-fold lower in “S3 large” cells ( Figure 4D , E , closed symbols ) . A similar pattern was observed in Stat5+/− embryos , but for each corresponding subset , Stat5 protein levels were approximately halved compared with wild-type cells ( Figure 4D , E , open symbols ) . There was a linear correlation ( R2 = 0 . 85 ) between Stat5 protein levels in each of the wild-type or Stat5+/− fetal liver subsets and their corresponding maximal p-Stat5 response ( p-Stat5max , Figure 4F; p-Stat5max was determined by fitting a Hill curve to data from each embryo ) . These findings suggest that decreased Stat5 protein levels may cause the decrease in the p-Stat5 response with cell maturation in wild-type embryos ( Figure 1E ) , as well as the reduced p-Stat5max in Stat5+/− embryos ( Figure 4A , B ) . We examined the Epo dose/p-Stat5 response in fetal livers derived from EpoR+/− embryos and their littermate controls ( Figure 4C ) . EpoR+/− fetal livers had an approximately 2-fold decrease in EpoR mRNA ( Figure S7B ) . Unlike the Stat5+/− embryos , there was no change in p-Stat5max in EpoR+/− fetal liver . Instead , the EpoR+/− dose/response curves were shifted to the right , with a 2-fold increase in the apparent Km ( Figure 4C , Figure S7C ) , raising the possibility that a doubling in Epo concentration compensated for the reduced expression of EpoR . Therefore , although EpoR+/− fetal liver requires a higher Epo concentration to elicit a given p-Stat5 signal , the likely reduced cell-surface EpoR in these embryos appears not to limit the maximal p-Stat5 response . To investigate this further , we asked whether EpoR+/− fetal livers in fact have less EpoR available for activation . We sorted Ter119 negative cells , equivalent to subsets S0 and S1 , from E13 . 5 fetal livers of either wild-type or EpoR+/− embryos . We briefly stimulated the cells with a high Epo concentration that would be expected to generate a maximal p-Stat5 response ( 2 U/ml for 5 min ) . We used quantitative Western blot analysis to examine both p-Stat5 and phosphorylated EpoR ( p-EpoR ) in each fetal liver ( Figure S8 ) . This analysis showed that EpoR+/− fetal liver cells had reduced p-EpoR but not reduced p-Stat5; specifically , the ratio of p-EpoR to p-Stat5 in each fetal liver was significantly higher in wild-type compared with the EpoR+/− embryos ( 1 . 7±0 . 02 versus 1 . 1±0 . 08 , p = 0 . 002; Figure S8B ) . These results support the conclusion that EpoR expression in primary fetal liver cells is present at sufficiently high levels so as not to limit the maximal p-Stat5 signal . To test whether the loss of the high-intensity p-Stat5 response in mature , S3 cells is indeed due to their decreased Stat5 expression ( Figure 4D–F ) , we asked whether we could rescue high-intensity Stat5 signaling in these cells by exogenously expressing Stat5 . In parallel , we also examined the effect of exogenous Stat5 expression in EpoR-HM erythroblasts , which signal exclusively via the low-intensity binary signaling mode ( Figure 3C–E ) . We electroporated FLAG-tagged Stat5a constructs ( “FLAG-Stat5” ) , or two control constructs , either FLAG-tagged Stat5aY694F ( “FLAG-Stat5Y694F” ) lacking the C-terminal tyrosine , or “empty vector” ( “pcDNA3” ) , into freshly isolated wild-type or EpoR-HM fetal liver . Cells were incubated overnight in the presence of Epo ( 0 . 2 U/ml ) to allow expression of the transduced constructs and were then deprived of Epo for 3 h prior to stimulation with a range of Epo concentrations ( 0 . 004 to 9 U/ml ) for 15 min . Cells were immediately fixed and labeled with both anti-FLAG and anti-p-Stat5 antibodies . A single electroporation contained cells with a spectrum of FLAG expression levels , allowing us to determine how FLAG-Stat5 expression affected the p-Stat5 response ( Figure 5 ) . We first examined how exogenous FLAG-Stat5 protein levels compared with endogenous Stat5 ( Figure S9 ) . Freshly isolated S3 cells express lower levels of the Stat5 protein than S1 cells ( Figure 4D , E , Figure S9A , top panel ) . Following transfection with FLAG-Stat5 , Stat5 protein in S3 cells increased to levels similar to those of the endogenous protein in S1 cells ( Figure S9A , lower panel ) . We were therefore in a position to ask whether increasing Stat5 protein in S3 would be sufficient for these cells to generate the high-intensity p-Stat5 signal characteristic of S1 . A minority of transfected S3 ( 18% , Figure S9A ) expressed FLAG-Stat5 at higher levels than endogenous Stat5 in fresh S1 . Of these , approximately 2% retained p-Stat5 following 3 h Epo deprivation ( Figure 5A , double-headed arrow ) . We excluded all cells expressing the very high FLAG-Stat5 levels from further analysis , by gating specifically on cells with lower FLAG fluorescence . This was possible as FLAG fluorescence was an accurate measure of the level of the exogenous FLAG-Stat5 protein ( Figure S9B ) . For a given Epo concentration , the p-Stat5 response of transfected S3 cells increased with increasing FLAG-Stat5 levels ( Figure 5A , top middle panel ) . There was no increase in the p-Stat5 signal in cells transfected with FLAG-Stat5Y694F , verifying that the p-Stat5 signal detected with increasing FLAG-Stat5 is indeed specific ( Figure 5A , central panel ) . To analyze the p-Stat5 response quantitatively for each FLAG-Stat5 expression level , we sub-divided the “p-Stat5 versus FLAG-Stat5” dot histograms into narrow vertical gates , each containing cells with similar levels of FLAG-Stat5 ( Figure 5B , left panels ) . Three of these vertical gates , numbered 10 to 12 , are color coded in red , green and blue respectively . Cells in these gates are shown either unstimulated ( Figure 5B , top left panel ) or stimulated with Epo concentrations of 0 . 33 U/ml ( middle left panel ) or 9 U/ml ( Figure 5B , lower left panel ) . Panels to the right show an overlay of the cells' responses in each of the red , green , or blue vertical gates ( Figure 5B ) . The entire dataset of the p-Stat5 response to nine Epo concentrations in each of four vertical gates ( 9 to 12 ) for either wild-type or EpoR-HM S3 cells were fitted with Hill curves ( Figure 5C ) . These show that exogenous FLAG-Stat5 has two principal effects . First , the maximal response ( p-Stat5max ) in any given vertical gate is positively and linearly correlated with the level of FLAG-Stat5 protein in that gate ( Figure 5D ) . Second , as FLAG-Stat5 levels increase , there is a decrease in the steepness of the p-Stat5 response curve , reflected by a decreasing Hill coefficient ( Figure 5C ) . As examples , transfected EpoR-HM S3 cells containing high FLAG-Stat5 levels had a dose/response curve with a lower Hill coefficient and a higher p-Stat5max ( nH = 1 . 0 , p-Stat5max = 1 , 100 , gate 12 , FLAG MFI = 1 , 700 ) than cells in the same sample containing lower levels of FLAG-Stat5 ( nH = 4 . 0 , p-Stat5max = 120 , gate 9 , FLAG MFI = 340 ) . Similarly , wild-type S3 cells containing very low levels of FLAG-Stat5 had a dose/response curve with a higher Hill coefficient and lower p-Stat5max ( nH = 3 . 5 , p-Stat5max = 25 , gate 6 , FLAG MFI = 75 ) than cells in the same sample with higher FLAG-Stat5 ( nH = 1 . 0 , p-Stat5max = 100 , gate 8 , FLAG MFI = 200 ) . Therefore , by varying the level of the Stat5 protein in mature S3 erythroblasts from either wild-type or EpoR-HM fetal livers , we were able to generate the entire spectrum of Stat5 signaling responses encountered in the erythroblast maturation series ( Figure 1E ) . Taken together , the data in Figures 4 and 5 show that decreasing Stat5 protein levels with erythroblast maturation is the cause of the gradual shift from high-intensity , graded signaling in early erythroblasts to low-intensity , binary signaling in mature erythroblasts . The loss of high-intensity Stat5 signaling in EpoR-HM shows that , in addition to high levels of the Stat5 protein , this mode of signaling also requires Stat5 phosphotyrosine docking sites on the EpoR . Exogenous expression of Stat5 successfully compensated for the EpoR-HM mutation , rescuing high-intensity graded signaling in these cells ( Figure 5 ) . The linear dependence of p-Stat5max on Stat5 protein levels , whether endogenous ( Figure 4F ) or exogenous ( Figure 5D ) , indicates that Stat5 is limiting for Stat5 phosphorylation in erythroid cells . By contrast , EpoR expression in erythroblasts is not limiting to the maximal p-Stat5 response ( Figure 4C ) . The Michaelis-Menten model of enzyme kinetics assumes that the substrate is present in excess . It therefore is unlikely to apply to the behavior of Stat5 activation in erythroblasts [31] , [32] . The kinetics that apply instead is further analyzed in Text S1 . We used Stat5−/− and EpoR-HM mice to elucidate the specific biological functions of the binary and graded Stat5 signaling modalities . Mice lacking Stat5 die perinatally of severe anemia [6] , [12] , [33] , [34] , suggesting that the functions of Stat5 in erythropoiesis are essential to life . By contrast , EpoR-HM mice , which retain only the binary low-intensity p-Stat5 signal , are viable and have near-normal basal erythropoiesis . Therefore , the low-intensity binary p-Stat5 signal is sufficient to support the essential erythropoietic Stat5 functions required for life . We examined this further by measuring Epo-mediated anti-apoptotic signaling in Stat5−/− and EpoR-HM fetal liver erythroblasts . Anti-apoptosis is a key function of Epo-activated Stat5 in both basal and stress erythropoiesis , and is mediated by its transcriptional activation of the anti-apoptotic protein bcl-xL and other targets [5]–[7] , [34] , [35] . We incubated fetal liver cells freshly isolated from EpoR-HM , Stat5−/− and strain- matched wild-type control embryos in the absence of Epo for 90 min . We then labeled the cells with Annexin V , to detect cells undergoing apoptosis ( Figure 6A , B ) . As reported previously [6] , [34] , a large fraction ( 40% ) of Stat5−/− S1 cells , but only 1%–2% of wild-type controls , were Annexin V positive , confirming the essential role for Stat5 in erythroblast survival ( Figure 6A ) . By contrast , there was little apoptosis in the EpoR-HM fetal liver ( Figure 6B , representative example in upper panel , summary of embryo litters in lower panel ) . Therefore , the low-intensity , binary Stat5 signal generated in EpoR-HM erythroblasts is sufficient for mediating Stat5's anti-apoptotic functions . Although adult EpoR-HM mice are viable , they are nevertheless mildly anemic , and are deficient in their response to erythropoietic stress [3] , [36] . Given our finding that these mice retain the binary but lack the graded high-intensity Stat5 signaling mode , we asked whether the latter is specifically required during stress . The transferrin receptor , CD71 , was recently identified as a Stat5 transcriptional target , and Stat5−/− fetal liver erythroblasts were found to express 50% lower levels of cell-surface CD71 ( Figure 6C; [12] , [34] ) . Here we found that EpoR-HM fetal liver erythroblasts had a milder , though statistically significant , 15% loss of CD71 expression ( Figure 6D; p<0 . 002 , unpaired t test ) , potentially the result of their Stat5 signaling deficit . Although CD71 is highly expressed on fetal and adult erythroid progenitors during basal erythropoiesis , we found that there is a substantial , further increase in its cell-surface expression during the stress response ( Figure 7A ) . Thus , a single subcutaneous Epo injection , which generates stress levels of Epo in blood for ∼24 h , caused a 3-fold increase in CD71 on the surface of splenic EryA erythroblasts ( CD71highTer119highFSChigh [21] ) ( Figure 7A , left panel ) . Further , CD71 increased nearly 2-fold in the same cells in mice placed in a reduced oxygen environment ( 11% oxygen , Figure 7A , right panel ) ; plasma Epo in these mice rises ∼3-fold in the initial 3 days following the onset of hypoxia . An in vivo Epo dose/CD71 response analysis showed a graded increase in cell surface CD71 in response to increasing Epo , with half the maximal increase seen in mice injected with 3 U of Epo/25 g body weight ( Figure 7B ) , and a Hill coefficient of 1 . 5 . These findings establish CD71 as a target of erythropoietic stress whose level is modulated with the degree of stress . Given the mild but significant deficit of CD71 expression in EpoR-HM fetal liver erythroblasts ( Figure 6D ) , we examined expression of erythroblast CD71 during the response of EpoR-HM adult mice to stress ( Figure 7C , D ) . We found that , unlike wild-type mice , EpoR-HM mice completely failed to upregulate CD71 when injected with high Epo ( 100 U/25 g mouse; Figure 7C , D ) . This failure may account in part for the failure of EpoR-HM mice to accelerate erythropoiesis and increase their hematocrit ( Figure 7E ) [3] . Since high exogenous Stat5 restored the high-intensity graded Stat5 signaling missing in EpoR-HM erythroblasts ( Figure 5C ) , we asked whether it may also restore high CD71 expression . We measured CD71 expression in EpoR-HM and wild-type fetal liver cells that were electroporated with FLAG-Stat5 in the experiment illustrated in Figure 5A–D , following overnight culture in stress Epo levels ( 0 . 2 U/ml , Figure 7F ) . We found that cells with increasing FLAG-Stat5 protein showed a corresponding , gradual increase in cell-surface CD71 , in both wild-type and EpoR-HM cells ( Figure 7F ) . These findings strongly suggest that the graded , stress-dependent CD71 up-regulation is a function specifically mediated by the high-intensity graded Stat5 signal during the erythropoietic response to stress .
The biological functions of the two Stat5 signaling modalities are exemplified by the EpoR-HM and Stat5−/− mouse models . EpoR-HM erythroblasts signal exclusively via the binary low-intensity signal . Unlike Stat5−/− mice , which die of fatal perinatal anemia due to erythroblast apoptosis , EpoR-HM mice are viable with near-normal basal erythropoiesis and normal erythroblast survival ( Figure 6B ) . The binary low-intensity pStat5 signal conveys binary , life or death decisions that rescue sufficient numbers of erythroblasts from apoptosis to make developmental and basal erythropoiesis possible . By contrast , the EpoR-HM mice lack an efficient stress response ( Figure 7E , [3] ) . We found that up-regulation of CD71 on the surface of erythroid precursors is a stress-specific graded response that depends on high Epo levels in vivo ( Figure 7A . B ) . It requires the graded , high-intensity p-Stat5 signal that is elicited by stress levels of Epo and that is missing in EpoR-HM mice . This is evident from the finding that EpoR-HM erythroblasts fail to up-regulate CD71 when subjected to high Epo and from rescue of the CD71 response in EpoR-HM erythroblasts transduced with exogenous Stat5 , which restores the high-intensity p-Stat5 signal to these cells ( Figure 7F , Figure 5C ) . Exogenous Stat5 similarly endowed mature wild-type erythroblasts with both high-intensity graded Stat5 signaling and with the ability to induce stress levels of CD71 ( Figure 7F , Figure 5C ) . These findings strongly suggest that the ability of an erythroblast to generate the CD71 stress response is determined by its ability to generate the high-intensity p-Stat5 signal , and not by other aspects of erythroblast maturation . Although the transferrin receptor ( CD71 ) is ubiquitous in dividing cells , it is expressed at uniquely high levels in erythroid progenitors , where it provides the high iron requirement for hemoglobin synthesis . Genetic mutations that decrease either CD71 or plasma iron compromise erythropoiesis , resulting in anemia and a loss of the stress response [37] , [38] . Recently , Stat5 was shown to be required for optimal erythroblast CD71 expression in the fetus [12] , [34] . Here we found that , during stress , CD71 in early erythroblasts increases beyond its already high level in basal erythropoiesis ( Figure 7A–B ) . This increase is a Stat5-dependent function that specifically requires the high-intensity Stat5 signaling mode ( Figure 7C–F ) . Though not reported previously , the increase in cell-surface transferrin receptor during stress is consistent with the higher requirement for iron during stress erythropoiesis [37] , [38] . It is also consistent with the increase in plasma soluble transferrin receptor , a known clinical indicator of increased erythropoietic rate [39] . The failure of EpoR-HM mice to up-regulate CD71 may therefore account for their deficient stress response . It is likely , however , that additional functions regulated by the high-intensity p-Stat5 signal also contribute , including a stress-dependent increase in the level of the anti-apoptotic bcl-xL protein [5] . Binary and graded signaling modes have fundamentally different functional consequences . The steepness of the binary dose/response curve has the advantage of filtering out noise and generating a clear signal that is easily distinguishable from background . This mode of signaling is therefore ideal at the low end of the Epo concentration range , where Epo stimuli , though low , are nevertheless essential for basal erythropoiesis and must be clearly distinguished from noise . A key disadvantage of binary signaling , however , is its inability to encode incremental changes in stimulus . This would exclude it as a useful signaling modality in erythropoietic stress , where Epo concentration determines the precise level of erythropoietic acceleration that is required to compensate for hypoxia . Stat5 bridges this conundrum by combining the binary and graded signaling modalities in a manner analogous to a dimmer switch ( Figure 8A ) , allowing signaling fidelity over a wide Epo concentration range . Low stimuli activate the binary component of the dimmer switch from “off” ( open on/off switch , Figure 8A ) to “on” ( closed on/off switch ) , which closes the electric circuit and switches the light on . A further turning of the power dial incrementally reduces the circuit's resistance , resulting in an incremental , graded increase in light intensity . Similarly , low Epo stimuli result in a binary activation of p-Stat5 . In S1 cells , this binary activation is superseded at higher Epo stimuli with a further , graded increase in the p-Stat5 signal intensity ( Figure 8B ) . Of note , although S3 cells are individually limited to a low-intensity binary response , increasing Epo results in an increasing number of signaling S3 cells , due to their varying activation thresholds ( Figure 8B ) . At the level of gene transcription , p-Stat5 signal intensity , rather than the steepness of the dose/response curve , is likely to determine which subset of gene targets will be activated . As example , it is likely that activation of lower affinity Stat5 binding sites will require higher p-Stat5 concentration , manifesting as higher signal intensities . Further , the p-Stat5 signal intensity may affect the likelihood of formation of Stat5 tetramers , which appear to bind to a functionally distinct subset of Stat5 targets [40] , [41] . While a steep dose/response curve is unlikely to determine which Stat5 targets are activated , its role is to ensure that the low-intensity p-Stat5 signal is generated only in response to a biologically appropriate stimulus . A key challenge of low-intensity signals is their inherently low signal-to-noise ratio . The steep dose/response curve of binary signaling provides a threshold for activation that filters out random noise and ensures that the low-intensity signal is decisive . Networks containing similar signaling components and similar topologies may vary in their output , generating either binary ( digital ) or graded ( analog ) responses , depending on the value of key parameters [42]–[44] . Thus , apparently homologous MAPK modules generate a switch-like response in Xenopus oocytes , but a graded response in the yeast mating pheromone pathway [43] . Further , isogenic yeast cells use a similar complement of transcription factors to generate either binary or graded transcriptional responses from the Gal1 promoter [42] . The mechanism ( s ) that determine whether a response is binary or graded in these examples is not fully understood [43] . By contrast , Ozbudak et al . showed both theoretically and experimentally that it is possible to interconvert binary and graded responses of the E . coli lac operon simply by titrating the Lac repressor LacI [44] . Recent reports suggest that binary and graded signaling modalities may coexist in cells [16] , [45] . Thus , Ras signaling in T lymphocytes is of a low-intensity , analog form , but can assume a high-intensity , digital form when an SOS positive feedback loop is activated . Here we suggest that low concentration of the Stat5 protein results in a binary response , while a high concentration generates a graded response . This model is consistent with the following data: ( i ) steeper dose/response curves in the Stat5+/− S3 , which contain less Stat5 , compared with wild-type S3 ( Figure 4B ) ; ( ii ) steeper dose/response curves in the more mature subsets of S3 ( Figure 3G ) , consistent with decreasing levels of Stat5 protein with maturation ( Figure 4D–E ) ; and ( iii ) gradual conversion from binary to graded responses in cells expressing increasing levels of transduced FLAG-Stat5 ( Figure 5C ) . The binary response of EpoR-HM may be explained within the same framework . Presumably , docking of Stat5 on EpoR phosphotyrosines increases Stat5 concentration in the immediate vicinity of the EpoR/Jak2 complex . The absence of EpoR phosphotyrosines in EpoR-HM might be expected to result in lower Stat5 concentration within the locality of EpoR/Jak2 and be functionally equivalent to low cellular Stat5 . Support for this comes from the fact that we can rescue graded signaling in EpoR-HM by transducing these cells with high FLAG-Stat5 ( Figure 5C , lower panels ) . It is unclear at this point how Stat5 concentration determines the steepness of the dose/response curve , a question that will form the focus of future work . Reduced levels of EpoR in EpoR+/− erythroblasts do not prevent the generation of a maximal p-Stat5 signal ( Figure 4C , Figure S8 ) . Further , the low-intensity p-Stat5 signal can be converted into a high-intensity signal by exogenous high expression of FLAG-Stat5 , consistent with EpoR expression in these cells not being limiting to the p-Stat5 signal ( Figure 5C–D ) . Estimates of the EpoR cell-surface occupancy required to generate the p-Stat5 response are consistent with the conclusion that cell surface EpoR is not limiting for this response . Using a value of 130 , 000 U of Epo per milligram [46] , a dissociation constant ( KD ) for Epo of 240 pmol/L [1] and Epo's molecular weight ( 34 , 000 Daltons ) , 50% occupancy will be seen at Epo concentrations of 1 U/ml . This is a much higher concentration than the apparent Km for generating the half-maximal p-Stat5 response , which we found to be between 0 . 06 and 0 . 15 U/ml ( Figures S3B , S7A , S7C ) . Assuming a hyperbolic binding curve for Epo , basal Epo levels ( 0 . 010–0 . 020 U/ml ) would occupy only 1%–2% of the cell surface EpoR , and an Epo concentration of 0 . 1 U/ml , generating half the maximal p-Stat5 response , would increase EpoR occupancy to 10% . At 35% occupancy , the p-Stat5 response is expected to be near-maximal in all erythroblast subsets . The very highest Epo levels , found for example in aplastic anemia , of 10 U/ml , result in 90% EpoR occupancy . This analysis suggests that cell-surface EpoR has vast reserves with respect to the generation of the p-Stat5 signal . We found that the maximal p-Stat5 signal intensity generated by a maximal Epo stimulus is largely determined by Stat5 protein levels ( Figures 4F , 5C–D ) , though it is also affected by high SOCS3 expression in mature erythroblasts ( Figure S6 ) . Michaelis-Menten enzyme kinetics assumes that the substrate is present in excess , and is therefore not applicable to Stat5 signaling in erythroblasts , where the substrate is limiting ( [31] , [32]; see Text S1 ) . This non-Michaelian behavior may explain recent reports linking higher Stat5 gene dosage or expression to leukemogenesis [47] , [48] . Thus , based on our findings , we suggest that the higher Stat5 protein found in leukemia cells may be causing a higher p-Stat5 signal , possibly activating gene targets that contribute to leukemogenesis . These considerations underscore the importance of identifying regulators of Stat5 expression both during normal erythroid differentiation and in leukemia . Our findings raise the possibility that there may be signaling pathways other than EpoR-Jak2-Stat5 in which the second messenger molecule , and not its upstream receptor , is limiting to the signal response . This non-Michaelian behavior has implications when such pathways are activated pathologically . To date , inhibition of abnormal signaling in tumor cells has largely focused on membrane or nuclear receptors and on other early or first steps of signaling cascades . Examples include the inhibition of the epidermal growth-factor ( EGF ) receptors , over-expressed in many solid tumors , and inhibition of Jak2 or Bcr-Abl in myeloproliferative disease and leukemia [49]–[52] . Our work suggests an alternative therapeutic paradigm , in which targeting second messengers that are limiting to signal transduction may be an effective therapeutic strategy . In the case of Stat5 , targeting its high-intensity signaling may inhibit its function in myeloproliferative disease without affecting the binary low-intensity p-Stat5 response in normal cells .
Fetal livers were isolated at E12 . 5–E14 . 5 , dissociated mechanically , and deprived of Epo for 90 min in the presence of 20% serum prior to Epo stimulation . Electroporations were performed using Amaxa Biosystem Nucleofector on fresh fetal liver . Cells were incubated for 18 h in Epo ( 0 . 2 U/mL ) , Stem Cell Factor ( 100 ng/mL ) , and Interleukin-3 ( 10 ng/mL ) and washed 3 times and incubated in 20% serum for 3 h prior to Epo stimulation . Epo-stimulated cells were harvested in phosphowash ( PBS , 1 mM sodium orthovanadate , 1 mM β-glycerol phosphate , 1 µg/mL microcystin ) , fixed in 1 . 6% paraformaldehyde , permeabilized in 80% acetone , and stored at −80°C . Thawed cells were stained in PBS/3% milk with AF647-conjugated anti-phospho Stat5 ( 612599 , BD Biosciences ) , for Ter119 and CD71 as described [21] , and where indicated , for Stat5 ( ab 7969 , Abcam followed by anti-rabbit-APC , Jackson ImmunoResearch Laboratories ) , FLAG ( F4049 , Sigma Aldrich ) , and Myc ( 2272 , Cell Signaling Technology ) . In all electroporation experiments , cells were stained with LIVE/DEAD Fixable Blue Dead Cell Stain Kit for UV excitation ( L-23105 , Invitrogen ) , prior to fixation and permeabilization in order to exclude dead cells from analysis . λ-phosphatase treatment was for 15 min at 37°C on fixed and permeabilized cells ( 1 , 000 units , New England Biolabs ) . Apoptosis assays were done on fresh fetal livers that were deprived of Epo for 90 min and then stained for CD71 , Ter119 , and Annexin V according to the manufacturer's instructions ( BD Biosciences ) . Spleen and bone marrow cells isolated from adult mice were immediately stained with CD71 and Ter119 as described [21] . Cells were analyzed on an LSRII cytometer ( BD Biosciences ) . Data were analyzed with FlowJo software ( Tree Star , Stanford University , Stanford , CA ) . For mouse strains , DNA constructs , quantitative RT-PCR , and si-RNA , see Text S2 . | Hormone signaling through the erythropoietin ( Epo ) pathway is required both for the continuous replacement of red blood cells ( RBCs ) that are lost through aging ( a process known as "basal erythropoiesis" ) and to boost tissue oxygen when bleeding , in anemia or at high altitude ( "stress erythropoiesis" ) . A key challenge lies in understanding how extracellular Epo concentration is translated into different intracellular signals that promote transcription of proteins that are specific to basal versus stress erythropoiesis . Binding of Epo to its receptor EpoR on the surface of an erythroblast ( the precursors of RBCs ) triggers the addition of phosphates to a target protein Stat5; the phosphorylated Stat5 becomes activated and induces transcription . We show that the dynamic properties of the Stat5 activation signal convey additional information that specifies either basal or stress responses . During basal conditions , the Stat5 signal is low and binary in nature—an on/off switch-like response . Stress , on the other hand , triggers a distinct Stat5 response consisting of a highintensity signal that increases in a graded fashion with rising Epo concentration . We found that a mouse bearing a truncated EpoR is restricted to the low-intensity binary Stat5 signal and correspondingly fails to initiate stress erythropoiesis . Ultimately , it is the Stat5 protein level in erythroblasts that determines their ability to generate the high-intensity graded Stat5 signal in response to high Epo . These findings have therapeutic potential: targeting Stat5's high-intensity graded signal may inhibit its aberrant function in blood cell cancers without affecting its important binary response in normal cells . | [
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] | 2012 | Stat5 Signaling Specifies Basal versus Stress Erythropoietic Responses through Distinct Binary and Graded Dynamic Modalities |
The mammalian hippocampus plays a crucial role in producing a cognitive map of space—an internalized representation of the animal’s environment . We have previously shown that it is possible to model this map formation using a topological framework , in which information about the environment is transmitted through the temporal organization of neuronal spiking activity , particularly those occasions in which the firing of different place cells overlaps . In this paper , we discuss how gamma rhythm , one of the main components of the extracellular electrical field potential affects the efficiency of place cell map formation . Using methods of algebraic topology and the maximal entropy principle , we demonstrate that gamma modulation synchronizes the spiking of dynamical cell assemblies , which enables learning a spatial map at faster timescales .
For years it has been assumed that the cognitive map is Cartesian , containing detailed information about locations , distances and angles . Undoubtedly , such information is provided by various brain regions , but increasing evidence suggests that the hippocampal map is topological in nature . For example , electrophysiological recordings in morphing environments demonstrate that the spatial order ( overlaps , adjacency and containment ) is preserved , even in the face of deformations of the environment that cause the place fields to stretch or change shape [10–14] . In other words , the sequential order of place cell activity induced by the animal’s moves through morphing environment remains invariant , at least within a certain range of geometric transformations [15–17] . This implies that place cell spiking encodes a rough-and-ready framework into which other brain regions integrate more detailed metrical information [17–21] . What sorts of neuronal computations could produce such a framework [22–27] ? The approach proposed in [28–30] exploits the connection between the place field map and the Alexandrov-Čech theorem , according to which the pattern of overlaps between regular spatial regions U1 , U2 , …UN covering a space X encodes the topological structure of X [32] . The construction suggested by the Alexandrov-Čech theorem is the following . If the regions Ui are represented as vertices , pairs of overlapping regions Ui ∩ Uj ≠ ∅ , as links between these vertices , the triples Ui ∩ Uj ∩ Uk ≠ ∅ as the facets between these links and so forth , then the resulting simplicial complex N is topologically equivalent to X ( see Glossary in the Methods section and S1 Fig ) . The fact that the place fields produce a dense cover of the environment suggests that the pattern of overlaps between them contains the information required to represent the environment’s topology , which we propose holds the key to the way in which the hippocampus encodes its topological map of a given space . Note that the domains where several place fields overlap are precisely the ones where the corresponding place cells cofire: the information about the overlap of place fields is represented via place cell coactivity , which suggests that the Alexandrov-Čech construction can be carried out not only via the geometric pattern of the place field overlaps , but also through analysis of place cell coactivities . The details of the topological model of the hippocampal map are discussed in [29 , 30] . In brief , the idea is to represent the combinations of coactive place cells ( c1 , c2 , … , cp ) as coactivity simplexes , σ = [c1 , c2 , … , cp]—combinatorial representations of multi-dimensional polyhedra ( see Methods ) . Together , these coactivity simplexes form a simplicial coactivity complex T σ . In this construction , the individual cell assemblies ( i . e . , a group of neurons that jointly drive a downstream readout neuron ) , provide local information about a given space; joined together into a neuronal ensemble ( i . e . , a population of cell assemblies ) , they represent the space as whole . By analogy , a collection of individual simplexes representing connected locations , together form a simplicial complex which represents environment as a whole . Numerical simulations demonstrate that T σ captures the topological structure of the environment and serves as a schematic representation of the hippocampal map [29 , 31] . For example , the sequences of place cell combinations ignited along the paths traversed by the animal are represented in T σ by chains of coactivity simplexes—the simplicial paths [33 , 34] . A non-contractible simplicial path may represent a navigational path that encircles a physical obstacle , whereas topologically trivial simplicial paths correspond to contractible routes in the physical space ( Fig 1A and 1B ) . The complex T σ begins to form as soon as the rat starts navigating . Every detected instance of place cell coactivity contributes a simplex to T σ . At the early stages of navigation , when only a few cells have time to produce spikes , the coactivity complex is small , fragmented , and contains many gaps ( in topological terms , “holes” ) , most of which do not represent physical obstacles in the environment . Such holes tend to disappear as spatial learning continues . Therefore , the minimal time , Tmin , after which the topology of T σ matches the topology of the environment , or more precisely , when the correct number of topological loops emerges , can be viewed as a theoretical estimate of the time required to learn the hippocampal map ( Fig 1C , [29 , 30] ) . An important property of the model is that the structure of the coactivity complex T σ and the time course of its formation during learning are sensitive to various parameters of the neuronal firing statistics , which allows us to study the effect that changes in any of these parameters ( e . g . , firing rate , place field size , number of neurons ) produce in the ability of the ensemble to correctly learn a space . For example , if the firing rate slows , the system can compensate with a change to the place field size or the number of neurons in the ensemble , but only up to a point: beyond certain limits , the assembly will not be able to learn efficiently , or even at all [29] . As another example , the oscillations of the extracellular electrical field potential , typically referred to as the local field potential ( LFP ) , are known to modulate place cells activity at several timescales: each place cell tends to spike within a small range of the phases of the theta component of the LFP ( θ , 4–12 Hz [35] ) , which depends on the distance that the animal has traveled into the corresponding place field . As a rat moves through the place field , the preferred θ-range of a place cell progressively decreases with each new θ-cycle , a phenomenon known as θ-phase precession [36] . The preferred θ-phases of different cells are additionally synchronized by the second major component of the LFP , the gamma rhythm ( γ , 30–80 Hz , [37] ) . In fact , the period of the more rapid γ-rhythm , Tγ , is believed to define the range of the preferred phases within the slower θ-rhythm; on average one θ-period , Tθ , contains about seven γ-cycles , Tθ ≈ 7Tγ ( see [38] and S2A Fig ) . Numerous experimental [39–43] and theoretical [38 , 44–47] studies demonstrate that both θ- and γ-waves play key roles in spatial , working , and episodic memory functions . Most theoretical analyses have addressed the way in which the γ-synchronization affects the informational contents of spiking in small networks or in individual cells , but the topological approach allows us to model the formation of cognitive map as a whole . For example , it was used in [30] to demonstrate that θ-precession makes otherwise poorly-performing ensembles more efficient at spatial learning . The present analysis applies the topological model to study the effect of γ-waves on spatial learning and to demonstrate that γ-synchronization of place cell spiking activity enables the encoding or retrieval of large-scale spatial representations of the environment by integrating place cell coactivity at a synaptic timescale . Computational modeling of θ-phase precession is relatively straightforward . At a basic level , it amounts to imposing a particular relationship between a place cell’s spiking probability , the phase of the θ-wave and the distance that the animal has traveled into the corresponding place field [48] ( see Methods ) . The effects of the γ-rhythm are , however , more diverse . Electrophysiological experiments suggest that there exist at least two types of place cells: “TroPyr” cells that spike at the trough of the fast γ-wave ( 50–80 Hz ) and “RisPyr” cells that fire at the rising phase of slow γ-waves , overriding θ-precession [49–51] . Although we can model both S2 Fig with our approach ( see Methods ) , in the following we will model only the TroPyr cells that exhibit more robust firing patterns and higher firing rates , and therefore may play a primary role in producing the cognitive map [29 , 30] .
In the above discussion , the central construction of the model , which is the coactivity complex T σ , was introduced as a schematic representation of the place field map [31] . However , as shown in [67 , 68] , a coactivity complex can be built not only by detecting higher-order cofiring events that directly mark the locations where several place field overlap , but also by integrating the information provided by the lower-order place cell coactivity . Physiologically , the latter option corresponds to the cell assembly network in which the readout neurons integrate lower-order coactivity inputs over a working or intermediate memory timescale , rather than merely react to cofiring as all-or-none coactivity detectors [69 , 70] . To model a network of cell assemblies driving a population of input-integrator readout neurons , we used the following approach . First , we detected the lowest-order , pairwise place cell coactivities and used them to build a connectivity graph G ( see [67] and S6 Fig ) . Fully interconnected subgraphs of G are called cliques ( see Methods ) ; cliques of G are identified with the simplexes of a new clique coactivity complex T ς . A key property of this algorithm is that the connections constituting a clique or a simplex do not have to be detected simultaneously but can be accumulated over an extended period of time . For physiological accuracy , we restrict this period to 10 mins or less , which results in a coactivity complex whose simplexes emerge over working or intermediate memory intervals . Although the algorithms for constructing temporal simplicial and clique complexes seem quite different , the actual difference between these two coactivity complexes is not as large . First , as shown in [31 , 67] , most simplexes of T ς correspond to the simplexes of T σ and vice- versa: the identities of the cell assemblies are largely the same , only the time course of their construction changes . Furthermore , the topological structures of these complexes are quite close . Second , most pairwise connections within the cliques of G are produced almost simultaneously while the rat traverses the region where several place fields overlap . In other words , most cliques appear at once , just as the simplexes do , and only a relatively small number of the maximal cliques are actually “corrected” over time [68] . Nevertheless , this effect does improve the overall performance of the clique coactivity complexes , which typically produce far fewer spurious topological loops and shorter learning times Tmin than those produced by simplicial coactivity complexes . Implementing the γ-synchronization mechanism in an integrator model yields the results illustrated in Fig 4 . First , the structure of the graphs on Figs 3A and 4A is qualitatively similar , though the pool of third-order cliques is slightly larger than the pool of 2D simplexes . This is because not every clique makes a simultaneous appearance as a simplex , but every simplex can be viewed as an instantly detected clique . The behaviors of the topological loops in T ς and in T σ , shown in Figs 3B and 4B are similar as well . The γ-synchronization reduces the number of cold , spurious loops in both types of complexes ( S7 Fig ) . Physiologically , this implies that a γ-rhythm produces the same organizing effect on the activity of cell assembly network , whether the latter is based on a coincidence detector or on the input integrator readout neurons . However , it should be noted that , for all βs , the number of loops in T ς is smaller than in T σ by an order of magnitude , illustrating the efficiency of the input integrating readout neurons . Most importantly , the integrator complex T ς produces finite learning times at the γ-timescale integration window , w ≈ Tγ . This demonstrates that the hippocampal network can produce a spatial map by reading out γ-synchronized place cell coactivity at the γ-timescale and accumulating the coactivities over the timescale of working or intermediate memory .
It has long been established that both θ and γ rhythms correlate strongly with the capacity for learning and memory , but the mechanisms by which they influence cognitive functions has remained unclear . These extracellular fields define the timescale of place cell coactivity , thereby controlling the “parcellation” of the information flow received by downstream networks . In particular , the synchronization of the processes taking place at the synaptic timescale , such as the processes controlled by the membrane time constant , the duration of receptor-mediated postsynaptic spike potentials , the rate of spike-timing dependent plasticity , and so forth , [71–73] is manifested at the network level as γ-frequency oscillations [74–78] . Processes that involve slower forms of synaptic plasticity , including slow-changing spiking thresholds [79–82] , synchronize at the θ-frequency timescale . As a result , θ-oscillations provide lower-resolution temporal packaging of place cell coactivity [66 , 83 , 84] , integrating spiking inputs from several cell assemblies over one or more θ-periods [85–87] . Our previous model , based on place cells that are independently θ-precessing , provided a self-consistent description of the hippocampal network’s function at the θ-timescale , which predicted an optimal integration window for reading out the information within the θ-range [30] . However , as the integration window became smaller , the spatial map encoded by independently precessing place cells failed to achieve correct spatial representation , which suggested to us the importance of additional synchronization at the γ-timescale . Here we developed a phenomenological model based on the assumption that the γ-rhythm not only controls the probability of the cell assembly spiking but also defines the temporal spread of the spikes produced by the cell assemblies around the troughs of the γ-wave . As a result , the model predicts that if the preferred θ-phases synchronize with the γ-troughs , then topological information about the given environment can be readily captured by integrating place cell coactivity at the γ-timescale . Thus , γ-synchronization of spiking activity is crucial for both encoding and reading out information from the cell assemblies arriving in “γ-packets” [61] . This result suggests a possible phenomenological explanation as to why reduction of the γ-wave amplitude correlates with impairments in learning , whether the cause is changes in the network’s synaptic physiology [88–91] , psychoactive drugs [92–94] , neurodegeneration , or aging [95 , 96] , whereas an increase of the γ-amplitude correlates with successful learning and retrieval of the learned information [97–101] . According to our model , reducing either the γ-amplitude or the diffused coupling between the γ-rhythm and place cell spiking activity , the latter being equivalent to lowering β , should increase learning times and lower the success rate in constructing topologically accurate cognitive maps . Vice versa , high γ-amplitude and strong coupling between the spike times and the γ-wave should result in more effective spatial learning .
An abstract simplex of order d , σd , is a set of ( d + 1 ) elements , e . g . , a set of ( d + 1 ) active cells . Note that the subsets of the set σd form subsimplexes of σd and that a nonempty overlap of any two simplexes σ 1 d and σ 2 d is a subsimplex of both σ 1 d and σ 2 d . A simplicial complex Σσ is a family of simplexes . The elements of a simplex σd can be visualized as vertices of d-dimensional polytopes: σ0 can be visualized as a point , σ1 as the ends of a line segment , σ2 as the vertices of a triangle , σ3 as the vertices of a tetrahedron , etc . [102] . A clique in a graph G is a set of fully interconnected vertices ( i . e . , a complete graph ) . Combinatorially , cliques have the same key properties as the abstract simplexes: any subcollection of vertices in a clique is fully interconnected , and hence forms a subclique . A nonempty overlap of two cliques ς 1 d and ς 2 d is a subclique in both ς 1 d and ς 2 d . Therefore , cliques define abstract simplexes and thus the collection of cliques in a graph G defines a clique simplicial complex Σς ( G ) . In [30] we showed that the time required to learn a large spatial environment is approximately equal to sum of times required to learn its parts . Therefore , we simulated a non-preferential exploratory behavior in a small planar environment ( 1m × 1m ) shown in Fig 1A , similar to those used in electrophysiological experiments [103] . The Poisson spiking rate of a place cell c at a point r ( t ) = ( x ( t ) , y ( t ) ) is given by λ c ( r ) = f c e - ( r - r c ) 2 2 s c 2 where fc is the maximal firing rate and sc defines the size of the place field centered at rc = ( xc , yc ) . The set of scs and fcs in an ensemble of N place cells are lognormally distributed around a certain ensemble-mean firing rate f and a certain ensemble-mean place field size s , with the variances σf = af and σs = bs , respectively . Thus , a place cell ensemble is described by a triplet of parameters: ( s , f , N ) [29] . As the rat moves over a distance l ( t ) into the place field of a cell c , the preferred spiking phase is φ θ , c ( t ) ≈ 2 π ( 1 - l ( t ) / L c ) , where Lc ∼ 3sc is the size of the place field [36 , 104] . To simulate the coupling between the firing rate and the θ-phase , we modulated the original Gaussian firing rate by a θ-factor Λθ , c ( φ ) , giving Λ θ , c ( φ ) = e - ( φ - φ θ , c ( t ) ) 2 2 ε c 2 , using the θ-component of the LFP recorded in wild type rats . The width ε of the Gaussian was defined in [30] to be the ratio of the mean distance that rat travels during one θ-cycle to the size of the place field , ε = 2πv/Lωθ , where v is the rat’s speed and ωθ/2π is the frequency of the θ-signal . To incorporate the γ-rhythm into our model , we extracted the 30–80 Hz frequency band from the same LFP signal so that all the existing correlations between θ and γ waves are preserved , then we shifted the simulated place cell spiking times towards the troughs of γ amplitude by modulating their respective spiking rates with the additional Boltzmann factor [58] , Λ γ ( t ) ∼ e - β γ A γ ( t ) , ( 1 ) where Aγ ( t ) is the amplitude of the γ-wave and 1/βγ is a formal parameter that plays the role of the effective temperature [105] ( Fig 2 ) . Simulating the net firing rate as a product of all three factors λ n e t = λ c ( x , y ) Λ θ , c ( φ ) Λ γ ( A γ ) preserves spatial selectivity of spiking and the θ-precession ( S8 Fig ) and forces the preferred phases of the θ-phase precession φc into the γ-cycles , in accordance with the θ-γ theory [38 , 41 , 56] . In a vicinity of the ith trough , the gamma signal has the form A γ ( t ) ≈ A γ , 0 - A γ , i cos ( ω i t ) ≈ a γ , i + A γ , i ω i 2 t 2 2 , ( 2 ) where the parameters Aγ , 0 are the mean amplitude of Aγ; Aγ , i and ωi are its instantaneous amplitude and frequency at the ith trough aγ , i = Aγ , 0 − Aγ , i and the index i runs over all troughs i ∈ I . Using the expansion Eq ( 2 ) in Eq ( 1 ) allows estimating the spread Δi of the spikes around the ith through from the Gaussian variance as Δ i 2 = 1 β γ A γ , i ω i 2 . ( 3 ) A priori , in order to accurately define the temporal spread of spikes produced by different cell assemblies at different times , the inverse effective temperature should be trough-specific , βγ , i . However , we consider a simplified case in which the average βγ = 〈βγ , i〉i ∈ I defines the coupling between the γ-wave and the place cell spike times across the entire hippocampal network . The variance Eq ( 3 ) is about six times smaller than the instantaneous period , i . e . , 6 β γ A γ , i ω i ≈ 2 π ω i , which implies that the effective temperature is approximately equal to the mean γ-amplitude 1 β γ ≈ A γ , where Aγ = 〈Aγ , i〉i ∈ I . By normalizing Aγ with the standard deviation σ γ = 〈 A γ 2 ( t ) - A γ , 0 2 〉 t , A = Aγ/σγ , we obtain the scaled parameter β = βγ σγ , with the characteristic value β = 1 A . The described approach can be applied to both the TroPyr and RisPyr cells . Mathematically , the “rising phases of γ” controlling the spiking of RisPyr cells correspond to the vicinities of peaks of the time derivatives of the γ-amplitude . Therefore , the spiking probability of RisPyr cells can be constrained by a factor similar to Eq ( 1 ) , involving the derivative of the γ-amplitude , A′ ( t ) , which would override the θ-precession constraint ( Λθ , c ( φ ) = 1 ) in the vicinity of the A′ ( t ) -peaks . The analysis of the mixed ( RisPyr and TroPyr ) ensembles is more complex and sui generis . Mathematical methods required for this study are based on Persistent Homology Theory ( see [29] and [106 , 107] ) implemented by the “JPlex” freeware package [108] . | One of the goals of theoretical systems neuroscience is to connect parameters of neuronal activity observed in electrophysiological experiments , such as cell firing rates , frequencies and amplitudes of the brain rhythms , with cognitive phenomena that emerge at the scale of large groups of cells . In previous work , we proposed an approach for modeling one such phenomenon: spatial learning in the mammalian hippocampus . This approach rested on the hypothesis that hippocampal neurons encode a rough-and-ready topological map of an environment , whereas geometric details likely come from multiple inputs from other brain regions . A key property of this model is that it allows us to estimate the effect produced by different parameters of neuronal activity on spatial learning . In particular , it we showed that theta oscillations strongly enhance the ability of the place cell ensembles to learn topologically accurate spatial maps . In this work , we show that synchronization of neuronal spiking activity by the other major component of the oscillating extracellular cell potential—the gamma rhythm—also enhances both spatial learning and the retrieval of spatial memories at the physiological timescale . | [
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] | 2016 | Gamma Synchronization Influences Map Formation Time in a Topological Model of Spatial Learning |
Post-operative recurrence in mycetoma after adequate medical and surgical treatment is common and a serious problem . It has health , socio-economic and psychological detrimental effects on patients and families . It is with this in mind , we set out to determine the predictors of post-operative recurrence in mycetoma . The study included 1013 patients with Madurella mycetomatis causing eumycetoma who underwent surgical excision at the Mycetoma Research Centre , Khartoum , Sudan in the period 1991–2015 . The clinical records of these patients were reviewed and relevant information was collected using a pre-designed data collection sheet . The study showed , 276 patients ( 27 . 2% ) of the studied population developed post-operative recurrence , 217 were males ( 78 . 6% ) and 59 were females ( 21 . 4% ) . Their age ranged between 5 to 70 years with a mean of 32 years . The disease duration at presentation ranged between 2 months and 17 years . The majority of the patients 118 ( 42 . 8% ) had mycetoma of 1 year duration . In this study , students were the most affected; 105 ( 38% ) followed by workers 70 ( 25 . 4% ) , then farmers 48 ( 17 . 3% ) . The majority of the patients were from the Central Sudan 207 ( 75% ) , Western Sudan 53 ( 19 . 2% ) while 11 patients ( 4% ) were from the Northern part . Past history of surgical intervention performed elsewhere was reported in 196 patients ( 71 . 1% ) . Family history of mycetoma was reported in 50 patients ( 18 . 1% ) . The foot was the most affected site , 245 ( 88 . 7% ) , followed by the hand seen in 19 ( 6 . 8% ) patients and 44 ( 4 . 5% ) had different sites involvement . Most of the patients 258 ( 93 . 5% ) had wide local surgical excisions while 18 had major amputation . The model predicted that the certain groups have a high risk of recurrence , and these include patients with disease duration greater than 10 years and extra-pedal mycetoma . Patients with disease duration between [5–10] years , with pedal mycetoma , who had previous surgery , with positive family history and underwent wide local surgical excision . Patients with disease duration [5–10] years , with pedal mycetoma , had previous surgery , with no family history but presented with a disease size ( > 10 cm ) , were non- farmers and underwent wide local surgical excision . Other groups are patients with disease duration ( ≤5 years ) , with pedal mycetoma , age <59 years , living in the Western /Eastern / Southern regions of the Sudan and with positive family history and had wide local surgical excision . Also included patients with disease duration ( ≤5 years ) , with pedal mycetoma , aged <59 years , living in the northern or central region , with no family history but presented with a disease size >10 cm , working as farmers or students and underwent wide local surgical excision . In conclusion , these groups of patients need special care to reduce the incidence of post-operative recurrence with its morbidity and detrimental consequences . In depth studies for the other predisposing factors for post-operative recurrence such as genetic , immunological and environmental factors are needed .
Eumycetoma is a chronic granulomatous destructive and mutilating subcutaneous fungal infection [1 , 2] . The disease is endemic in many tropical and subtropical regions in what is known as the Mycetoma Belt [3 , 4] . It has many medical , health , socio-economic detrimental bearings on the affected patients and communities [5 , 6] . Currently there are no accurate data on its prevalence and incidence globally , likewise the infection route , susceptibility or resistance [7 , 8] . The patient usually presents with small painless subcutaneous mass which gradually increases in size and spreads along the different tissue planes which eventually causes massive damage , destruction and loss of function of the affected body parts [9 , 10] . The extremities are affected most but any site can be affected [11 , 12] . Effective management of mycetoma depends mainly on accurate diagnosis . This in turn depends on identification of the type of mycetoma and extent of the disease through a meticulous clinical interview , clinical examinations and a battery of investigations . The later includes various imaging techniques , organism identification using grains culture , phenotypic morphological identification , molecular techniques and cyto-histopathological identification [13 , 14 , 15 , 16] . The management usually involves a combination of surgery and prolonged antifungal therapy [17] . The surgical treatment ranges from wide local surgical excision ( WLE ) , repeated debridement and amputation . Early small lesions are amenable to cure with good prognosis . However , the majority of patients present late with advanced disease and such patients are difficult to cure and frequently relapse after apparently adequate treatment with a high morbidly [18 , 19 , 20] . Post-operative recurrence in mycetoma is a frequent problem and its explanation is an enigma . It has many impacts on the patients and health authorities in endemic areas . With this background , this study was conducted to understand the clinical predictors of post-operative recurrence of eumycetoma . It also aims to identify the interactions between the different predictive factors in an attempt to develop a predictive model for eumycetoma post-operative recurrence based on the most important clinical factors identified . However , this study is presented with some limitation such as the retrospective and the single-center experience nature .
The association between clinical factors and mycetoma post-operative recurrence as a target/ outcome variable was investigated . Clinical predictive factors were selected and reformatted using the available domain knowledge provided by expertise in the field of mycetoma . These factors were: age , gender , residence , disease site and duration in years , occupation , family history , previous surgery and type of surgery . These characteristics are shown in Table 1 . In this study , missing data were small and arbitrary ( less than 5% ) . Therefore , the Markov chain Monte Carlo ( MCMC ) method was used assuming a multivariate normality [21] . In this study , machine learning algorithms; Decision Tree ( DT ) and Random Forest ( RF ) were utilized for predicting mycetoma post-operative recurrence assuming unknown data mechanism [22] . Data was partitioned as 70% for training the algorithms with the remaining 30% of the data kept for the validation purpose . Models were trained with 5-fold cross-validation to avoid model over fitting and to ensure model stability [23] . Model performance was evaluated using model accuracy , Positive Predictive Value ( PPV ) , Negative Predictive Value ( NPV ) and Area under the Receiver Characteristic Curve ( AUC ) [24] . AUC measures the discrimination ability of the model on predicting the class levels of target variable based on the predictive factors . It is a single scalar value that represents the excepted performance of receiver operating characteristic ( ROC ) curve .
The study included 1013 patients with Madurella mycetomatis caused eumycetoma patients who underwent surgical treatment at the Mycetoma Research Centre , Khartoum , Sudan in the period 1991–2015 . The study documented that , 276 patients ( 27 . 2% ) developed post-operative recurrence , [Table 1] . The study population were 727 males ( 71 . 8% ) and 286 females ( 28 . 2% ) . Their age ranged between 5 and 70 years with a median of 23 years . Most of the patients , 625 ( 61 . 7% ) were in the age group 18–39 years , 199 ( 18 . 6% ) in the age group 31–59 years and 169 ( 16 . 7% ) were less than 18 years old at presentation . The disease duration at presentation ranged between 2 months and 17 years . The majority of patients , 839 ( 82 . 8% ) had mycetoma of less than five years duration , 171 ( 14 . 3% ) had mycetoma with a duration ranged between 5 to 10 years and 29 ( 3% ) had mycetoma for more than 10 years . In this study , students were affected most; 398 ( 39% ) followed by farmers 156 ( 15 . 4% ) . The majority of the patients were from the Central Sudan 817 ( 80 . 7% ) , Western Sudan 117 ( 11 . 5% ) while 48 patients ( 4 . 7% ) were from the Northern part . The history of previous surgical treatment performed elsewhere was reported in 566 patients ( 56% ) . Family history of mycetoma was reported in 132 patients ( 13% ) . The foot 868 ( 85 . 7% ) was the most affected site followed by the hand seen in 84 patients ( 8 . 3 ) and 61 patients ( 6% ) had mycetoma at different parts . Most of the patients , 962 ( 95% ) had wide local surgical excisions while 51 patients ( 5% ) had major amputation . The amputation included above and below knee , below elbow and Syem’s amputations . The study showed that , 276 patients ( 27 . 2% ) developed post-operative recurrence of whom 217 were males ( 78 . 6% ) and 59 were females ( 21 . 4% ) . Their age ranged between 5 year and 70 years with a mean of 32 years [Table 1] . The disease duration at presentation ranged between 4 months and 19 years . The majority of the patients 118 ( 42 . 8% ) had mycetoma of 1 year duration . In this study , students were affected most; 105 ( 38% ) , followed by workers 70 ( 25 . 4% ) , then farmers 48 ( 17 . 3% ) . The majority of the patients were from the Central Sudan 207 ( 75% ) , Western Sudan 53 ( 19 . 2% ) while 11 patients ( 4% ) were from the Northern part . Past history of surgical intervention performed elsewhere was reported in 196 patients ( 71 . 1% ) . Family history of mycetoma was reported in 50 patients ( 18 . 1% ) . The foot was the most affected site 245 ( 88 . 7% ) followed by the hand seen in 19 ( 6 . 8% ) patients and 12 ( 4 . 5% ) had mycetoma at different parts . 258 ( 93 . 5% ) of the patients had wide local surgical excisions while the rest of patients had major amputation . Amputation included above and below knee , below elbow and Syem’s amputations .
Ethical clearance was obtained from Soba Hospital Ethical Committee . Patients’ informed consents proved to be unnecessary in this study . | Post-operative recurrence in mycetoma is a thoughtful problem . It has numerous undesirable medical , health , socio-economic and psychological impacts on the affected patients and their families , communities and health authorities in endemic regions . It is an important motive for patients to drop out follow up and treatment incompliance and hence the inclination of patients for traditional medical treatment . The factors predicating this phenomenon were not studied previously . However , patients’ characteristics and clinical presentation can partially offer an explanation . Thus the present study was set out to understand the predictive ability of some clinical factors on predicting the post-operative recurrence of eumycetoma . The present study had showed young farmers with small sized pedal mycetoma , with short disease duration , who are residing in endemic areas , with no family history and who underwent wide local excision are most likely to remain disease free . We can also concluded that , adequate surgical treatment conditions are obligatory to achieve good outcome and to reduce recurrence . Appropriate health education programmes to encourage early presentation to medical care are essential to reduce the postoperative recurrence rate with its detrimental impacts . | [
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] | 2016 | Predictors of Post-operative Mycetoma Recurrence Using Machine-Learning Algorithms: The Mycetoma Research Center Experience |
Assembly and release of human immunodeficiency virus ( HIV ) occur at the plasma membrane of infected cells and are driven by the Gag polyprotein . Previous studies analyzed viral morphogenesis using biochemical methods and static images , while dynamic and kinetic information has been lacking until very recently . Using a combination of wide-field and total internal reflection fluorescence microscopy , we have investigated the assembly and release of fluorescently labeled HIV-1 at the plasma membrane of living cells with high time resolution . Gag assembled into discrete clusters corresponding to single virions . Formation of multiple particles from the same site was rarely observed . Using a photoconvertible fluorescent protein fused to Gag , we determined that assembly was nucleated preferentially by Gag molecules that had recently attached to the plasma membrane or arrived directly from the cytosol . Both membrane-bound and cytosol derived Gag polyproteins contributed to the growing bud . After their initial appearance , assembly sites accumulated at the plasma membrane of individual cells over 1–2 hours . Assembly kinetics were rapid: the number of Gag molecules at a budding site increased , following a saturating exponential with a rate constant of ∼5×10−3 s−1 , corresponding to 8–9 min for 90% completion of assembly for a single virion . Release of extracellular particles was observed at ∼1 , 500±700 s after the onset of assembly . The ability of the virus to recruit components of the cellular ESCRT machinery or to undergo proteolytic maturation , or the absence of Vpu did not significantly alter the assembly kinetics .
Assembly and release of progeny virions are fundamental steps in viral replication . In the case of retroviruses , such as human immunodeficiency virus type 1 ( HIV-1 ) , the viral structural polyprotein Gag plays a central role in mediating both of these processes which occur concomitantly at the plasma membrane of the infected cell . Gag comprises domains required for membrane binding , multimerization , nucleic acid binding as well as for interaction with the host cell derived budding machinery and has been demonstrated to direct the formation of virus like particles ( VLP ) in the absence of other viral proteins [1] . During or shortly after virus release , Gag is cleaved by the viral protease resulting in domain separation and maturation of the infectious virion ( for review see [2] , [3] , [4] ) . The assembly process , leading from monomeric Gag molecules translated at cytoplasmic polysomes to the virus bud comprising several thousand Gag molecules at the plasma membrane , has been investigated using a variety of techniques . Results from pulse-chase labeling , density gradient fractionation , time-lapse fluorescence imaging and intracellular fluorescence resonance energy transfer measurements have resulted in a current general view of retroviral assembly: Gag is rapidly converted from a soluble form to a multimeric , membrane associated complex and this process is reflected by changes in the distribution of Gag within the cell ( e . g . [5] , [6] , [7] , [8] , [9] , [10] ) . Initial Gag-Gag interactions occur prior to arrival at the plasma membrane , while assembly of higher order structures appears to be confined to the plasma membrane . At the membrane , Gag localizes to or induces membrane microdomains from which budding and release are believed to occur ( e . g . [11] , [12] , [13] , [14] ) . All these steps may be influenced by cellular factors , but these factors and their mechanistic role are currently poorly understood ( reviewed in [2] , [15] ) . Completion of budding and release require the cellular ESCRT machinery , normally involved in endosomal sorting and cytokinesis ( reviewed in [16] , [17] , [18] ) . While the general pathway of HIV morphogenesis has been elucidated in recent years , several aspects are still controversial and the details of this process and in particular its kinetics are poorly understood . Oligo- or multimers of Gag have been detected in the cytoplasm and at intracellular membranes [10] , [19] and are believed to be assembly intermediates , but the size of Gag complexes added to a growing bud and their route of trafficking are currently not known . It is also unclear , whether Gag molecules are recruited from the cytoplasm or an intracellular membrane to the budding site or whether they first associate with the plasma membrane and are then organized into a budding structure by lateral movement . Budding may occur from platforms giving rise to multiple consecutive budding events , as has been reported for Rous sarcoma virus [20] , or may occur mostly from unique sites , as suggested for Moloney murine leukemia virus [21] . Finally , the kinetics of assembly have only recently been described [22] and kinetics of release are unknown at present . While traditional biochemical and cell biology techniques are not well suited to address these aspects , insight can be gained from direct visualization of budding site formation at the plasma membrane with high time resolution . Such studies are now possible due to recent advances in technology . Gladnikoff et al . applied atomic force microscopy to monitor retroviral budding [21] , albeit with a technically limited time resolution of several minutes per frame . Atomic force microscopy is only suited for the analysis of budding , however , since it measures distortion of the membrane , while analysis of the initial nucleation complex and early assembly requires other techniques . To investigate the kinetics of HIV assembly at high time resolution , we employed a combination of wide-field ( WF ) and total internal reflection fluorescence ( TIRF ) microscopy in conjunction with fluorescently labeled and photoconvertible HIV-1 derivatives . Jouvenet et al . also analyzed the dynamics of VLP formation by fluorescently labeled Gag polyproteins and reported assembly of Gag complexes to occur within minutes [22] . Here , we confirm and extend this analysis by investigating the dynamics of HIV-1 assembly in more detail , using a complete viral plasmid expressing all HIV-1 proteins except Nef and determining the time until a complete virion is released . Virions assembled at individual sites and rarely appeared to bud from larger Gag platforms . The rate of assembly was similar for wild-type HIV and for variants that lack the PTAP late domain motif or are deficient in protease activity . Using a recently developed photoconvertible protein , it was found that nucleation of the assembly site and bud growth occurred preferentially from cytosolic Gag molecules . The average time from the appearance of an assembly site at the plasma membrane to release was 1 , 500±700 seconds .
The HIV-1 Gag protein is the main driving force for virion assembly and Gag by itself can assemble into virus-like particles which are released from expressing cells [1] . However , the details of the assembly and release processes may be influenced by other virus components . Furthermore , it has been demonstrated that the steps of Gag targeting and release may be influenced by the nuclear export pathway of its encoding mRNA , which in the case of HIV is mediated by the virus encoded protein Rev [23] . For this reason , we believe that Gag analyzed in a viral context will most accurately reflect the kinetics of HIV-1 assembly and release in living cells . Thus , we made use of a fluorescently labeled HIV-1 derivative with the egfp gene inserted between the MA and CA coding regions of the gag gene . Cells transfected with an equimolar mixture of this construct and its wild-type ( wt ) counterpart produce highly fluorescent particles displaying wt morphology and infectivity [24] . For the microscopic analyses described here , non-infectious derivatives lacking the long terminal repeat regions [25] were used . These constructs express Gag in the viral context , i . e . in the presence of all other HIV proteins ( except for Nef ) and dependent on Rev mediated nuclear export of the encoding RNA . For these reasons , Gag accumulation is slower and overall Gag expression levels are much lower than for the Rev-independent , codon-optimized version of Gag alone used in a previous study [22] . For our studies , time points between 20 and 30 h post transfection ( hpt ) were found to be optimal for microscopic observation of budding site formation in HeLa cells . Jouvenet et al . have demonstrated the capability of investigating the assembly of HIV-1 with TIRFM [22] . In order to gain quantitative information over the assembly process , it is necessary to distinguish between changes in the fluorescence intensity arising from the incorporation of new fluorescently labeled Gag particles and axial movements of the complex in the exponentially decaying evanescent field of the TIRF microscope [26] . To this end , we have constructed a custom-made microscope capable of switching between TIRF and WF microscopy modes in alternating frames up to frame rates of 30 Hz . A diagram of the experimental setup is shown in Figure S1 and details can be found in the materials and methods and supporting information . Figure 1A and B shows a cell producing fluorescent HIV-1 imaged by WF and TIRF microscopy , respectively . Individual punctate clusters of Gag . eGFP were detected and tracked in TIRF mode and the corresponding fluorescence of the punctae in WF was determined . As can be seen in Video S1 , the fluorescent punctae were typically mobile . In order to perform a quantitative , statistically meaningful analysis , software was developed to semi-automatically track large numbers of individual punctae in up to three different color channels or different microscopy modes . The background-corrected intensity , particle location , and instantaneous velocities of the individual assembly complexes were calculated and analyzed ( see also SI and ref [27] ) . In the time window of 20–30 hpt chosen for our analysis , the extent of Gag expression varied strongly from cell to cell . Some cells were already displaying bright fluorescence at 20 hpt while others showed detectable fluorescent Gag expression only towards the end of the observation period . The appearance of Gag punctae in our system did not correlate with the level of Gag expression in contrast to the results obtained for Gag . GFP in the previous study by Jouvenet et al [22] who reported a gradual change in the rate of assembly of Gag complexes with time . This difference may be due to the faster and higher expression of this codon-optimized Gag encoding construct used in the previous study and possibly also to its Rev-independence . Some cells exhibited bright cytosolic fluorescence over several hours without showing formation of punctate clusters . Hence , we selected cells where the first punctae at the plasma membrane became detectable at the beginning of the observation period and measured the dynamics of assembly and budding over 1 to 2 hours with a temporal resolution of 2 s/frame . Punctate clusters rapidly accumulated at the plasma membrane within approximately one hour after the initial detection of individual nucleation sites ( Figure S2 ) . At later time points , the cell membrane became densely packed with punctate clusters , which made tracking of individual clusters problematic . Cotransfection of constructs expressing Gag . mCherry and the early endosome marker GFP . Rab5 revealed no colocalization of Gag . mCherry punctae with early endosomes during our observation window ( data not shown ) . This is consistent with the fact that the constructs used express the viral protein Vpu , which is able to counteract attachment and endocytosis of newly produced virions proposed to be mediated by cellular restriction factors in HeLa cells [28] , [29] , [30] . To determine whether the observed punctae represent individual nascent virions or larger accumulations of Gag molecules serving as budding platforms , we estimated the overall size of Gag . eGFP punctae from their relative fluorescence intensity . The fluorescence intensity distribution of punctate assembly sites was compared to that of particles released during the live-cell imaging experiments , which were found attached to the cover slip between HIV-1 producing cells , as well as to individual particles from a purified HIVeGFP preparation ( Figure 1C ) . We have previously established that purified HIVeGFP preparations consist of a single particle population with an average diameter of 172±10 nm and do not contain large aggregates [31] . However , the fluorescence intensities of individual particles are broadly distributed corresponding to the variability of particle diameters and total number of Gag molecules per virion [32] , [33] . Recent results further suggest that Gag stoichiometry also varies with the amount of Gag produced per cell [33] . Furthermore , the co-expression system used in this study can result in variations in the ratio of labelled to unlabelled Gag between different cells . Accordingly , differences in average particle intensities between individual cells as well as between experiments were observed . However , the fluorescence intensity of membrane associated Gag clusters was consistently less than or similar to that of free particles bound to the coverslip during the course of the imaging experiment or of particles purified from the supernatant of virus producing cells ( Figure 1C ) . This suggests that the Gag . eGFP assemblies in the stationary phase ( phase II discussed in the next section ) contain roughly the same amount of Gag as individual released particles and do not represent larger assembly patches but rather individual virus buds . This conclusion is supported by the observation that appearance of multiple particles from the same site was very rare . Video S2 demonstrates the one instance in several hundred events analyzed where multiple particles appeared to bud from a single site . Similar results have been reported for Rous sarcoma virus using two-photon live-cell imaging [20] and for Moloney murine leukemia virus using atomic force microscopy [21] . Recently , Manley et al . measured the movement of HIV-1 Gag fused to a tandem dimer of EosFP using photoactivated localization microscopy [34] and reported that immobile Gag molecules were often found in clusters with a radius of less than 300 nm , which also argues against the formation of large Gag assemblies serving as budding platforms . Having established that Gag clusters correspond to individual assembly sites , we investigated the kinetics of the assembly process . Figure 2A and B display the appearance of two individual fluorescent clusters ( indicated by arrows in Figure 1A and B ) of Gag . eGFP above the cytosolic background . Fluorescence intensities of individual clusters were found to change in three characteristic phases . Formation of the budding site was observed as a rapid increase in fluorescence intensity during the first few minutes after initial detection ( Phase I ) , followed by a stationary phase where the fluorescence signal fluctuates about a constant value ( Phase II ) . The duration of this plateau phase varied between individual clusters . Subsequently , a decay of fluorescence intensity was observed ( Phase III ) . During phase I , three types of kinetic behavior were observed: a saturating exponential ( in ∼80% of all cases ) , a linear increase in intensity until saturation was reached ( 10–20% ) or intensities that increased exponentially until reaching a plateau ( <3% ) ( Figure S3 ) . Here , we concentrate on the most prevalent behavior , the saturating exponential . To determine the kinetics of assembly , we averaged 309 individual traces with a minimal length of 300 frames , corresponding to 10 min ( Figure 3A ) . The asynchronous initiation of assembly sites was compensated for by aligning the intensity traces before averaging . Details of the averaging process are given in the Supporting Text S1 . The average value ( black line ) and the standard deviation ( light grey halo ) of the selected traces are plotted for measurements on single cells . In contrast to Jouvenet et al . [22] , who measured the time between the onset of assembly and saturation , we describe the budding kinetics in terms of a model function . The red line displays the fit of the data to a saturating exponential function , ( 1 ) where AI is the maximum fluorescence amplitude , kI is the rate of fluorescence increase and t0 is the time when assembly begins . By averaging and fitting the kinetics to a model function , our estimation of the assembly rate is independent of uncertainties in detection of the particle at early time points and in the subjective determination of when assembly is complete . The values derived from experiments on multiple cells are summarized in Table 1 . The rate of assembly is ∼4 . 3×10−3 s−1 or 233 s , which corresponds to a time of ∼9 minutes for 90% completion of assembly . To determine the distribution of assembly rates for individual sites , single traces were fit separately to equation ( 1 ) . The resulting histogram ( Figure 4C , left panel ) could be approximated by a log-normal distribution of rates . The maximum position of ( 5 . 5±0 . 5 ) ×10−3 s−1 obtained from this distribution corresponds well to the rate of 4 . 3×10−3 s−1 determined from the averaged traces shown in Figure 3A . We confirmed the observed assembly rates using SDCM ( Video S3 ) . While SDCM does not offer the contrast of TIRF microscopy , it has the advantage that three-dimensional information is collected and a projection of the z-stack can then be used for tracking of the individual assembly sites . Hence , the measured fluorescence intensity is not sensitive to axial motion of a cluster over several micrometers . The SDCM measurements confirmed the saturating exponential kinetics of Phase I yielding comparable rates ( kI = 5 . 5×10−3 s−1 for the ensemble average in Figure S4 and kI = 3 . 6×10−3 s−1 for the typical trace shown in Figure S4A ) . The observed characteristic time of ∼200 s ( or ∼8–9 min for 90% completion of assembly ) is in a similar range with the estimate of 5–6 minutes for complete assembly made by Jouvenet et al [22] . The variation of rates between individual sites on one cell suggests that additional factors besides the overall Gag concentration influence the rate of assembly . We analyzed the rate of HIV assembly at early ( 0–25 minutes ) and intermediate ( 40–60 minutes ) time points of Video S1 . Between these time intervals , the concentration of Gag at the plasma membrane increased significantly whereas the concentration of Gag in the cytosol remained relatively constant . The assembly rates for the two time periods were similar ( compare Figure 4A and 4B ) . Moreover , we observed no change in the assembly rates of individual assembly sites as a function of time after onset of punctae formation ( Figure 4C ) . Considering that some cells exhibited cytosolic fluorescence for hours without producing virus particles and that the rate of assembly appear to be independent of Gag concentration one can speculate that additional factors are involved in the initiation of assembly with some cells apparently lacking assembly competence . The observed saturating exponential increase in fluorescence intensity indicates that Gag accumulation at a growing budding site is retarded over time and eventually stops . The simplest explanation for this observation is that completion of the spherical Gag shell is preventing the addition of further Gag molecules . However , this is inconsistent with data from electron cryo-tomography experiments showing that the Gag shell of immature particles is incomplete [35] , [36] . Therefore , other mechanisms must be responsible for restricting Gag incorporation , such as Gag-induced membrane curvature or closure of the bud neck by the cellular ESCRT machinery restricting access for further Gag molecules . After the initial increase in fluorescence intensity ( phase I ) , the signal fluctuated about a nearly constant average intensity for variable time periods ( Phase II ) before a decay in fluorescence intensity was observed ( phase III ) . Using photobleaching experiments , Jouvenet et al . [22] have shown that there is no exchange of Gag between the assembly site and the cellular environment during the plateau region following the assembly phase . This phase most likely reflects the interaction of the assembled Gag shell with cellular membrane constituents but further investigations are required to elucidate the biological significance of phase II . We determined the duration of phases I+II , i . e . the time period between the first detection of the assembly site and the onset of phase III , for 160 individual trajectories ( Figure S5A ) . The total duration of phase I+II is 1 , 300±700 s . Using 90% saturation of phase I ( 90% level of the exponential amplitude in equation ( 1 ) ) as the onset of the phase II , the average duration of phase II was determined to be ∼10 min . The duration of phase II varied between individual events and the distribution is shown in Figure S5B . Phase III is defined by a decay in fluorescence intensity of the TIRF signal . It began abruptly as seen in Figure 2A and 2B , indicating a change in particle behavior . This phase was also frequently observed in wide-field mode . We characterized the dynamics of phase III by averaging multiple traces as we did to analyze phase I ( see Supporting Text S1 ) . The results were fit to a decaying exponential function with an offset: ( 2 ) The results are given in Table 1 . A rate of 2 . 7×10−3 s−1 was determined for phase III . Many physical processes can potentially contribute to the decay of fluorescence intensity . These processes include ( i ) loss of Gag . eGFP molecules from the assembly site , ( ii ) a change in the distance of the particle from the cover slip or ( iii ) increased motility of the particle upon budding or dissociation from cellular factors . Most likely , multiple factors are responsible for the fluorescence decay in phase III and their contribution may vary from event to event . For example , formation of the budding structure may cause Gag molecules to approach the coverslip . This would lead to higher illumination intensities and faster photobleaching rates . Hence , a short rise in fluorescence intensity would be observed at the end of phase II followed by the decrease in fluorescence intensity characteristic of phase III . Such fluorescence intensity curves were occasionally observed for specific assembly sites but were in general not indicative of the transition from phase II to phase III . In other cases , we observed an increase in mobility of the particle ( as discussed below ) . When the position of a particle changes during the integration time of the camera , the signal becomes more diffuse leading to a decrease in peak intensity . Currently , we cannot determine what physical process or processes are , in general , responsible for Phase III . However , the results suggest that the onset of phase III is related to formation of late budding structures and/or the budding event . A detailed analysis of single traces provided a clear indication for virus release at the onset of phase III in individual cases . Release of HIV-1 from the producer cell should be accompanied by an increase in mobility of the virion . The particle depicted in Figure 2A , underwent a dramatic increase in its instantaneous velocity concomitantly with the onset of Phase III . The trajectory of the particle , color coded for the three phases is shown in Figure 2C and a movie of the assembly and release of the particle is shown in the Video S4 . A mean-square-displacement analysis of the trajectory starting at Phase III shows random Brownian motion with a 100 fold increase in the diffusion coefficient ( Figure 2D ) . The mobility of the particle is significantly higher than expected for the movement of HIV-1 within the cytosol in the absence of directed transport , although still lower than the diffusion coefficient of HIV-1 in buffer alone [31] . The diminished diffusion coefficient is likely due to confinement of the virus between the cover slip and the cell . Such clear signatures of release were rare . Due to the TIRF geometry , we can only observe particles budding towards the cover slip . These virions are typically constricted in the narrow space between the cell and the cover slip or may even adhere nonspecifically to the glass surface . In addition , released viruses may be tethered to the plasma membrane upon release by interaction with cell surface constituents . Complete assembly and release could be tracked in TIRFM for a total of 18 particles starting from nucleation of the assembly site and using changes in diffusional behavior or rapid disappearance of the particle as a marker for release . Of these 18 events , 5 disappeared within the first 60 seconds after the onset of Phase III and 8 trajectories showed anomalies in the MSD analysis due to sticking of the virus for a few frames to the glass cover slip and/or cell surface . Of the remaining 5 traces , all showed random diffusional motion with a diffusion coefficient above 10−3 µm2/s . From these data , the mean time from appearance of an assembly site to particle release was determined to be 1 , 500±700 s . This time frame is similar to the time of 1 , 300±700 s that we had previously determined as the onset of phase III and further supports the idea that the onset of phase III is related to the formation of late budding structures and/or release . To investigate whether the long delay between assembly and release was influenced by adherence of the ventral cell surface to the coverslip , individual HIV-1 budding events were also recorded on the dorsal membrane using SDCM . In 12 observed events , the average time from initiation of assembly to release was 1 , 700±1 , 000 s , indistinguishable from the results obtained with TIRFM . An example of an HIV-1 assembly/release event recorded by SDCM is shown in Figure S6 and Video S5 . Besides these clear release events , many more viruses were found to remain associated with the dorsal plasma membrane for extended periods of time ( at least 3 hours ) . This observation is consistent with that of Larson et al . [20] and is probably a result of non-specific sticking of newly produced virions to the cell surface . Our data indicate that assembly of the Gag shell in HeLa cells occurs with a characteristic time of ∼200 s ( or 8–9 min for 90% completion of assembly ) followed by an apparent delay of ∼15 min ( phase II ) before the virion is released . This lag period after formation of the Gag shell was unexpected and further analyses are required to analyze whether release kinetics are similar in different host cells and experimental systems . One may speculate that the delay period serves to complete membrane closure and fission involving host cell functions . It may also be advantageous for the virus . For example , the time lag may allow movement of the bud towards a virological synapse [37] , which serves as preferred pathway of virus transmission from cell to cell . To obtain further insight into what determines the kinetics of assembly , we performed experiments using HIV-1 variants impaired in release due to mutation of the PTAP motif in the p6 domain of Gag [38] or in proteolytic maturation due to a D25A mutation in the PR active site [39] . The results are summarized in Figure 3 and Table 1 and showed no significant differences for the various constructs . The largest difference in the average rates of Phase I was observed between wt and PR-defective constructs . We compared , therefore , the distributions of rates for these two variants . The maximum of the distribution for the PR- variant was at ( 8 . 4±0 . 7 ) ×10−3 s−1 , which compares well with the average value of ( 7 . 9±1 . 5 ) ×10−3 s−1 from Table 1 . Although these values are higher than those obtained for wt ( 5 . 5×10−3 s−1 and 4 . 3×10−3 s−1 , respectively ) , statistical evaluation yielded a p-value of 0 . 74 , indicating that the difference is not significant . We conclude that the assembly process corresponding to Phase I is independent of ESCRT recruitment or protease activity . PR-defective HIV-1 had exhibited a decreased rate of release in previous pulse-chase experiments [40] , but showed no apparent difference in the assembly phase of individual particles . To obtain further insight , we also analyzed a variant defective in the viral protein Vpu , which is required to counteract the tetherin-mediated restriction to virus release in HeLa cells [28] , [41] . Neither the rate of 4 . 6×10−3 s−1 for Phase I alone ( Table 1 ) , nor the combined duration of Phases I+II of 1 , 600±1 , 000 s ( Figure S5C ) appeared to be significantly different from those observed for the wild-type virus . The average duration of the Phase II ( Figure S5D ) , determined as previously discussed , was calculated to be approximately 10 min , also comparable to the wild type construct ( Figure S5B ) . These observations are consistent with the fact that the tetherin-mediated restriction manifests itself after the actual budding event . In addition to similar assembly rates and durations of phase II , all mutants exhibited a phase III with rates similar to that of wild type ( Table 1 ) . Observing a similar phase III for the ESCRT-defective variant appears counterintuitive since this variant is known to exhibit a defect in particle release . However , a quantitative analysis of the mobility of these particles showed differences in the mobility as compared to the wt measurements . The averaged diffusion coefficient determined from 170 trajectories was D = ( 1 . 5±1 . 9 ) ·10−4 µm2/s . In three instances , rapid movement on the order of ∼10−3 µm2/s was observed , but these punctae resided at the cell edge and displayed collective motion indicating that the particles had not been released . A change in fluorescence intensity in TIRFM without concomitant change in instantaneous velocity can be caused by an altered axial position of a late budding structure . Due to the exponential decay of the evanescent wave in TIRF mode , virons or late budding structures that are close to the coverslip would lead to an increase in the fluorescence intensity of the assembly site observed in TIRFM but not in WF . We exploited the sensitivity of TIRFM to the axial position of the fluorescent protein cluster by comparing the distributions in fluorescence intensity of the TIRF and WF channels . A time course of the TIRF ( Figure S7B ) and WF ( Figure S7C ) fluorescence intensity distributions at individual assembly sites is shown in Figure S7 for three different time windows ( 0–15 min , 15–30 min , 30–45 min after their initial detection ) . The histogram of fluorescence intensities determined by TIRF microscopy revealed three discernible peaks indicating different brightness classes , one centered about 500 counts , a brighter subpopulation at ∼1 , 000 counts , increasing in amplitude with time , and a third subpopulation observed as a small shoulder around 2 , 000 counts . Figure S7A shows an example image indicating individual particles from the different brightness classes . The brighter subpopulations were hardly discernible in the WF histograms ( compare Figure S7B , D with C , E , respectively ) , indicating that they represent protruding Gag clusters , i . e . trapped virions or arrested budding structures close to the cover slip . The late domain deficient HIV variant exhibited a second bright subpopulation in TIRF mode , which was detectable as a shoulder in the brightness histogram ( Figure S7D , E ) and is more prevalent at the later time points . In this case , the bright fraction may represent arrested late budding structures . In Phase I , individual Gag clusters form rapidly at the plasma membrane and further Gag molecules are recruited to the growing assembly sites on a time scale of minutes . To determine whether Gag molecules arrive at the assembly site directly from the cytosol or by lateral diffusion within the plasma membrane , we made use of an HIV-1 variant labeled with a thermostable mutant of the photoconvertible protein mEosFP ( kindly provided by J . Wiedenmann ) . This protein can undergo irreversible photoconversion from green to red emission upon exposure to 405-nm irradiation [42] , [43] . Photoconversion using TIRF excitation results in preferential conversion of Gag . mEosFP molecules in the plasma membrane whereas cytosolic Gag will be mostly unaffected ( Figure 5A ) . Ideally , when Gag is recruited exclusively from the cytosol , the green fluorescence intensity should increase whereas the red fluorescence intensity remains constant . In contrast , recruitment of Gag from the surrounding membrane would result in signal increase in the red channel , whereas the intensity in the green channel would remain constant . Figure 5 shows TIRF ( upper panels ) and WF ( lower panels ) views of a cell producing HIV-1mEos before ( Figure 5B ) and after ( Figure 5C ) photoconversion using TIRF mode . Arrival of new Gag molecules at the sites of virus assembly was monitored simultaneously in the green and red channels . In the representative experiment shown here , approximately 30% of mEosFP was photoconverted upon illumination for 90s with 405 nm light . Illumination in TIRF mode converted primarily Gag . mEosFP molecules at the plasma membrane and very little increase in cytosolic background was observed in the red channel ( Figure 5C , lower right panel ) . As non-photoconverted Gag . mEosFP is continually recruited to the membrane , we focused our analysis on assembly sites that appeared within 15 min after photoconversion . Figure 5D shows the fluorescence intensity from a single assembly site that was first observable 26 s after photoconversion . The increase in the fluorescence intensity of the green channel was much more pronounced than that of the red channel . The amount of non-photoconverted Gag . mEosFP to photoconverted Gag . mEosFP in the plasma membrane is continually changing during the experiment . This is due to , for example , diffusion of the labeled Gag within the plasma membrane , new non-photoconverted Gag being delivered to the plasma membrane , late maturation of the chromophore or the different photobleaching rates for the photoconverted and non-photoconverted species . Therefore , we monitored the fluorescence intensity of the plasma membrane ( Figure 5E ) . The fluorescence intensity after 488 nm excitation ( i . e . the non-photoconverted species ) increased with time whereas the fluorescence intensity after 561 nm excitation ( i . e . the photoconverted species ) was gradually decreasing . To account for the time-dependent changes in the fluorescent signal of the non-photoconverted and photoconverted species in the plasma membrane , we define a normalized ratio , , which is the ratio of the signal after 488 nm excitation to the signal after 561 nm excitation divided by the same ratio calculated for the background in the neighborhood of the respective assembly site at the same time point . This normalization accounts for any differences due to incomplete photoconversion , slow maturation of the mEosFP chromophore , changes in the concentration of the non-photonconverted and photoconverted Gag . mEosFP due to diffusion within the membrane , delivery of new Gag molecules to the plasma membrane , photobleaching , or potential Förster Resonance Energy Transfer between the non-photoconverted and photoconverted species . In addition , the background used for the analysis is in the near vicinity of the assembly site meaning that we are not sensitive to undulations of the plasma membrane on the lateral scale of ∼1 . 5 µm or larger . However , preexisting budding structures would have a higher percentage of photoconversion due to the closer proximity of the virion to the cover slip than the local background . Accordingly , this can be observed for the assembly sites that were present before photoconversion , plotted at t = 0 in Figure 5F , which show a distribution of normalized ratios with an average value significantly lower than one . When assembly sites recruit membrane-bound Gag molecules , the ratio of fluorescence intensities from the individual assembly site will be approximately equal to the background signal coming from the plasma membrane . Technically , the background contains a contribution of fluorescence from cytosolic non-photoconverted Gag . mEosFP that is still observable in the TIRF evanescent field . This signal does not appear in the fluorescence intensities of the individual assembly sites as they are background corrected . Therefore , the ratio of non-photoconverted signal to photoconverted signal will be lower than that calculated for the local background: when Gag is accumulated from the plasma membrane . In the opposite case , when the majority of Gag is recruited from the cytosol , this ratio in the assembly site would be greater than that of the local background . Therefore , we plotted the normalized ratio , , and compared it to unity ( Figure 5F ) . For the newly generated assembly sites , we determined this normalized ratio either at the onset of assembly or in the time window of 3–5 minutes after assembly has begun . Thus , we could also investigate whether Gag is recruited from different locations during assembly . at the onset of nucleation was determined by extrapolating linear fits of the early points of the fluorescence signal and the background to the time of first detection . The normalized ratios are plotted in Figure 5F as a function of time of appearance after photoconversion . At early time points after photoconversion , we observed some variability and the newly formed assembly sites had normalized ratios both greater than and less than one . However , after ∼100 s , almost all new assembly sites exhibited normalized ratios above one . This observation suggests that nucleation of assembly sites involves Gag molecules coming directly from the cytosol or Gag that has only recently been delivered to the plasma membrane . As we cannot distinguish between Gag molecules that were delivered directly to the assembly site from Gag molecules that were delivered to the plasma membrane in the direct vicinity of the assembly site and immediately incorporated into the assembly site , it is also possible that Gag molecules may first bind briefly to the plasma membrane ( ∼100s ) before being incorporated into the assembly site . Regardless of whether the Gag is delivered directly from the cytosol to the assembly site or interacts briefly with the plasma membrane before incorporation into the assembly site , the Gag is effectively being delivered from the cytosol . By analyzing the normalized ratio in the time interval of 3 to 5 minutes after initiation of the individual sites , we determined that the assembly sites grow with the majority of Gag coming effectively from the cytosol ( Figure S8 ) . Within the context of current controversial theories of HIV-1 assembly ( reviewed in [15] ) , our results support the model that virion formation occurs exclusively at the plasma membrane , at least in HeLa cells . We observed no evidence for Gag containing vesicles arriving at the plasma membrane . As a control measurement , we performed dual-color experiments using eGFP-GPI [44] as a membrane marker along with Gag . mCherry ( Figure 6 ) . Interestingly , no difference in eGFP-GPI concentration was seen at assembly sites compared to the surrounding background ( Figure 6C ) . This suggests that there is no preferred incorporation or exclusion of eGFP-GPI from the budding site . Similar results were obtained using a myristoylated and palmitylated version of eYFP ( Figure S9 [45] ) . Thus , while HIV-1 particles display a ‘raft’-like lipid composition [46] , the budding sites likely represent a specialized form of DRM distinct from other ‘raft’-like membrane domains . Bright bursts of GFP fluorescence in eGFP-GPI expressing cells as observed in supporting Video S6 most likely originate from fusion of GPI-loaded vesicles with the plasma membrane . Such bursts were not detected in measurements with Gag . eGFP . While this does not rule out that small numbers of Gag molecules are delivered to the plasma membrane via vesicles , the data suggest that the delivery of larger assemblies of Gag molecules through a vesicular compartment is not involved in the Gag assembly process . We also did not detect pre-assembled large Gag multimers being transported to the plasma membrane in our SDCM experiments . However , our results suggest that HIV-1 assembly is nucleated by Gag oligo- or multimers at the plasma membrane which are subsequently extended by further Gag molecules recruited effectively from the cytosol with perhaps a small contribution from Gag molecules diffusing laterally in the plasma membrane . In summary , our results indicate that formation of HIV-1 assembly sites occurs during a relatively short period after onset of assembly in an individual cell , while particle production is completely asynchronous in the overall culture . Virus release occurs from individual assembly sites and not from preformed budding platforms . Assembly of the Gag shell ( phase I ) occurred with a characteristic time of ∼200 s ( or ∼8–9 min for 90% assembly ) , slightly slower than the 5–6 min recently reported by Jouvenet et al . [22] . Using the instantaneous velocity of the particle as a marker for extracellular release , we provide a first estimate for the duration from appearance of an assembly site to particle release of a complete HIV-1 particle from HeLa cells: approximately 25 minutes . Assembly of the Gag shell appears to constitute only a minor part of this period indicating that traversing the membrane and fission are the rate-limiting stages in virion formation . Similar results were obtained for release from the ventral membrane measured via TIRF microscopy and from the dorsal membrane determined using SDCM . Using a photoconvertable label , we established that the Gag molecules that participate in nucleation of a new assembly site and in bud growth are recruited preferentially from the cytosolic pool of Gag molecules and from recently membrane-attached Gag . We found that , in spite of DRM-like composition of the lipid envelope , GPI was not enriched at the budding site . The described results add essential dynamic information to our picture of virus release and provide an experimental basis for interfering with this stage of virus replication .
Construction of plasmids pCHIV , pCHIV . eGFP , pCHIV . mCherry , their late-domain defective variants , which contain a PTAP to LIRL exchange in the p6 domain , and of the protease-negative variants has been described elsewhere [24] , [25] . Variants defective in Vpu were cloned by exchange of an EcoRI-XhoI fragment from plasmid HIV-1 NL4-3 p210-13 3′ D-Vpu [47] . pCHIV . mEOS was constructed the same way , based on a construct encoding folding-optimized monomeric EosFP in the pQE32 vector kindly provided by J . Wiedenmann [42] , [43] . HeLa and 293T cells were maintained in DMEM supplemented with 10% FCS . For microscopic analyses , HeLa cells were transfected using FuGene6 ( Roche ) according to the manufacturer's instructions and imaged in phosphate buffered saline supplemented with 0 . 9 mM CaCl2 and 0 . 5 mM MgCl2 . As a standard for the fluorescence intensity of complete viruses , particles were purified by ultracentrifugation from the supernatant of 293T cells transfected by CaPO4 precipitation with a mixture of pCHIV and pCHIV . eGFP in a molar ratio of 1∶1 as previously described [25] . The particle concentration was measured by p24 ELISA . A microscope capable of synchronous switching between TIRF- and WF-microscopy on a frame by frame basis was built for this study . A scheme of the experimental setup is shown in Figure S1 . The excitation paths were combined using a polarizing beam splitter so that all wavelengths could be used for each type of microscopy . Mechanical shutters were used to switch between microscopy modes . In this configuration , photoconversion could also be performed in the TIRF mode , allowing photoconversion of plasma membrane associated Gag . mEOS molecules . The detection path was split into two channels and imaged on different portions of an EMCCD camera ( Andor Technology ) . For comparison , experiments were also performed on a SDCM . A more detailed description of the setups is given in the Supporting Text S1 . Single virus tracing was applied to track individual Gag clusters over the time course of assembly . The tracking routine consisted of four steps: ( i ) particle identification , ( ii ) prediction of the particle position using a spatial-temporal filter , ( iii ) global mapping of detected particles to the predicted particle position and ( iv ) final assignment of particles within the movie based on the estimates of the predicted and detected particle position . For dual channel tracking , an additional step was incorporated to take the union of trajectories found in each channel separately . In addition to the position of the tracked particle , the total intensity at the particle position was determined along with the background about the particle , which was then subtracted from the total intensity to provide the particle intensity . A detailed description of the tracking algorithm can be found in the Supporting Text S1 and in [48] . | Human immunodeficiency virus ( HIV ) particles are formed and released at the plasma membrane of the infected cell . Here , we analyzed the dynamics of HIV assembly and release making use of fluorescently labeled HIV structural proteins . We determined that assembly of the viral protein shell occurs within ∼8–9 min after nucleation of an assembly site and virus particles are formed individually and not from large patches . Virion release was observed ∼25 min after nucleation of the assembly site . Assembly of the Gag shell thus appears to constitute only a minor part of the period required for particle formation indicating that traversing the membrane and fission are the rate-limiting stages in virion formation . Using a photoconvertible label in the viral Gag protein , we established that the Gag molecules driving nucleation of a new assembly site and in bud growth are recruited preferentially from the cytosolic pool of Gag molecules and from recently membrane-attached Gag . No intracellular assembly or vesicular trafficking of Gag was observed . The described results add essential dynamic information to our picture of virus release and provide an experimental basis for interfering with this stage of virus replication . | [
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] | 2009 | Dynamics of HIV-1 Assembly and Release |
Ageing populations pose one of the main public health crises of our time . Reprogramming gene expression by altering the activities of sequence-specific transcription factors ( TFs ) can ameliorate deleterious effects of age . Here we explore how a circuit of TFs coordinates pro-longevity transcriptional outcomes , which reveals a multi-tissue and multi-species role for an entire protein family: the E-twenty-six ( ETS ) TFs . In Drosophila , reduced insulin/IGF signalling ( IIS ) extends lifespan by coordinating activation of Aop , an ETS transcriptional repressor , and Foxo , a Forkhead transcriptional activator . Aop and Foxo bind the same genomic loci , and we show that , individually , they effect similar transcriptional programmes in vivo . In combination , Aop can both moderate or synergise with Foxo , dependent on promoter context . Moreover , Foxo and Aop oppose the gene-regulatory activity of Pnt , an ETS transcriptional activator . Directly knocking down Pnt recapitulates aspects of the Aop/Foxo transcriptional programme and is sufficient to extend lifespan . The lifespan-limiting role of Pnt appears to be balanced by a requirement for metabolic regulation in young flies , in which the Aop-Pnt-Foxo circuit determines expression of metabolic genes , and Pnt regulates lipolysis and responses to nutrient stress . Molecular functions are often conserved amongst ETS TFs , prompting us to examine whether other Drosophila ETS-coding genes may also affect ageing . We show that five out of eight Drosophila ETS TFs play a role in fly ageing , acting from a range of organs and cells including the intestine , adipose and neurons . We expand the repertoire of lifespan-limiting ETS TFs in C . elegans , confirming their conserved function in ageing and revealing that the roles of ETS TFs in physiology and lifespan are conserved throughout the family , both within and between species .
Ageing is characterised by a steady systematic decline in biological function , and increased likelihood of disease[1] . Understanding the basic biology of ageing therefore promises to help improve the overall health of older people , who constitute an ever-increasing proportion of our populations . In experimental systems , healthy lifespan can be extended by altered transcriptional regulation , coordinated by sequence-specific TFs[2–6] . Thus , understanding TFs’ functions can reveal how to promote health in late life . Forkhead family TFs , especially Forkhead Box O ( Foxo ) orthologues , have been studied extensively in this context . This effort has been driven by the association of Foxo3a alleles with human longevity[7]; and the findings that the activation of Foxos is necessary and sufficient to explain the extension of lifespan observed following reduced insulin/IGF signalling ( IIS ) in model organisms[8–11] . Foxos interact with additional TFs in regulatory circuits , and it is in this context that their function must be understood . For example , in Caenorhabditis elegans , the pro-longevity activity of Daf-16 is orchestrated with further TFs including Hsf , Elt-2 , Skn-1 , Pqm-1 and Hlh-30/Tfeb [3 , 12–15] . Examining regions bound by Foxos across animals has highlighted the conserved presence of sites to bind ETS family TFs[16] . In Drosophila , two members of this family , namely Aop ( a . k . a . Yan ) and Pnt , have been linked to ageing via genetic interactions with Foxo and IIS[4] , and similar interactions are evident in C . elegans [17] . These findings raise questions of the overall roles of ETS factors in ageing , and their relationship to the activities of Foxos . The ETS TFs are conserved across animals , including 28 representatives in humans[18 , 19] . Their shared , defining feature is a core helix-turn-helix DNA-binding domain , which binds DNA on 5’-GGA ( A/T ) -3’ ETS-binding motifs ( EBMs ) . They are differentiated by tissue-specific expression , and variation in peripheral amino acid residues which , along with variation in nucleotides flanking the core EBM , confers DNA-binding specificity[20] . ETS TFs generally function as transcriptional activators , but a few repress transcription[21 , 22] . Aop is one such repressor in Drosophila . Aop and its human orthologue Tel are thought to repress transcription by competing with activators for binding sites[21 , 23] , recruiting co-repressors[22 , 24 , 25] , and forming homo-oligomers that limit activator access to euchromatin[26–30] . Consequently , Aop's role in physiology must be explored in the context of its interactions with additional TFs , especially activators . Foxo is one such activator[31] . Both Foxo and Aop are required for longevity by IIS inhibition[9] , each is individually sufficient to extend lifespan[4] , and both are recruited to the same genomic loci in vivo . Whilst activating either in the gut and fat body extends lifespan , the effect of activating both is not additive . Furthermore , if Aop is knocked down , activating Foxo not only ceases to extend lifespan , but even becomes deleterious for lifespan[4] . Overall , these findings suggest that gene expression downstream of IIS is orchestrated by the coordinated activity of Aop and Foxo , and that there is a redundancy in the function of the two TFs , even though Foxo is a transcriptional activator and Aop a transcriptional repressor . We started this study by characterising Aop and its relationship with relevant transcriptional activators , including Foxo . This led us to uncover that roles in ageing are widespread throughout the ETS TF family , extending across multiple fly tissues and diverse animal taxa .
How does the transcriptional programme triggered by Aop relate to that triggered by Foxo ? We sought to identify genes that were differentially regulated in response to activation of either TF . We focused on adult female fly guts and fat bodies ( equivalent to mammalian liver and adipose ) , since these are the organs from which Foxo and Aop promote longevity[4] . We induced expression of Foxo , AopACT ( encoding a constitutively active form of AOP ) or both under the control of the S1106 driver by feeding flies with the RU486 inducer . We profiled genome-wide transcriptional changes in dissected guts and abdominal fat bodies ( as associated with the cuticle ) with RNA-Seq and identified genes responding to RU486 within each genotype at a False Discovery Rate ( FDR ) of 10% ( these and all subsequently mentioned gene set assignments are given in S1 Supplementary Information , along with full statistics for all genes in all genotypes; the key to the location of each sheet is contained within the S1 Supplementary Information ) . In both tissues , we found that the sets of genes regulated by either Foxo or AopACT overlapped significantly ( gut p<10−19 , fat body p<10−4 , Fig 1A ) . To further assess whether Aop’s and Foxo’s transcriptional programmes were similar , we tested for correlated expression changes in response to the two TFs within the union of all 712 genes differentially regulated by either TF in the gut , or the equivalent 727 genes in the fat body . The transcriptional programmes triggered by Foxo or AopACT were significantly correlated within these unions ( Fig 1B and 1C , Kendall's Tau rank-correlation test: gut tau = 0 . 17 , p = 1e-14; fat body tau = 0 . 32 , p<2 . 2e-16 ) . Interestingly , the sets of differentially expressed genes were largely tissue-specific ( S1 Fig ) , suggesting that this correlated response may be a general feature of the Aop and Foxo regulons and independent of the tissue-specificity of target promoters . Gene Ontology ( GO ) enrichment analysis suggested that , in the gut , this combined set of Aop- and Foxo-regulated genes tended to be involved in translation and energy metabolism , whilst the equivalent analysis in the fat body showed enrichment for regulators of gene expression ( details of this GO analysis and all those subsequently mentioned are given in S1 Supplementary Information ) . We independently confirmed this correlated response to Aop and Foxo using qRT-PCR of two transcripts identified by transcriptomics: a characterised transcriptional target of IIS [32] , tobi ( Fig 1D , linear model: RU486 F1 , 13 = 26 . 04 , p = 2e-4; no effect of genotype , full details of this and all subsequent linear models are contained in one sheet of the S1 Supplementary Information ) , and alcohol dehydrogenase ( Adh , Fig 1E—linear model: RU486 F1 , 9 = 7 . 83 , p = 0 . 02; no effect of genotype ) . Hence , Aop and Foxo not only promote longevity , but also individually effect equivalent transcriptional programmes . What are the outcomes of combining Aop and Foxo activity ? FOXO co-localises extensively with AOP in the genome , with 60% of FOXO-bound loci also bound by AOP in the adult gut and fat body[4] . Since AOP functions by repressive interactions with transcriptional activators , we hypothesised that FOXO activity would be modulated by AOP . We tested this hypothesis in vitro . Transcriptional reporters were constructed by combining the Adh basal promoter with FOXO-responsive elements ( FREs: AACA ) , ETS-binding motifs ( EBMs: GGAA ) or both , and examined for their response to FOXO and AOPACT in Drosophila S2 cells ( Fig 2A , S2 Fig ) . In the presence of EBMs , AOP prevents activation by ETS activators [e . g . 33] . In the presence of FREs , FOXO is known to activate transcription [31] . We confirmed published observations for individual TFs on the reporters that contained their individual binding elements: FOXO was sufficient to activate transcription from the FREs ( t-test t = 6 . 64 , p = 3 . 7e-5 ) , while , as expected [23 , 26 , 28] , AOPACT did not impact expression from EBMs ( t-test t = -0 . 66 , p = 0 . 26 ) . We conducted three replicate experiments to assess the interactive output of AOP and FOXO . Combining the FREs and EBMs allowed AOPACT to attenuate activation by FOXO , revealing that AOP can moderate FOXO’s activity when brought onto the same promoter . By striking contrast , in the absence of EBMs , AOPACT synergised with FOXO to stimulate induction to an order of magnitude greater than FOXO alone , indicating that AOPACT can indirectly accentuate FOXO’s ability to activate transcription ( Fig 2A ) . While the magnitude of these effects varied , it was consistently present across three independent experiments ( S2 Fig ) . To analyse these data we used linear modelling , testing how the complement of TF binding motifs altered the output of combining the TFs , both in each individual experiment , and across the three experiments . This analysis confirmed that the output of combining AOP and FOXO was promoter-dependent ( Linear model: FOXO:AOP:FRE:EBM—data from all three replicates , F1 , 158 = 21 . 06 , p = 9e-6; data from Fig 2A F1 , 48 = 15 . 34 , p = 2e-4; see also statistical analysis section of S1 Supplementary Information ) . Since the synergistic interaction occurred in the absence of EBMs , this is most likely an indirect effect , occurring not via a direct interaction on the promoter but rather via AOP-induced transcriptional changes elsewhere in the genome . Note that in the presence of EBMs , any synergistic effect of AOP appears counteracted by the repression occurring from direct AOP binding to the promoter . Synergy may account in part for the similarity of AOP’s and FOXO’s transcriptional programmes in vivo ( Fig 1 ) . Hence , AOP is not only able to moderate the activity of other ETS activators , but also the Forkhead TF FOXO , with the presence or absence of EBMs in a promoter determining whether AOP enhances or moderates FOXO activity . The in vitro analysis suggested that Foxo’s in vivo output should depend on Aop activity . To examine if synergy and antagonism of Foxo by Aop can be observed on native promoters in vivo , we looked at what happens when Aop and Foxo were combined . We used our above-described RNA-Seq experiment and sorted the union of differentially expressed genes by the direction of regulation upon induction of Foxo , AopACT , or both , paying attention to altered regulation when the TFs were co-induced . To visualise the groupings , we compared the fold-change values for each gene between different conditions by calculating per gene Z-score ( number of standard deviations away from the mean fold-chage; Fig 2B and 2C ) . In this way , we could identify sets of genes that may be synergistically or antagonistically regulated by Aop and Foxo . We note that neither Aop nor Foxo were significantly down-regulated by the other in either tissue , indicating that their combined transcriptomic outputs result from interactive effects on promoters ( S1 Supplementary Information ) . We selected specific candidates for validation by qRT-PCR , and used linear models to test for interactive effects of the TFs , indicated by differential effects of RU486 feeding on the study genotypes . Indeed , we found that Aop was able to antagonise Foxo’s induction of aay and 4ebp in the gut ( Fig 2D and 2E; aay genotype:RU486 F2 , 17 = 15 . 43 , p = 1e-4; 4ebp genotype:RU486 F2 , 17 = 8 . 38 , p = 2e-3; full analysis in S1 Supplementary Information ) . On the other hand , Aop synergised with Foxo to modulate expression of PGRP-SC2 in the gut and dilp6 in the fat body ( Fig 2F and 2G; PGRP-SC2 genotype:RU486 F2 , 15 = 4 . 06 , p = 0 . 03; dilp6 genotype:RU486 F2 , 17 = 6 . 61 , p = 8e-3; full analysis in S1 Supplementary Information ) . Thus , transcript profiling followed by qRT-PCR validation confirmed that the two modes of AOP:FOXO interaction observed on synthetic reporters can also occur in vivo . This simultaneous synergy and antagonism of AOP and FOXO may explain why , whilst activation of either TF is sufficient to promote longevity , their co-activation does not extend lifespan additively[4] . Whilst interactions with FOXO appear to account for some of the transcriptional outputs of AOP , 80% of AOP-bound genomic sites are not bound by FOXO in vivo[4] . Since AOP alone is insufficient to regulate transcription when brought onto a promoter ( Fig 2A and references[21 , 23 , 26 , 28] ) , interactions with other transcriptional activators must account for the full breadth of Aop's physiological and transcriptomic effects . Pnt is one such transcriptional activator . Pnt and Aop have mutually antagonistic roles in development , which is presumed to occur by competition for binding sites since the two recognise the same DNA sequence[23 , 30 , 34] . We confirmed this interaction on reporters in S2 cells: Transcriptional induction by PNTP1 ( a constitutively active isoform[35] ) was completely blocked by AOPACT ( Fig 3A , linear model AOP:PNT F1 , 16 = 41 . 8 , p = 7 . 9e-6; also see references[23 , 28 , 36 , 37] ) , suggesting that PNT inhibition may be a key factor in Aop’s pro-longevity effect . Additionally , Pnt over-expression can block the longevity effects of both Foxo and IIS [4 , 9] , suggesting that Pnt may also modify Foxo’s transcriptional output . To evaluate emergent interactions in vivo , the transcriptome-wide effects of co-expressing AopACT , PntP1 and Foxo in the gut and fat body were examined . We assessed the transcriptomic outcomes of induction of PntP1 either alone or in combination with AopACT and Foxo ( note that this is an extension of the above-described transcriptomic experiment , which was performed at the same time ) . For each of the gut and the fat body , we assembled sets of genes that were differentially regulated upon induction of any of the three TFs or their combinations ( union of all genes differentially expressed at FDR 10% , set assignments per tissue in S1 Supplementary Information , noting that the preceding Foxo/Aop-regulated genes are a subset ) . This formed a union of 945 genes in the gut , and 1214 genes in the fat body . We sorted these genes by their pattern of regulation ( i . e . set assignment ) and visualised the groupings based on per-gene Z-score . This revealed a complex pattern in both tissues where each TF appeared able to influence the outcomes of the other two ( Fig 3B and 3C ) . To distil these interactions , we tested explicitly for genes whose regulation is subject to a statistically significant three-way interaction of Foxo , AopACT and PntP1 induction . In the gut , 511 transcripts were subject to the combinatorial , interactive effects of the three TFs , as were 617 in the fat body ( 10% FDR , see results in S1 Supplementary Information ) . To reveal emergent transcriptional programmes in each tissue , principal components analysis ( PCA ) was performed over these sets of transcripts ( Fig 3D and 3E ) . Remarkably , the first principal component ( PC ) of differentially expressed genes in the gut distinguished flies by published lifespan outcomes[4] , with short-lived flies expressing PntP1 alone or in combination with Foxo at one end of the PC; long-lived flies expressing one or both Foxo and AopACT forming a distinct group at the other end of the PC; and AopACT countering the effect of PntP1 to form an intermediate group ( Fig 3E ) . In the fat body , a similar grouping was apparent on the diagonal of PCs 1 and 2 ( Fig 3D ) , despite more variability in the data , probably resulting from the difficulty of dissecting this organ . To infer functional consequences of these distinct transcriptional programmes , transcripts from the input set corresponding to the PCs were isolated and GO enrichment analysis performed . This revealed a strong enrichment of genes with roles in energy metabolism , whose expression was strongly correlated to the PCs ( S3 Fig ) . Overall , a combined view of the PCA and GO analysis predicted that: ( 1 ) inhibiting Pnt may recapitulate the transcriptional programme of Aop and Foxo and promote longevity , and ( 2 ) that Pnt , alongside Aop and Foxo , may regulate metabolism in young flies . Since Aop and Foxo appeared to drive a transcriptional programme opposed to that of Pnt , we hypothesised that directly limiting physiological levels of Pnt would be sufficient to recapitulate their effect on gene expression . We first assessed the transcriptome-wide changes in the gut and fat body induced by RNAi-mediated knockdown of Pnt . The sets of genes differentially regulated by Pnt knockdown ( FDR 10% ) significantly overlapped those regulated by AopACT or Foxo in both the gut and the fat body ( Fig 4A ) . Additionally , in the union of the genes regulated by PntRNAi , AopACT or Foxo induction in the fat body , correlated effects of Pnt knockdown and Foxo or Aop activation were evident ( Fig 4B and 4C ) . However , such broad correlations were not evident in the gut ( Fig 4D and 4E ) . Hence , reducing the physiological levels of Pnt can recapitulate some aspects of the Aop/Foxo transcriptional programme . But is this sufficient to extend lifespan ? Inducing RNAi against Pnt from day three of adulthood in the gut and fat body was indeed sufficient to increase lifespan ( Fig 4F , log-rank p = 7 . 2e-4 ) . To further validate this finding , we backcrossed a loss-of-function p-element insertion in Pnt ( PntKG04968 , henceforth PntKG ) , into an outbred , wild-type background for ten generations . The mutation was homozygous lethal . However , heterozygote females exhibited a 20% increase in median lifespan ( Fig 4G , log-rank p = 9 . 2e-11 ) . We also tested whether expressing a transcriptional repressor of the Pnt locus extended lifespan . The HMG-box repressor capicua ( cic ) represses expression of Pnt[38] and , consistent with the effects of PntRNAi , overexpressing cicΔC2 ( a cic mutant lacking a known MAPK phosphorylation site ) in the gut and fat body also substantially extended lifespan ( Fig 4H , log-rank p = 1 . 5e-7 ) . These experiments demonstrated that countering Pnt is sufficient to recapitulate aspects of the Aop/Foxo transcriptional programme and extend lifespan , and corroborate the conclusions of transcriptomic analysis that Aop and Foxo act in part by countering Pnt . Our data show that each member of the Foxo-Aop-Pnt circuit can be targeted in the gut and fat body to extend lifespan . What is the function of this circuit , and Pnt in particular , in young flies , before ageing occurs ? The RNA-Seq data sets suggested metabolic regulation . Since the levels of Pnt appeared to dictate transcriptional and lifespan outcomes ( Figs 3 and 4 ) , we evaluated its metabolic role in more detail . The presence of genes including lipases and perilipin ( Lsd-2 ) in the transcriptome data suggested that Pnt modulates lipid metabolism . Therefore , we applied nutritional stresses to alter triacylglyceride ( TAG ) storage , and assessed how PntP1 altered the response to these stresses . We quantified TAG after a week of PntP1 induction , and then after a subsequent six days of starvation . PntP1 accentuated the loss of TAG per unit weight induced by starvation ( Fig 5A; linear model RU486:starvation F1 , 19 = 7 . 03 , p = 0 . 02 ) , but not overall weight loss ( S4 Fig ) , suggesting that Pnt sensitises flies specifically to cues for lipolysis . The mobilisation of TAG stores was associated with decreased resistance to starvation , with flies over-expressing PntP1 dying 24% earlier on average ( Fig 5B log-rank p = 1 . 3e-14 ) . This ability of Pnt to promote catabolism of energy stores may be beneficial in the face of over-nutrition , and relevant to the Western human epidemic of metabolic disease associated with energy-rich diets . A Drosophila model of such energy-rich diets is feeding flies a high sugar diet . Flies fed 40% sugar die substantially earlier than controls fed a 5% sugar diet , and accumulate TAG[39 , 40] . However PntP1 overexpression restored TAG levels in flies on a high-sugar diet to those observed on a low-sugar diet ( Fig 5C ) . Whilst there was no statistically significant interaction of sugar and PntP1 induction in a linear model ( RU486:sugar F1 , 17 = 0 . 32 , p = 0 . 57 ) , the adipogenic effect of sugar was opposed by Pnt , such that TAG levels on a high-sugar diet with Pnt induction were not different from those on a low-sugar diet without Pnt induction ( t-test: t = 0 . 01 , p = 0 . 99 ) . Moreover , PntP1 induction spared flies from the full extent of the early death induced by dietary sugar , increasing median survival time by 26% , despite having no effect on the low-sugar diet ( Fig 5D; cox proportional hazards RU486:sugar p = 6 . 2e-3 ) . Altogether , these results indicate that while Pnt activity is detrimental during ageing , in youth it predisposes flies to leanness , which correlates survival of nutritional stress . This may suggest that metabolic regulation is an adaptive function of the Foxo-Aop-Pnt circuit in early life , but that the configuration which is optimal for metabolism is deleterious for later survival . Animal genomes encode multiple ETS factors: In Drosophila the ETS family comprises the repressor Aop and activators: Pnt , Eip74EF , Ets21C , Ets65A , Ets96B , Ets97D and Ets98B , each of which is expressed with its own unique tissue-specific pattern ( Fig 6A ) . Finding lifespan-limiting roles of Pnt in addition to the previously described pro-longevity role of Aop , suggested that other ETS TFs with functions as transcriptional activators may also have the same lifespan-limiting effect . We examined the function of the other ETS TFs in Drosophila lifespan by knocking down their expression levels with RNAi in combination with inducible drivers . The data obtained in >40 lifespan assays are summarised in Fig 6B , including information on Aop , Pnt and Foxo . Summary statistics of each lifespan , along with associated genetic information , are presented in S1 Supplementary Information , while individual lifespan curves are presented in S5–S8 Figs . We identified each of Eip74EF , Ets21C and Ets97D as limiting lifespan in at least one tissue . For Ets21C , we confirmed the result using an available mutant ( S5 Fig ) . Whilst some of the effects we observed were modest , overall the data pointed to roles in ageing for five of the eight Drosophila ETS TFs . The effects were in general tissue specific . RNAi against Pnt , Ets21C and Ets97D restricted to the gut and the fat body with the S1106 driver extended lifespan ( Fig 6B , S5 Fig ) , the same tissues where Foxo and Aop act [4] . Knockdown of Pnt but not of Ets21C in enterocytes ( ECs ) , using the GS5966 driver , was sufficient to extend lifespan , as was activating either AopACT or Foxo ( Fig 6B , S6 Fig ) . Pnt and Ets21C have been characterised as regulators of intestinal stem cell ( ISC ) proliferation[38] , but activating cognate RNAi constructs with drivers that are active in ISCs ( GS5961 and TIGS ) did not consistently or substantially extend lifespan ( Fig 6B , S6 Fig ) . Pnt functions in neurogenesis[41] , and its continued expression in adult neurons suggested an ongoing , physiologically relevant role . However , neuronal PntRNAi induction , achieved with the ElavGS driver , did not affect lifespan , while over-expressing either AopACT or Foxo was deleterious and in contrast to their benefits in gut and fat body ( Fig 6B , S7 Fig ) . Eip74EF is more highly expressed in the brain than other tissues ( Fig 6A ) , indicating that neurons may mediate the beneficial effect of its ubiquitous knockdown . Indeed , expressing Eip74EFRNAi in neurons using the inducible , neuron-specific driver Elav-GS extended lifespan ( Fig 6B , S7 Fig ) . Overall , these data show that members of the Drosophila ETS family , along with Foxo , have distinct effects on lifespan in distinct tissues . The ETS TFs act downstream of receptor tyrosine kinase ( RTK ) pathways . The insulin receptor InR is an established regulator of Aop and Foxo[8] , and reducing its activity promotes lifespan[9] . Whilst expressing InRDN ( a dominant-negative form ) in the gut and fat body enhanced lifespan , expressing the same construct in ECs did not ( Fig 6B , S8 Fig ) , indicating that another RTK may function upstream of ETS TFs and Foxo in the ECs . The epidermal growth factor receptor EGFR can signal to Pnt and Ets21C via cic[38] , suggesting EGFR in ECs may regulate lifespan . Indeed , inducing the dominant-negative form EGFRDN in the gut and fat body or ECs extended lifespan ( Fig 6B , S8 Fig ) . Hence , different ETS factors may limit lifespan downstream of different RTK pathways in different tissues . The evidence suggested that a role in ageing is shared amongst multiple ETS factors in Drosophila . ETS TFs are conserved throughout multicellular animals , and the extensive conservation of roles in lifespan amongst the ETS family in the fly suggested that this lifespan modulation may be a fundamental property of these TFs , that extends to other species . The genome of the nematode C . elegans encodes 11 ETS TFs in total . At least one of these , Ets-4 , has been reported to limit lifespan in the worm intestine[17] . We screened the majority of the other C . elegans ETS TFs for roles in lifespan by feeding worms RNAi from egg or L4 onwards ( S1 Supplementary Information ) . Expanding the repertoire of proteins that limit worm lifespan , we found that knockdown of Lin-1 ( an orthologue of human ELK1 , ELK3 and ELK4 ) consistently extended C . elegans lifespan , in multiple independent trials from L4 stage or egg ( e . g . Fig 6C ) . Thus , multiple ETS factors limit lifespan in species separated by hundreds of millions of years of evolutionary divergence , hinting at a general role for this family of TFs in animal longevity .
Promoting healthy ageing by transcriptional control is an attractive prospect , because targeting one specific protein can restructure global gene expression to provide broad-scale benefits . This study suggests key roles for ETS TFs in such optimisation . The results show dual roles for Aop: balancing Foxo’s outputs , and opposing Pnt’s outputs . These functions coordinate transcriptional changes that correspond to lifespan . Repressing transcription from the ETS site appears to be the key longevity-promoting step , and indeed lifespan was extended by limiting multiple ETS TFs , in multiple fly tissues , and in multiple taxa . Altogether , these results show that inhibiting lifespan is a general feature of ETS transcriptional activators . Presumably the expression of these TFs is maintained , despite costs in late life , because of benefits in other contexts . For example , Pnt is important during development[23 , 34–36] , and expression may simply run-on into adulthood . We have now shown that Pnt is also important for adults facing nutritional variation or stress , and genomic evidence suggests equivalent functions for Ets-4 in C . elegans[17] . In addition , Ets21C is required to mount an effective immune response[42] , and both Ets21C and Pnt control gut homeostasis[38] . Tissue environment appears to be another important contextual factor that determines the lifespan effects of specific ETS TFs . Differences between tissues in chromatin architecture are likely to alter the capacity of a given TF to bind a given site , and our results show that a given TF , and also upstream RTKs , do not necessarily lead to the same lifespan effect across all tissues . The tissue-specific functions that we show for ETS TFs , Foxo and RTKs , suggests that transcription is locally coordinated by distinct receptors and TFs in distinct tissues , but that lifespan-regulatory signalling nevertheless converges on the ETS site . This differentiation makes it all the more remarkable that roles in lifespan appear to be conserved amongst ETS family TFs , even in diverse tissue contexts . The structure of molecular networks and their integration amongst tissues underpins phenotype , including into old age . Unravelling the basics of these networks is a critical step in identifying precise anti-ageing molecular targets . Identifying the least disruptive perturbation of these networks , by targeting the “correct” effector , is a key goal in order to achieve desirable outcomes without undesirable trade-offs that may ensue from broader-scale perturbation . This targeting can be at the level of specific proteins , cell types , points in the life-course , or a combination of all three . The tissue-specific expression pattern of ETS TFs , and the apparent conservation of their roles in longevity , highlights them as important regulators of tissue-specific programs that may be useful in precise medical targeting of specific senescent pathologies .
All experiments were carried out in outbred , Wolbachia-free Dahomey flies , bearing the w1118 mutation and maintained at large population size since domestication in 1970 . All transgenes ( S1 Supplementary Information ) were backcrossed into this background at least 6 times prior to experimentation , and stocks were maintained without bottlenecking . Cultures were maintained on 10% yeast ( MP Biomedicals , OH , USA ) , 5% sucrose ( Tate & Lyle , UK ) , 1 . 5% agar ( Sigma-Aldrich , Dorset , UK ) , 3% nipagin ( Chemlink Specialities , Dorset , UK ) , and 0 . 3% propionic acid ( Sigma-Aldrich , Dorset , UK ) , at a constant 25°C and 60% humidity , on a 12:12 light cycle . Experimental flies were collected as embryos following 18h egg laying on grape juice agar , cultured at standardised density until adulthood , and allowed to mate for 48h before males were discarded and females assigned to experimental treatments at a density of 15 females/vial . To induce transgene expression using the GeneSwitch system , the inducer RU486 ( Sigma M8046 ) was dissolved in absolute ethanol and added to the base medium to a final concentration of 200 μM . Ethanol was added as a vehicle control in RU-negative food . For lifespan experiments , flies were transferred to fresh food and survival was scored thrice weekly . Feeding RU486 to driver-only controls did not affect lifespan ( S1 Supplementary Information ) . For starvation stress experiments , flies were fed RU486 or EtOH-supplemented media for one week , before switching to 1% agarose with the equivalent addition of RU486 or EtOH , with death scored daily until the end . For sugar stress experiments , sugar content was increased to 40% w/v sucrose[39 , 40] . Worms were maintained by Brenner’s protocol[43] , at 20°C on NGM plates seeded with Escherichia coli OP50 . For lifespan experiments , N2 ( wildtype N2 male stock , N2 CGCM ) were used at 20°C on NGM plates supplemented with 15μM FUDR to block progeny production . RNAi treatment was started from egg or late larval stages ( details in Supplementary Materials ) . Animals that died from internal hatching were censored . The pGL3Basic-4xFRE-pADH-Luc construct ( called pGL4xFRE , reference [31] ) was used as template to generate PCR products containing 6xETS-4xFRE-pADH , 4xFRE-pADH , 6xETS-pADH- or pADH ( primers in S1 Supplementary Information , ETS sequence described in [44] ) , flanked by XhoI and HindIII sites , cloned into the corresponding sites in pGL3-Basic and confirmed by sequencing . PntP1 was amplified from UAS-PntP1 genomic DNA with Q5 High-Fidelity Polymerase ( NEB M0491S - primers in S1 Supplementary Information ) , and AopACT was cloned from genomic DNA of UAS-AopACT flies[4] . PntP1 and AopACT sequences were then cloned into the pENTR-D-TOPO gateway vector ( Thermo 450218 ) before recombination into the pAW expression vector . Drosophila S2 cells were cultured in Schneider’s medium ( Gibco/Thermo Scientific 21720024 ) , supplemented with 10% FBS ( Gibco/Thermo Scientific A3160801 ) and Penicillin/Streptomycin ( Thermo 15070063 ) . Cells were split into fresh media 24h before transfection , then resuspended to a density of 106 ml-1 and transfected using Effectene reagent ( Qiagen 301425 ) in 96-well plates , according to the manufacturer’s instructions . Reporters and TF expression plasmids were co-transfected with pAFW-eGFP to visually confirm transfection , and pRL-TK-Renillaluc as an internal control for normalisation of reporter-produced Firefly luciferase . Reporters and pRL-TK-Renillaluc were transfected 1:1 . When multiple TF expression plasmids were transfected , it was done 1:1 . Each TF expression plasmid was transfected 4:1 relative to reporters or pRL-TK-Renillaluc ( i . e . for every ng TF expression plasmid , 0 . 25 ng reporter and 0 . 25 ng pRL-TK-Renillaluc were transfected ) . The total amount of DNA transfected was then topped up to a standard quantity across all experimental conditions with pAFW-eGFP , in equal volumes of TE buffer . Reporter activity was measured 18h after transfection using Dual-Luciferase reagents ( Promega E1960 ) . pAHW-Foxo and/or pAW-AopACT were co-transfected with promoters bearing combinations of FREs and EBMs . pAW-AopACT and pAW-PntP1 were co-transfected with a promoter bearing EBMs . Flies bearing combinations of UAS-Foxo , UAS-AopACT and UAS-PntP1 , or UAS-PntRNAi in an S106-GS background were dissected after six days adult feeding on RU486 . Tissues were dissected in ice-cold PBS . Guts were dissected by cutting off the head and last abdominal segment , pulling on the crop through an incision at the abdomenal-thoracic junction , then removing tubules . Reproductive anatomy was then removed from the abdomen and the remainder of the abdomen taken as fat body . Dissected tissues were placed directly into ice-cold Trizol ( Ambion 15596026 ) . In the Foxo-AopACT-PntP1 epistasis RNA-Seq experiment , four experimental replicates were sampled per condition , each comprising a pool of 12 fat bodies or guts . In the PntRNAi experiment three replicates were sampled per condition , also each comprising organs from 12 flies . RNA was extracted by Trizol-chloroform extraction , quantified on a NanoDrop , and integrity was assessed on an Agilent Bioanalyzer . Poly ( A ) RNA was pulled down using NextFlex Poly ( A ) beads ( PerkinElmer NOVA-512981 ) and integrity was re-assessed . In the Foxo-AopACT-PntP1 epistasis RNA-Seq experiment , only samples with the highest RNA yields and integrity were included in library preparation , leaving 2–3 samples per experimental condition . All three replicates were prepped and sequenced in the PntRNAi RNA-Seq experiment . RNA fragments were given unique molecular identifiers and libraries were prepared for sequencing using NextFlex qRNAseq v2 reagents , ( barcode sets C and D , PerkinElmer NOVA-5130-14 and NOVA-5130-15 ) and 16 cycles of PCR . Individual and pooled library quality was assessed on an Agilent Bioanalyzer and quantified with a Qubit spectrophotometer . Sequencing was performed by the UCL Cancer Institute , using an Illumina HiSeq 2500 instrument ( paired-end 50bp ) for the Foxo-AopACT-PntP1 epistasis experiment , and a NextSeq 500 ( paired-end 75bp ) for the PntRNAi experiment . cDNAs were made from the polyA RNA preps that were prepared for sequencing , using SuperScript II Reverse Transcriptase ( Thermo 18064014 ) and OligoDT . qRT-PCR was performed on an Applied Biosystems QuantStudio 6 Flex real-time PCR instrument with Fast SYBR Green PCR Master Mix ( Thermo Fisher ) , with primers supplied by EuroFins Genomics ( all oligo sequences in S1 Supplementary Information ) , relative to a standard curve comprising a pool of all samples and the instrument's standard PCR cycle . TAG was measured as in [45] in whole adult S106; UAS-PntP1 flies following one week of RU486 feeding . Briefly , flies were CO2-anaesthetised , weighed on a microbalance , and immediately flash-frozen in liquid N2 . Flies were thawed in ice-cold TEt buffer ( 10 mM Tris , 1 mM EDTA , 0 . 1% v/v Triton-X-100 ) and homogenised by shaking with glass beads ( Sigma G8772 ) for 30s in a ribolyser at 6500 Hz . Aliquots of homogenates were heated to 72°C for 15m to neutralise enzymatic activity , then spun 1m at 4500g and 4°C to pellet debris . Triglyceride was measured by treating 5 μl sample with 200 μl Glycerol Reagent ( Sigma F6428 ) for 15m at 37°C and measuring absorbance at 540 nm , then incubating with 50 μl Triglyceride Reagent ( Sigma F2449 ) for 15m at 37°C and re-measuring absorbance at 540 nm , calculating glycerol content in each reading , then quantifying triglyceride content as the difference between the first and second measurement . Sequence libraries were quality-checked by FastQC 0 . 11 . 3 , duplicate reads were removed using Je 1 . 2 , and reads were aligned to D . melanogaster genome 6 . 19 with HiSat2 2 . 1 . Alignments were enumerated with featureCounts 1 . 6 . All downstream analyses were performed in R 3 . 3 . 1 . The gut and fat body were analysed in parallel . In the RNA-Seq experiment analysing Foxo-AopACT-PntP1 epistasis , genes with no counted transcripts were excluded ( S1 Supplementary Information ) . In the subsequent PntRNAi experiment , genes were filtered by the same criteria and any genes that were not analysed in the first experiment were also excluded . Read counts are given in S1 Supplementary Information . The transcriptomic effect of RU486 feeding was established for each individual genotype in the experiment , using DESeq2 at a false discovery rate ( IHW ) of 10% . To identify correlated effects amongst genotypes , sets of shared targets were formed as unions of DE gene sets from individual genotypes . Log2 fold-change values ( Figs 1–4 ) were plotted from the DESeq2 output . Three-way epistatic interaction amongst TFs were identified by fitting models of the form yi∼genotype+RU486+block+genotype:RU486 where block represented experimental replicate . The tripartite interaction of Foxo , AopACT and PntP1 was identified by applying the model to all genes across all experimental conditions , and isolating genes with a significant genotype:RU486 term . GO analysis was performed using the TopGO package , applying Fisher’s test with the weight01 algorithm . Principal Components Analysis was performed on read counts of these genes following a variance-stabilizing transformation . To characterise gene-expression correlates of principal components , loadings onto principal components were extracted using the dimdesc function from the FactoMineR library , and GO analysis performed as previously . Transcripts of genes annotated with enriched GO terms were then plotted per term by centring variance-stabilised reads to a mean of zero and plotting against PC values per sample . Heatmaps were plotted using the heatmap . 2 function from the gplots library , ordering rows by hierarchical clustering by Ward’s method on Euclidian distance , and scaling to row . Fly lifespan data were analysed using log-rank tests in Microsoft Excel or Cox Proportional Hazards in R for the interaction of sugar and PntP1 expression . Worm lifespan data were analysed by log-rank tests in JMP . Luciferase reporter data were normalised by taking the ratio of firefly luciferase to renilla luciferase signal and , for each promoter , taking the median reporter signal in the absence of FOXO and AOPACT as the start value , then calculating fold-change ( i . e . difference in start and end values , divided by start value ) for each sample . To assess the interaction of FOXO and AOP with promoters’ complements of TF-binding motifs , these normalised data were analysed by fitting a linear model of the form y∼FRE*EBM*FOXO*AOPACT in which y was the natural log of fold-change+1 , FRE and EBM represented the TF-binding complement , and FOXO and AOPACT represented co-transfection with pAHW-Foxo or pAW-AopACT . By the same approach , the interactive effect of PNTP1 and AOPACT were assessed by fitting a linear model of the form y∼PNTP1*AOPACT in which y represented the natural log of fold-change+1 . The effect of PntP1 overexpression on TAG and lifespan responses to nutrient stress ( starvation or high-sugar diet ) were analysed by a model of the form y∼RU486*diet where y represented TAG normalised to unit weight in a linear model , or survival in a Cox Proportional Hazards model ( survival library ) . | Understanding the basic biology of ageing may help us to reduce the burden of ill-health that old age brings . Ageing is modulated by changes to gene expression , which are orchestrated by the coordinate activity of proteins called transcription factors ( TFs ) . E-twenty six ( ETS ) TFs are a large family with cellular functions that are conserved across animal taxa . In this study , we examine a longevity-promoting transcriptional circuit composed of two ETS TFs , Pnt and Aop , and Foxo , a forkhead TF with evolutionarily-conserved pro-longevity functions . This leads us to demonstrate that the activity of the majority of ETS TFs in multiple tissues and even different animal taxa regulates lifespan , indicating that roles in ageing are a general feature of this family of transcriptional regulators . | [
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] | 2019 | Longevity is determined by ETS transcription factors in multiple tissues and diverse species |
Progesterone , via the progesterone receptor ( PGR ) , is essential for endometrial stromal cell decidualization , a cellular transformation event in which stromal fibroblasts differentiate into decidual cells . Uterine decidualization supports embryo implantation and placentation as well as subsequent events , which together ensure a successful pregnancy . Accordingly , impaired decidualization results not only in implantation failure or early fetal miscarriage , but also may lead to potential adverse outcomes in all three pregnancy trimesters . Transcriptional reprogramming on a genome-wide scale underlies progesterone dependent decidualization of the human endometrial stromal cell ( hESC ) . However , identification of the functionally essential signals encoded by these global transcriptional changes remains incomplete . Importantly , this knowledge-gap undercuts future efforts to improve diagnosis and treatment of implantation failure based on a dysfunctional endometrium . By integrating genome-wide datasets derived from decidualization of hESCs in culture , we reveal that the promyelocytic leukemia zinc finger ( PLZF ) transcription factor is rapidly induced by progesterone and that this induction is indispensable for progesterone-dependent decidualization . Chromatin immunoprecipitation followed by next generation sequencing ( ChIP-Seq ) identified at least ten progesterone response elements within the PLZF gene , indicating that PLZF may act as a direct target of PGR signaling . The spatiotemporal expression profile for PLZF in both the human and mouse endometrium offers further support for stromal PLZF as a mediator of the progesterone decidual signal . To identify functional targets of PLZF , integration of PLZF ChIP-Seq and RNA Pol II RNA-Seq datasets revealed that the early growth response 1 ( EGR1 ) transcription factor is a PLZF target for which its level of expression must be reduced to enable progesterone dependent hESC decidualization . Apart from furnishing essential insights into the molecular mechanisms by which progesterone drives hESC decidualization , our findings provide a new conceptual framework that could lead to new avenues for diagnosis and/or treatment of adverse reproductive outcomes associated with a dysfunctional uterus .
For healthy couples , the chance for conception per menstrual cycle is only thirty-to-forty percent [1] , underscoring the remarkable inefficiency of human reproduction . Moreover , approximately a third of pregnancies detected early by human chorionic gonadotropin assay fail to result in live births [2 , 3] , implicating early implantation failure as a causal factor for preclinical pregnancy loss . Implantation failure and early embryo miscarriage also undercut the full potential of assisted reproductive technologies ( ARTs ) which rely on the transfer of healthy embryos into a uterus that must in turn execute the requisite cellular and molecular changes to ensure the establishment of the fetomaternal interface [4] . Early functional impairment of the uterus is also implicated in recurrent pregnancy loss when parental chromosomal abnormalities , maternal thrombophilic disorders and anatomical uterine defects are first eliminated as causal factors [5] . Apart from contributing to early implantation failure , insufficient execution of normal uterine changes around the time of implantation is also thought to underpin adverse outcomes—pre-eclampsia and placental insufficiency , intrauterine fetal growth restriction , and preterm birth—in later trimesters of pregnancy , termed the “adverse ripple effect”; reviewed in [6] . Successful advancement of embryo implantation into a receptive endometrium requires decidualization of the endometrial stromal cell layer , a critical cellular transformation process in which quiescent stromal fibroblasts proliferate and differentiate into transient epithelioid decidual cells [7] . In addition to promoting local resistance to oxidative insults and gestational immunotolerance for the developing fetal allograft , decidual cells are thought to modulate the invasion of the embryonic trophoblast to a sufficient depth to establish fetoplacental circulation between the chorionic villi and the maternal intervillus space . Therefore , due to the indispensability of the decidualized stroma for placentation , inadequate endometrial decidualization is considered a critical yet poorly understood etiologic factor not only for early implantation failure but also for a broad spectrum of pregnancy disorders that manifest later . Along with exposure to preovulatory and nidatory estradiol-17β ( E2 ) , the endometrial stroma requires progesterone ( P4 ) “the hormone of pregnancy” and its nuclear receptor ( the progesterone receptor ( PGR ) ) for decidualization [6] . While many of the cellular events that underpin endometrial stromal cell decidualization are known , the key molecular signals that mediate P4-dependent decidualization of the human endometrium remain unclear . Integrating datasets from chromatin immunoprecipitation followed by next generation sequencing ( ChIP-Seq ) and RNA sequencing ( RNA-Seq ) , we reveal that the promyelocytic leukemia zinc finger ( PLZF ) transcription factor is rapidly induced by P4 in human endometrial stromal cells ( hESCs ) and that this induction is indispensable for P4-driven hESC decidualization . Additionally , we show that PLZF tightly regulates the expression levels of the early growth response 1 ( EGR1 ) transcription factor , the perturbation of which impairs hESC decidualization . Together , our studies have uncovered a new mediator pathway of the P4 signal that enables quiescent hESCs to differentiate into decidual cells , a cellular transformation process that is essential for early pregnancy establishment .
Comparing undecidualized hESCs with decidualized hESCs in culture , we recently revealed by RNA-seq analysis that PLZF ( also known as ZBTB 16 , a Kruppel C2H2-type zinc-finger transcription factor ) is listed in the top 20 genes which are significantly induced in hESCs during decidualization [8] ( S1A Fig ) . Additionally , we previously showed by microarray analysis that Plzf is present in the top 100 genes induced in total uterine tissue of the mouse in response to acute P4 administration [9] , suggesting PLZF is a P4-responsive gene and important for decidualization . To address this proposal , an established cell culture-based model for decidualization of primary hESCs was used to determine the transcriptional induction profile of PLZF ( Fig 1A ) . In response to a hormone decidual stimulus ( 17β-estradiol ( E2 ) , medroxyprogesterone acetate ( MPA ) , and cAMP ( EPC ) ) , rapid induction of high transcript levels for PLZF was observed by quantitative real time PCR ( Fig 1A ) . The induction of transcripts encoding insulin-like growth factor binding protein 1 ( IGFBP1 ) and prolactin ( PRL ) ( both established decidual biomarkers [10] ) confirmed decidualization of hESCs by EPC at the molecular level . Consistent with PLZF’s role as a transcription factor , most of the induced PLZF protein expression was present in the nucleus of decidual cells following EPC treatment ( Fig 1B ) . Similar to findings in Fig 1A , PLZF protein expression was not detected in pre-decidual hESCs or with vehicle treatment ( Fig 1B ) . Treatment of hESCs with E2 , cAMP , or MPA revealed that only MPA significantly induced PLZF transcription in hESCs ( Fig 1C ) , providing strong support for the progestin in the decidual hormone cocktail as the key driver of PLZF induction in decidualizing hESCs . Moreover , PLZF transcript levels are induced with acute progestin treatment ( as early as 2 hours post MPA administration ) , which is not dependent on new protein synthesis ( S1B and S1C Fig ) . These results support PLZF as an early progestin responsive gene in hESCs . As further support for this conclusion , immunohistochemical analysis of human endometrial biopsy samples show that PLZF protein is highly expressed in the nucleus of the endometrial stromal cell during the P4-dominant secretory phase of the menstrual cycle but is expressed at low levels in endometrial tissue biopsied during the E2-dominant proliferative phase of the cycle ( Fig 1D–1F ) . In the uterus of the ovariectomized mouse , Plzf transcript and protein levels are also significantly and rapidly induced by P4 ( S2 Fig ) , validating our previous microarray analysis of this tissue which showed Plzf transcription is induced by short-term P4 exposure [9] . Transcript and protein levels for Plzf were detected as early as six hours following P4 administration ( S2A and S2B Fig ) . Furthermore , expression levels of Plzf protein are low in the endometrium of the adult virgin mouse but are significantly increased in the uterine stromal compartment of the early pregnant mouse ( S2C Fig ) . It should be noted that global knockout of Plzf in the mouse results in a number of embryonic and developmental phenotypes [11] that preclude its use for uterine studies . However , the above results support the proposal that PLZF acts as a progestin/P4 early response transcription factor in human and mouse uterine stromal cells . To confirm that PLZF is rapidly induced by progestin via the progesterone receptor ( PGR ) rather than by rapid non-genomic progestin effects [12] , RU486 ( a PGR antagonist ) and small interfering ( si ) RNA-mediated knockdown of PGR were administered to hESCs ( Fig 2 ) . Following three days of culture with MPA or MPA plus RU486 , PLZF transcript levels were examined in hESCs by quantitative real time PCR ( Fig 2A ) . Compared to vehicle treatment , MPA significantly increased the transcript levels of PLZF; however , this level of induction was markedly attenuated with the co-administration of RU468 ( Fig 2A ) . Further confirming the requirement of the PGR in this transcriptional induction , progestin induction of PLZF transcription was significantly attenuated with PGR knockdown ( Fig 2B ) . In addition , acute hormone treatment of the ovariectomized Pgr knockout ( PRKO ) mouse revealed that rapid P4-induced Plzf transcription in the murine uterus requires endometrial Pgr ( S2D Fig ) . Together , the above results strongly support an indispensable role for PGR in the rapid induction of PLZF transcription by progestin or P4 in human and murine uterine cells . Ten PGR binding sites ( labeled 1 to 10 ( Fig 2C ) ) in the PLZF gene were revealed from an analysis of a recently published chromatin immunoprecipitation-sequencing ( ChIP-Seq ) dataset derived from PGR cistrome studies on decidualized hESCs [8] . Four of these PGR binding sites ( 2 , 4 , 5 , and 6 ) contain consensus progesterone-response elements ( PREs ) that are enriched in the hESC PGR-cistrome . Chromatin immunoprecipitation assay disclosed significant enrichment in PGR occupancy for sites 3 , 4 , 6 , and 8 within the PLZF gene as compared to a negative control region ( Fig 2D ) . Collectively , these results suggest that progestin transcriptional induction of PLZF involves binding of PGR to multiple sites across the PLZF gene . To determine whether PLZF is functionally required for in vitro hESC decidualization , siRNA mediated knockdown of PLZF transcription was performed in hESCs prior to EPC administration . Compared to control siRNA , hESCs treated with siRNA targeting PLZF failed to undergo the typical cellular transformation process in which elongated stromal fibroblasts transform into a polygonal epithelioid morphology , a morphological change which is indicative of stromal decidualization following EPC treatment ( Fig 3A ) . Consistent with this block in stromal cell decidualization , a significant attenuation in the induction of decidual biomarkers ( PRL and IGFBP-1 ) was observed in hESCs with PLZF knockdown ( Fig 3B and 3C ) . Although these results underscore a critical functional role for PLZF in advancing progestin dependent hESC decidualization , surprisingly the expression level of a number of previously published PGR target genes in hESCs ( FOXO1A , HAND2 , and HOXA10 [13–15] ) is not changed with PLZF knockdown ( Fig 3D ) . These results indicate that the PLZF transcription factor is either positioned below these targets in the hierarchy of target genes for PGR action or PLZF operates in parallel to control its own gene target ensemble , which in turn underpins PGR dependent hESC decidualization . To identify possible PLZF target genes , ChIP-Seq was performed to identify the genome-wide binding events for PLZF in decidualizing hESCs ( Fig 4 ) . Having passed Illumina’s purity filter , the PLZF cistrome consists of 19 , 092 , 785 unique alignments ( without duplicate reads ) from more than 36 Million reads and aligns with no more than 2 mismatches to the genome . A total of 1 , 120 sequence intervals are enriched for PLZF binding across all chromosomes ( supporting information ) . Peak distribution analyses revealed significant enrichment for PLZF binding near transcription start sites ( TSS ) with 48% of PLZF intervals located within 500 base pairs ( b . p . ) of TSS , indicating preferential binding of the PLZF transcription factor to gene promoter regions ( Fig 4A and S1 Table ) . DNA sequence motif enrichment analysis revealed the zf-C2H2 DNA binding domain ( MC00418 ) in the top enrichment cluster of motifs in the PLZF cistrome , a common DNA binding motif for many ZBTB transcription factors including PLZF [16] ( Fig 4B ) . This cluster also contained DNA binding motifs for breast cancer 1 early onset ( BRCA1 ) ( MC00299 ) and v-ets avian erythroblastosis virus E26 oncogene homolog 1 ( ETS1 ) ( MC00018 ) ( S2 Table ) . Following these enrichment groups , are motifs that are commonly found in cistromes of TSS-proximal binding transcription factors , such as motifs recognized by CCAAT-enhancer binding proteins ( or C/EBPs ) , interferon-regulatory factors , SP1 , and by leucine zipper family of transcription factors ( i . e . JUN and FOS ) ( S2 Table ) . To identify bona fide PLZF gene targets , we next compared 689 genes that have at least one PLZF ChIP binding region within 1kb of TSS with genes in hESCs for which expression changed with EPC treatment [8] . From this analysis , we identified 124 genes that are bound by PLZF and are differentially expressed during hESC decidualization ( Fig 4C and S3 Table ) . By DAVID and GSEA analysis , the majority of these genes are associated with mitosis and cell cycle progression ( S4 Table ) . Because PLZF was previously shown to act as a transcriptional repressor of cellular proliferative programs in a number of physiological systems; reviewed in [16] , we first focused on genes in the 124 gene list that promote early cellular growth responses and are downregulated during decidualization . The rationale for this prioritization is that a switch from a proliferative to a differentiative phenotype is an essential reprogramming step for hESCs to fully decidualize in response to EPC [7] . Based on the above criteria , we chose Early Growth Response 1 ( EGR1 ) for further study because the gene: ( a ) ranks in the top five most downregulated gene in the 124 gene list ( S3 Table ) ; ( b ) belongs to a small family of transcription factors that enable early cellular growth responses [17]; and ( c ) exhibits strong PLZF ChIP binding at its promoter ( Fig 5A and 5B ) . In keeping with its role as a driver of cell growth and proliferation , EGR1 transcript levels are significantly reduced as hESCs differentiate into decidual cells ( Fig 5C ) . Interestingly , analysis of our recent RNA-seq data from pre-decidual and decidualized hESCs shows that transcript levels of EGR1 along with EGR2 and EGR3 are significantly reduced with decidualization ( S3A and S3B Fig ) ; however , only EGR1 shows PLZF binding ( S3 Table ) . Importantly , knockdown of PLZF expression results in a significant attenuation in the normal reduction of EGR1 transcript levels following three days of EPC treatment ( Fig 5C and 5D ) , suggesting PLZF represses EGR1 transcription as hESCs decidualize . Moreover , lentiviral-mediated overexpression of EGR1 significantly attenuated hESC decidualization in response to EPC treatment as evidenced by a marked reduction in the levels of the decidual biomarkers: IGFBP1 and PRL at the molecular and cellular level ( Fig 5E and 5F ) . Interestingly , immunohistochemistry disclosed an inverse correlation between stromal PLZF and EGR1 protein expression in the human endometrium during the E2 dominant proliferative and P4 dominant secretory phase of the menstrual cycle ( S4 Fig ) . It should be noted that the human endometrial stroma can decidualize during the secretory phase of the cycle even in the absence of a conceptus [6] . In the late proliferative phase of the cycle , EGR1 expression is detected in both epithelial and stromal cells of the endometrium whereas PLZF expression is not detected ( S4 Fig: compare A-C with G-I ) . During the secretory phase of the cycle , significant PLZF expression is found in endometrial stromal cells; however , EGR1 expression is negligible during this time period ( S4 Fig: compare D-F with J-L ) . Together , these immunohistochemical findings further support the proposal that PLZF acts as a novel transcriptional repressor of EGR1 in endometrial stromal cells to enable decidualization . In the uterus of the ovariectomized mouse , Egr1 is markedly induced with E2 treatment alone ( E2 ) whereas this induction is significantly blunted with the inclusion of P4 ( P4E2 ) ( S5 Fig ) . Further confirming inhibition of E2 induction of Egr1 by P4 , inclusion of RU486 in the P4E2 hormone treatment returns Egr1 levels to those observed with E2 alone ( S5 Fig ) . Interestingly , P4 induction of PLZF expression during this period parallels P4 suppression of Egr1 expression ( S5 Fig ) , providing correlative support for the proposal that P4 suppression of E2 induction of uterine Egr1 is mediated by PLZF . Together , our human and mouse studies support the proposal that E2-induced EGR-1 expression is suppressed by P4-induced PLZF in the endometrium . In sum , recent genome-wide studies at the cistrome and transcriptome level underscore the regulatory complexity of P4-dependent hESC decidualization . Within this complexity , we have uncovered a new signaling pathway ( PGR-PLZF-EGR1 ) that is crucial for P4-driven hESC decidualization .
Extensive transcriptional reprogramming underpins hESC decidualization [8] , we report here that induction of PLZF as part of these transcriptional changes is critical for P4-dependent hESC decidualization . Highly conserved in mammals , PLZF is a member of the poxvirus and zinc finger ( POZ ) / broad-complex , tramtrack , and bric-`a-brac ( BTB ) and Kruppel zinc finger ( or POK ) family of transcription factors; reviewed in [16] . Previous investigations have demonstrated a pleiotropic regulatory role for PLZF in a myriad of developmental programs and physiological responses , from hind-limb skeletal patterning [11] to spermatogonial stem-cell maintenance [18 , 19] . Due to its role in limiting cell-cycle progression and cellular proliferation [20 , 21] , PLZF is known to act as a tumor suppressor in a broad spectrum of malignancies [22–26] . At the transcriptional level , PLZF is known primarily as a repressor; reviewed in [16]; however , an increasing number of studies describe PLZF as an activator of transcription [27–31] . Recruitment of corepressors and subsequent chromatin remodeling has been shown to underlie the repressor function of PLZF [32–34] . In different physiological contexts , PLZF is induced by the steroid hormones: cortisone [35] , testosterone [36] , and aldosterone [37] . These steroids along with P4 mediate their actions through closely related nuclear receptors belonging to subfamily 3 ( Group C ) of the nuclear receptor superfamily [38]; note: members of this receptor subfamily bind to a similar DNA response element containing the consensus half-site: AGAACA . Therefore , PLZF most likely evolved as one of a small number of specific target genes for this subclass of steroid hormones , which are known to exert wide-ranging physiological and pathophysiological responses . Although , a previous study reported that glucocorticoids and P4 can induce PLZF transcription in both hESCs and myometrial smooth muscle cells in vitro [39] , a functional role for PLZF in these uterine cell types was not addressed . Observed both in-vitro and in-vivo , endometrial stromal fibroblasts undergo proliferation and differentiation before their transformation into polygonal epithelioid decidual cells [7] . Using an integrative genome-wide approach , we reveal that PLZF is indispensable for P4-dependent hESC decidualization . Indeed , the requirement for PLZF in the morphological and functional transformation of an endometrial stromal fibroblast to an epithelioid decidual cell is reminiscent of its previously reported role in the morphological transformation of embryonic fibroblasts to a more flattened polygonal morphology [40 , 41] , a change in cell shape which correlates with the acquisition of cellular resistance to oncogenic transformation . Furthermore , the terminal differentiation which results from decidualization is in keeping with PLZF’s role in other physiological systems in which this transcription factor suppresses cell growth and proliferation in favor of differentiation; reviewed in [16] . The rapid P4-induction of PLZF transcription in uterine stromal cells suggests that this transcription factor acts as a direct functional mediator of the P4 signal . This proposal is supported by our ChIP-Seq studies which identified at least ten potential PGR binding sites that span introns 1–3 of the human PLZF gene . This DNA binding pattern concurs with previous studies which show that nuclear receptor binding sites are commonly found in introns located far beyond the confines of the promoter-proximal region of target genes [42 , 43] . Looping of the intervening DNA has been posited as one possible mechanism to enable long-range interaction by transcription factors with the promoter-proximal region [44 , 45] . Early PLZF induction following progestin/P4 administration also suggests that this transcription factor regulates target gene expression changes that occur early in the P4 signaling cascade . Through the integration of ChIP-Seq and RNA-Seq datasets , we revealed that EGR-1 represents one of these target genes for which its expression level is tightly controlled by PLZF to enable normal hESC decidualization by P4 exposure . Recognizing the GC-rich consensus sequence GCG ( T/G ) GGGCG in promoter regions of target genes , EGR1 ( also known as NGFI-A , Zif 268 , or Krox 24 ) is a Cys2-His2 zinc finger transcription factor , which belongs to an immediate early response gene family that includes EGR 2–4 [46] . A wide spectrum of extracellular signaling cues activates EGR1 , which in turn modulates cellular proliferation , differentiation , and apoptosis in diverse target tissues [17] . For example , EGR1 is induced by E2 in MCF-7 cells [47] , suggesting that EGR1 is a mediator of the established E2 mitogenic effects in this human breast cancer cell line . Studies on the Egr1 knockout mouse reveal that Egr1 is required for normal follicular development , ovulation , and luteinization [48]; however , uterine functionality was not assessed . Despite the aforementioned , earlier studies demonstrated that Egr1 is rapidly induced in the rat uterus by E2 [49 , 50] , suggesting that Egr1 may mediate the known mitogenic effects of E2 in this tissue . Interestingly , results from these studies are similar to our findings in the mouse in which Egr1 is rapidly induced to high levels by E2 in the uterus; however , we further showed that this induction is also markedly reduced by P4 with simultaneous induction of PLZF . Intriguingly , recent investigations show that unscheduled elevation of EGR1 levels compromises hESC viability in vitro [51] , indicating that strict controls on the levels of this transcription factor are critical for normal hESC function . These data may explain the need to reduce the expression levels of EGR1 as the pre-decidual fibroblast transforms into a decidual cell and underscores the importance of the P4:PGR: PLZF signaling axis in this regulatory process . Recent mouse studies further support the above by showing that while Egr1 is strongly expressed in the subluminal stroma surrounding the implanting blastocyst during early pregnancy [52] , Egr1 expression is significantly reduced following in vivo and in vitro artificial decidualization [52] . Moreover , Egr1 overexpression downregulates the expression of the decidual marker decidual/trophoblast prolactin-related protein ( Dtprp ) in murine uterine stromal cells , while inhibition of Egr1 upregulated the expression of Dtprp under in vitro decidualization conditions [52] . Collectively , these results agree with our findings in hESCs in which overexpression of Egr1 blocks decidualization and significantly attenuates the induction of the decidual biomarkers , PRL and IGFBP-1 . Interestingly , we demonstrate that PLZF knockdown does not change the expression levels of FOXO1A , HOXA10 or HAND2 during hESC decidualization; these three genes have been shown to be important for the hESC decidual response [14 , 53–57] . We also reveal that knockdown of FOXO1A , HAND2 or HOXA10 expression levels does not affect PLZF induction during hESC decidualization , suggesting that PLZF operates in a separate parallel pathway to FOXO1A , HAND2 and HOXA10 signaling . As further support for this conclusion , previous global transcript profiling of hESCs following FOXO1A or HOXA10 knockdown did not show changes in PLZF expression levels [58 , 59] . In related studies , our ChIP-Seq analysis did not detect PLZF binding sites within the IGFBP1 or PRL genes , indicating that the downregulation of IGFBP1 and PRL expression following PLZF knockdown is through an indirect signaling axis and is not through FOXOA1 regulation of these decidual biomarkers [60] . In conclusion , pregnancy success relies on the execution of a programmed sequence of events , with uterine decidualization following embryo implantation representing a crucial early event which ensures not only the establishment but also the long-term maintenance of the fetomaternal interface . Advancement in our mechanistic understanding of P4’s involvement in decidualization is predicated upon identifying the key effectors that mediate the P4 signal into a decidual response . An integrative analysis of genome-scale data , together with primary hESC studies , we disclose PLZF as a potent mediator of P4 responsiveness which is essential for hESC decidualization . We also reveal that PLZF controls EGR-1 expression levels , the perturbation of which can compromise decidual progression . Given that further understanding of the molecular mechanisms that underlie P4-driven decidualization is essential if we are to formulate new clinical modalities in the diagnosis , prognosis , and treatment of infertility based on a defective endometrium , we believe our findings offer a new mechanistic perspective on P4 responsiveness in the uterus which may contribute to novel fertility solutions in the future .
For in vitro decidualization studies , endometrial biopsies were obtained using a pipelle catheter or curette under sterile conditions from the uterine fundus of healthy women of reproductive age during the proliferative phase of their menstrual cycle . Subjects ranged in age between 27–38 years and had a normal uterus as evaluated by transvaginal ultrasound . Volunteers provided written informed consent prior to endometrial tissue biopsy , which was conducted in accordance with a protocol approved by the Institutional Review Board ( IRB ) at Baylor College of Medicine and the guidelines of the Declaration of Helsinki [61] . A portion of the endometrial biopsy was used for immunohistochemistry ( see below ) . The remainder of the biopsy was used to prepare human endometrial stromal cells ( hESCs ) as previously described [9 , 62] . Isolated hESCs were cultured in DMEM/F-12 media containing 10% fetal bovine serum ( FBS ) , 100 units/ml penicillin , and 0 . 1 mg/ml streptomycin ( termed hESC media ) ; studies were conducted with early passage primary hESCs ( ≤ four passages ) . For each experiment , at least three individual subjects provided the endometrial biopsy samples for cell preparation . For immunohistochemical analysis , endometrial biopsies also were taken from volunteers during the mid-secretory phase of their cycle ( day 19–23 ) and fixed in formalin overnight ( along with proliferative phase endometrial tissue ) before paraffin embedding the following day . For RNA interference based studies , hESCs were transfected in triplicate with non-targeting ( or control ) small interfering ( si ) RNAs ( D-001810-10-05 ) , siRNAs targeting PLZF ( L-005196-00-0005 ) , or PGR ( L-006763-00-0005 ) or FOXO1A ( L-003006-00-0005 ) or HAND2 ( L-008698-02-0005 ) or HOXA10 ( L-006336-00-0005 ) ( Thermo Scientific , Dharmacon RNAi Technologies , Chicago , IL ) in six-well culture plates . In each well , siRNAs ( 60 pmol ) were transfected into hESCs using Lipofectamine 2000 reagent in 1× Opti-MEM I reduced-serum media ( Invitrogen Corporation ) . Forty-eight hours after transfection , in vitro decidualization of hESCs was initiated by treating cells with 1× Opti-MEM I reduced-serum media containing 2% FBS , 10nM E2 , 1μM MPA ( Sigma-Aldrich ) , and 50 μM cAMP ( Sigma-Aldrich ) ( termed EPC media ) . The EPC medium was changed every two days , and hESCs harvested at defined time points as indicated [62] . Quantitation of PRL and IGFBP-1 transcript levels served as a positive molecular readout for hESC decidualization . Human ESCs were cultured in EPC media for 72 hours before PLZF and Input ChIP-Seq were performed by Active Motif , Inc . ( Carlsbad , CA ) . Human ESCs derived from six subjects were pooled before being fixed in 4% formaldehyde , snap-frozen , and shipped on dry ice to Active Motif . A goat polyclonal anti-human PLZF antibody ( sc-11146; Santa Cruz Biotechnology , Inc . ) was used for the PLZF ChIP assay . Enriched DNA from the ChIP assay was amplified and DNA libraries were sequenced on an Illumina platform , followed by alignment of sequenced reads ( 24 million reads ) to the human genome ( GRCh37/hg19 , February 2009 ) . Peak calling was performed by applying a threshold of 18 ( 5 consecutive bins containing 0 . 18 aligns ) before the resultant-called peaks were stored as Browser Extendable Data ( BED ) files . Due to the pronounced enrichment for TSS-proximal binding of PLZF by Cis-regulatory Element Annotation System ( CEAS ) analysis , genes associated with PLZF binding were called if the ChIP binding intervals were located within 950 bp from the TSS . Galaxy/Cistrome’s implementation of the Cis-regulatory Element Annotation System ( CEAS ) and Genomic Regions Enrichment of Annotations Tool ( GREAT ) tools were used for enrichment analyses on chromosomes and genomic annotations . Database for Annotation , Visualization , and Integrated Discovery Bioinformatics Resources ( DAVID ) v6 . 7 ( http://david . abcc . ncifcrf . gov/ ) ) and Gene Set Enrichment Analysis ( GSEA ) on Molecular Signatures Databases ( MSigDB ) collection ( http://software . broadinstitute . org/gsea/index . jsp ) analyses were performed on protein coding genes to identify enriched biological themes . Integration of PLZF ChIP-Seq data with RNA-Seq data sets was performed using a relational database system [63] . ChIP validation assays were performed on hESCs cultured in EPC media for seventy-two hours ( Active Motif ) . A goat polyclonal anti-human PLZF antibody and a rabbit anti-human PGR ( H-190 ) antibody ( sc-7208 , Santa Cruz Biotechnology , Inc . ) were used to immunoprecipitate PLZF and PR proteins with associated bound DNA from fragmented chromatin respectively . Enriched DNA fragments from the PLZF and PGR ChIP assays were subjected to standard PCR analyses using specific primers ( S5 Table ) for specific binding regions . Reverse cross-linked chromatin fragments that were not immunoprecipitated ( input DNA ) were used as an internal control . For quantitative real time PCR studies , total RNA was isolated from cells or tissue using the RNeasy total RNA isolation kit ( Qiagen Inc . , Valencia , CA ) . Complementary DNA ( cDNA ) was synthesized using the Bio-Rad Reverse Transcription kit ( Bio-Rad Laboratories , Inc . , Hercules , CA ) . Quantitative real time PCR analyses were performed using validated primers ( Applied Biosystems/Life Technologies , Grand Island , NY ) and TaqMan 2× master mix; ribosomal RNA ( 18S ) was used as an internal control for gene specific primers; the primer list is shown in S6 Table . For western analyses , total protein was extracted from uterine tissue using 1× protein lysis buffer ( 0 . 5% sodium deoxycholate , 1% Nonidet P-40 , 0 . 1% SDS , phosphate-buffered saline ( PBS ) , pH 7 . 4 ) . An equal amount of protein extract ( 20 μg ) was run on 4–15% gradient SDS-PAGE gels ( Bio-Rad Laboratories , Inc . ) and transferred to polyvinylidene difluoride ( PVDF ) membranes . Membranes were blocked with 1× TBS containing 0 . 5% Tween-20 and 5% non-fat dry milk powder . Subsequently , membranes were incubated with rabbit polyclonal anti-PLZF ( Santa Cruz Biotechnology , Inc . ) or rabbit anti-human PGR ( H-190 ) antibody ( Santa Cruz Biotechnology , Inc . ) or monoclonal anti-β-actin ( Sigma-Aldrich ) antibodies to detect PLZF , PGR and β-actin proteins respectively . Horseradish peroxidase-conjugated antibodies ( Santa Cruz Biotechnology , Inc . ) were used as secondary antibodies and the SuperSignal West Pico Chemiluminescent Substrate kit ( Pierce Biotechnology , Rockford , IL ) was used to detect the chemiluminescent signal . When hESCs reached 60–70% confluence on eight-well chamber slides in triplicate wells , media was changed to EPC media . The EPC media was changed every two days for a total of six days before hESCs were fixed in 2% paraformaldehyde and permeabilized in 0 . 2% Triton-X detergent . Cells were washed and blocked in 1× PBS with 0 . 5% normal goat serum before incubation overnight with the rabbit polyclonal anti-human PLZF primary antibody ( ( H-300 ) Santa Cruz Biotechnology , Inc . ) . To detect immunoreactivity , hESCs were incubated with the Alexa Fluor 488-conjugated goat anti-rabbit secondary antibody ( A-11008; Life Technologies ) to visualize immunopositive cells ( green fluorescence ) . Cell nuclei were visualized using the 4 , 6-diamidino-2-phenylindole stain ( DAPI; Sigma-Aldrich ) . Finally , slides were separated from the chambers and mounted using Slowfade mounting media before imaging . A lentiviral vector expressing EGR1 ( EX-OL00487-LX304 ) and a control vector expressing EGFP ( EX-EGFP-LX304-B ) were purchased from GeneCopoeia ( Rockville , MD ) . Lentivirus was produced in the Gene Vector Core at Baylor College of Medicine . Twenty four hours following transduction of hESCs with control virus harboring empty vector or lentivirus expressing EGR1 , media was replaced with EPC media to induce decidualization; lentiviral-mediated EGR1 overexpression was confirmed by real-time PCR . Briefly , paraformaldehyde-fixed tissues were processed and embedded in paraffin as previously described [62] . Tissue blocks were serially sectioned into 5-μm thick sections that were placed on Superfrost Plus glass slides ( Fisher Scientific , Inc . , Pittsburgh , PA ) . For immunohistochemical analyses , sections were deparaffinized before being rehydrated and boiled in a citric acid-based antigen unmasking solution ( Vector Laboratories , Inc . , Burlingame , CA ) . After blocking , sections were incubated overnight at 4°C with a rabbit polyclonal anti-human PLZF ( H-300 ) antibody ( 1:150 dilution; sc-22839 , Santa Cruz Biotechnology Inc . , Dallas , TX ) or anti-human EGR1 ( #15F7 ) antibody ( 1:150 dilution; Cell Signaling , Danvers , MA ) . After incubation with the primary antibodies , sections were incubated with the goat anti-rabbit IgG secondary antibody ( Vector Laboratories , Inc . ) , followed by incubation with ZyMax streptavidin-horseradish peroxidase conjugate ( Invitrogen Corporation , Carlsbad , CA ) . The 3 , 3’-diaminobenzidine ( DAB ) peroxidase substrate kit ( Vector Laboratories , Inc . ) was used to visualize immunoreactivity before sections were counterstained with hematoxylin . Cover slips were mounted onto stained sections using Slowfade mounting media ( Fisher Scientific , Inc . ) . In a recurrent photocycle of 12h lights on-off , mice were housed in temperature controlled ( 22°C ± 2°C ) rooms in an AAALAC accredited vivarium maintained by the Center for Comparative Medicine at the Baylor College of Medicine . A diet of irradiated Tekland global soy protein-free extruded rodent food pellets ( Harlan Laboratories Inc . , Indianapolis , IN ) and fresh water were provided ad libitum to mice . Animal studies were undertaken following the guidelines described in the Guide for the Care and Use of Laboratory Animals ( published by the National Research Council ( Eighth Edition 2011 ) ) . Animal protocols ( AN-1513; AN-544; and AN-4203 ) used in this research received prior approval from the Institutional Animal Care and Use Committee ( IACUC ) at Baylor College of Medicine . The PRKO mouse has been previously described [64] . The following anesthesia was used for surgery: ketamine 37 . 5mg , xylazine 1 . 9mg , and acepromazine 0 . 37 mg sq to 5ml with 2 . 45ml sterile water given at 0 . 75–1 . 5ml/kg BW , IP . Mice were euthanized by cervical disarticulation while under the plane of anesthesia; CO2 euthanasia was achieved with automated CO2 euthanasia chambers ( EUTHANEX ) . To evaluate uterine responsiveness to steroid hormone , mice were ovariectomized at six weeks-of-age and then rested for two weeks to ensure removal of serum ovarian steroid hormones before administration of exogenous hormone . Subcutaneous ( s . c . ) injection within the interscapular region of 100 μl sesame oil ( vehicle control ) , 1 mg P4 ( Sigma-Aldrich , St . Louis , MO ) , 100 ng E2 ( Sigma ) , or 1mg RU486 ( Sigma ) in 100 μl vehicle was used to administer hormones and the antiprogestin . Following hormone treatment , whole uterine tissue was processed from euthanized mice for RNA , protein , and/or histological analysis . For statistical analyses , a two-tailed student’s t test and one-way ANOVA with Tukey’s post-hoc test were performed using the Instat tool package version 3 . 0 ( GraphPad software Inc . , La Jolla , CA ) . Data with p values less than 0 . 05 were considered statistically significant . | Following embryo attachment to the uterine epithelium , the underlying decidualized stroma is critical for further invasion by the conceptus into the maternal compartment . Because endometrial decidualization is required early in the continuum of events that lead to a successful pregnancy , abnormal decidualization can contribute not only to implantation failure or early miscarriage , but may initiate adverse reproductive outcomes that manifest in subsequent pregnancy trimesters . Genome-wide transcriptional changes by progesterone are known to underlie decidualization; however , the pivotal signals that functionally enable progesterone-driven decidualization are not fully known . Using an integrative analysis of genome-scale data along with studies on primary human endometrial stromal cells ( hESCs ) , we reveal that the promyelocytic leukemia zinc finger ( PLZF ) transcription factor is rapidly induced by progesterone , and its induction is essential for progesterone-dependent hESC decidualization . Although PLZF in turn governs a remarkable array of target genes in the hESC , we demonstrate that PLZF tightly regulates the expression level of the early growth response 1 ( EGR1 ) transcription factor , the perturbation of which compromises progesterone dependent decidualization . Together , our findings provide a new mechanistic perspective on progesterone action in the uterus which may furnish new opportunities for the formulation of more effective fertility solutions in the future . | [
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] | 2016 | The Promyelocytic Leukemia Zinc Finger Transcription Factor Is Critical for Human Endometrial Stromal Cell Decidualization |
Many fundamental cellular processes such as gene expression are tightly regulated by protein allostery . Allosteric signal propagation from the regulatory to the active site requires long-range communication , the molecular mechanism of which remains a matter of debate . A classical example for long-range allostery is the activation of the methionine repressor MetJ , a transcription factor . Binding of its co-repressor SAM increases its affinity for DNA several-fold , but has no visible conformational effect on its DNA binding interface . Our molecular dynamics simulations indicate correlated domain motions within MetJ , and quenching of these dynamics upon SAM binding entropically favors DNA binding . From monitoring conformational fluctuations alone , it is not obvious how the presence of SAM is communicated through the largely rigid core of MetJ and how SAM thereby is able to regulate MetJ dynamics . We here directly monitored the propagation of internal forces through the MetJ structure , instead of relying on conformational changes as conventionally done . Our force distribution analysis successfully revealed the molecular network for strain propagation , which connects collective domain motions through the protein core . Parts of the network are directly affected by SAM binding , giving rise to the observed quenching of fluctuations . Our results are in good agreement with experimental data . The force distribution analysis suggests itself as a valuable tool to gain insight into the molecular function of a whole class of allosteric proteins .
Protein allostery plays a key role in the regulation of cellular functions such as transcription or enzymatic action [1] . It crucially governs the formation of protein or protein-DNA complexes as well as the functional activity of individual proteins . Allosteric signals used by nature are diverse , ranging from ligand binding to reversible covalent modifications such as phosphorylation , or changes in the environment like pH or temperature . Intriguing examples are allosteric proteins in which effector molecules bind distal to the active site [2] , [3] . A fundamental question is how the allosteric perturbation is transmitted through the protein to the active site for functional regulation . Can we understand and predict the mechanism and the network of interactions that propagate an allosteric signal ? Answering this question is a prerequisite for functional mutagenesis and rational design of allostery . Sequence-based statistical analysis has proven highly successful to detect signal propagation pathways within and between allosteric proteins on the basis of evolutionary constraints [4] , [5] . On the theoretical side , various thermodynamic concepts for inter-domain communication in allosteric proteins have been established [6] , [7] . As yet , the molecular basis for long-range allosteric coupling between the regulatory and active site of a protein remains a matter of debate . This is why a range of experimental and computational techniques to monitor conformational changes involved in allostery have been developed and applied [7] , [8] , among others NMR [9] , molecular dynamics ( MD ) simulations [10] , [11] , normal mode analysis and elastic network models [12] , [13] . The basic premise of the above approaches is a conformational transition between two distinct states or a shift in a pre-existing conformational ensemble upon allosteric perturbation . In a commonly accepted picture , allosteric signals cause a perturbation at the regulatory site of the protein , analogous to an externally applied force . The perturbation then dissipates as internal strain or energetic coupling through the protein to the active site [14] . Signal propagation in turn causes conformational rearrangements , inducing an enhancement or decrease in the protein's activity . However , examples of long-range allosteric communication in the absence of any obvious conformational changes [9] , [15] question this picture , showing that allostery does not necessarily rely on a change in mean atomic coordinates . Instead , allosteric strain can dissipate through rigid scaffolds without detectable conformational rearrangements . A more fundamental understanding of allostery would thus require a way to directly follow strain propagation through proteins . This could reveal the allosteric network in a protein even in the absence of - or prior to the occurrence of - conformational changes . We recently presented a method termed force distribution analysis ( FDA ) , based on MD simulations , that allows to detect propagation of internal strain caused by an external signal through proteins . The high sensitivity of the method makes it possible to even detect propagation through stiff materials , where a signal will propagate causing only minimal conformational changes that are below the threshold of experimentally accessible resolution . We have previously demonstrated the feasibility of FDA to detect force propagation in two mechanically perturbed proteins , namely the highly robust titin immunoglobulin domain I27 [16] , and silk crystalline units [17] . While classical approaches focus on conformational changes or ensemble redistributions as a consequence of the signal-induced strain , such as normal mode analysis or essential dynamics [18] , FDA sets out from the strain distribution itself . This renders FDA a perfectly fitted tool to elucidate the mechanism underlying allosteric signaling in proteins in general , be it with or without the involvement of structural rearrangements . We here chose to test the feasibility of FDA to detect allosteric networks in proteins using the classical textbook example of the methionine repressor protein MetJ [19] . MetJ is a challenging candidate , as it features long-range allosteric communication , yet without any noticeable changes in protein structure upon effector binding . MetJ is a transcription factor in the met regulon of Escherichia coli , the gene regulatory control system for methionine biosynthesis [20] . MetJ regulates the transcriptional levels of its own gene and those of several other proteins . Repressor activity results from binding to its operator , a specific 8 bp DNA sequence ( the “metbox” ) , located in the promoter regions of genes regulated by MetJ . Changes in sequence of the metboxes are supposed to explain different regulatory activity [20] , [21] . MetJ forms a homodimer in its native state [22] . In case of multiple adjacent metboxes it may form complexes of several homodimers arranged in a wheel-like structure around the DNA [23] . DNA binding of MetJ is regulated by its co-repressor , S-adenosylmethionine ( SAM ) , an end product of methionine biosynthesis , Fig . 1A . Sensitivity for DNA is increased several-fold [24] , [25] upon co-repressor binding . Of special interest is that SAM binds distant from the DNA binding site , with a minimal SAM-DNA distance of in crystal structures [26] . Holo and apo structures do not show significant structural changes [15] . For this reason it remains controversial how SAM influences DNA binding . S-adenosylhomocysteine ( SAH ) , a SAM analogue , binds MetJ with a binding affinity similar to SAM , but has no effect on its affinity for the operator ( S . Philipps , Leeds Univ , 2009 , personal communication ) . The main difference between SAM and SAH ( Fig . 1B ) is a positive charge on the sulfur atom of SAM , and it has been suggested that the increased sensitivity upon co-repressor binding is of purely electrostatic nature [27] . On the other hand , introduction of positive charges by a series of point mutations could not substitute the need for co-repressor [28] . Based on the force distribution pattern observed within the MetJ homodimer , we here propose a new model for MetJ activation upon cofactor binding . We measure directed propagation of internal strain from the SAM binding site to distinct residues in the DNA binding interface , through a specific network of a few key residues . The consequence is a wide-spread quenching of slow fluctuations and relocation and stiffening of specific side chains at the MetJ-DNA interface , leading to increased protein - DNA interaction . A distinct interaction pattern of individual residues with the co-repressor allows MetJ to fine-tune its response to co-repressor binding , explaining the inability of SAH to act as a co-repressor . Our results yield a molecular basis for MetJ allosteric function and are consistent with previous experimental studies .
We carried out extensive MD simulations to elucidate the force distribution and conformational properties of MetJ . We used crystal structures of MetJ ( PDB code 1CMC [15] ) and MetJ in complex with DNA ( PDB code 1CMA [26] ) as starting point for our simulations . Throughout the manuscript , we will use the terms MetJ for the system without DNA and MetJ-dna for the MetJ-DNA complex . In both cases , simulations of the holo and apo forms were performed for comparison . Apo forms were created by deleting the bound SAM molecules from the crystal structures . An apo structure of MetJ is available , but as force distribution analysis is very sensitive to structural changes we decided to use the same crystal structure as basis for our simulations . Structures for Q44K , a mutant not relying on cooperativity to be functional [29] , exist as well . Yet , as the altered charge distribution alters the DNA recognition pattern , though not the allosteric effect itself , we decided not to further investigate Q44K . For each of the five systems , 10 independent 30 ns MD simulations were performed , totaling 300 ns of simulation time , respectively . In agreement with crystallographic data [15] , our simulations do not show major deviations between holo and apo forms . The overall backbone root mean square deviation ( RMSD ) of average structures is 0 . 66 Å for MetJ-dna and 0 . 64 Å for MetJ . This compares well with the crystal structures where we find a backbone RMSD for holo and apo structures of 1 . 63 Å which lowers to 0 . 59 Å after excluding poorly resolved loop regions having different conformation ( residues 12–20 and 77–84 ) , Fig . 1C . Crystal waters in the protein-DNA interface of 1CMA were found to quickly move into the bulk solvent and are thus unlikely to bridge specific interactions . To elucidate distribution of the allosteric signal induced by co-repressor binding , we directly calculated forces between each pair of atoms and from our MD trajectories . We here analyze scalar pair-wise forces , which in contrast to the vectorial representation are unaffected by rotation of the system during the simulations . Observing pairwise forces has the advantage that forces do not average to zero over time , thus being the measure of choice for internal strain in systems equilibrated under a perturbation . Forces are calculated individually for bonded and non-bonded ( electrostatic and van der Waals ) interactions below the cutoff distance using the interaction potential defined by the Amber03 [30] force field . Long-range interactions as well as solvation effects such as screening of electrostatic forces and hydrophobic forces are not directly included in , which is calculated only for the solutes and within the non-bonded cut-off . We however indirectly accounted for these effects by calculating forces from a system simulated in explicit solvent and with full electrostatics . Details are given in Methods . Propagation of the mechanical perturbation caused by SAM binding is measured as the difference in pairwise force , , between the apo and holo forms of MetJ/MetJ-dna . For convergence , forces for each system were averaged over all ten equilibrium trajectories , each 30 ns in length . To reduce noise further , mainly resulting from slow side chain fluctuations that cannot equilibrate during simulation time , data were normalized as described in Methods . Dimensionless normalized changes in force are denoted . The MetJ homodimer has a high degree of symmetry , and we thus expect the force distribution pattern to be highly symmetric as well . We checked this by calculating correlation coefficients between residue wise forces , see Methods . Indeed , we find the force propagation pattern for the monomers to be very similar in all systems . For MetJ , residue wise forces correlate with , Fig . S2A . The MetJ-dna structure shows a less symmetric pattern , with , Fig . S2B . The lower symmetry of MetJ-dna might be a result of the lower resolution of the 1CMA crystal structure ( 2 . 8 Å for 1CMA vs . 1 . 8 Å for 1CMC ) or of the only partially resolved DNA . Force distribution at the DNA binding site ( Fig . 2A ) reveals that remote MetJ binding induces a high degree of strain at distinct regions of the MetJ-DNA interface . In particular , Arg40 and a loop formed by residues 50–53 are subjected to high strain . The presence of the co-repressor thus is sensed by the DNA binding site , apparently via a long-range propagation of force from the bound SAM molecule through the protein scaffold to the MetJ-DNA interface . Importantly , the force distribution pattern was equally observed in the absence of DNA , Fig . 2B . In fact , forces in MetJ and MetJ-dna distribute in a very similar way , yielding a correlation of , Fig . 2C . First , this is strong evidence that the observed change in forces is a result of SAM binding , independent from the presence of DNA . Second , as the initial crystal structures differ in resolution and conformation , the significant correlation highlights that the distribution pattern is robust with regard to the starting structure . On the basis of FDA , we next investigated which protein structural elements are key to the strain distribution , allowing communication between SAM and the protein-DNA interface over a distance of more than 1 nm . Within the protein scaffold , we observe force propagation through helix B ( B′ ) and forces are transmitted via side chain interactions onto helix A ( A′ ) , which in turn forms various side chain contacts with the DNA , Fig . 3A+B . Force propagation is highly non-isotropic and directed . This is to say , when compared to helix A and B , we see relatively little changes in pair-wise forces for the and the loops formed by residues 12–20 , both in direct contact with the DNA , as well as for helix C ( C′ ) , Fig . 3C . In agreement with the low allosteric strain in the , this motif , even though binding to the major groove of the metbox , has been found to play a role in DNA sequence specificity , but not in the allosteric regulation of DNA binding affinity [31] . Only a few side chains of helix A show significant changes in pair wise force , the strongest of which is observed for Glu39 , Arg40 , Arg42 and Arg43 . Out of these residues only Arg40 is in direct contact with the DNA . This observation is remarkable as an almost complete loss of binding affinity was reported for mutation of Arg40 and its spacial neighbor Thr37 , but not for mutation of others in direct contact with DNA [31] . Thr37 , however , has been suggested to be involved in enhancing cooperativity , thereby only indirectly regulating DNA affinity . In agreement , we do not find Thr37 to be under SAM-induced strain . We find two inter-related mechanisms of force propagation responsible for the specific targeting of the above mentioned structural elements . First , SAM strongly exerts a direct strain onto a set of MetJ residues , as reflected by extra-ordinarily high forces between the co-repressor and these residues , , Fig . 3D . Most importantly , the adenosyl group of SAM strongly interacts with Glu39 and Arg42 in helix A , influencing their dynamics ( see below and Fig . 3D ) . Second , SAM features repulsive forces with helix B , inducing a high strain in the helix backbone hydrogen bonds . This apparently involves slight helix bending , Fig . 4A . Indeed , measuring the angle defined by the atoms of residues Ala64 , Cys58 and Asn53 shows a bending upon SAM binding of for MetJ and for MetJ-dna . We note that it is the significant difference in hydrogen bond forces , not in the mere atomic coordinates , between apo and holo form , that serves as robust indication for SAM-induced signal propagation . Helix bending in turn imposes strain on the salt bridge between Glu59 in helix B and Arg43 in helix A by minor conformational rearrangements , Fig . 4A+B . We measure high change in force between these residues , suggesting this electrostatic interaction , buried in the protein core , to propagate force between helix B and helix A . Both mechanisms , direct forces imposed from SAM onto key residues in helix A , and propagation of forces from SAM via bending of helix B , result in inconspicuous rearrangements at the DNA binding interface; most notably in the loop linking helices A and B ( residues 50–53 ) and Arg40 , as described above , Fig . 2A+B . Repositioning of Arg40 upon SAM binding is accompanied by an adjustment of the side chain packing with its direct neighbors , Thr37 and Asn53 , Fig . 4C . Again , pairwise forces here served as a measure for signal propagation , rather than the only minor , yet reproducible coordinate changes ( as for example a change of the angle in Asn53 between , and found for both , MetJ-dna as well as MetJ ) . The described rearrangement of Arg40 caused by propagation of strain entails a strengthening of its saltbridge with DNA . From FDA , we measured an increase in attraction between Arg40 and DNA of . Overall , the potential energy between MetJ and DNA decreases by from in the holo to in the apo form , as a result of allosteric signaling by the co-repressor . The loops formed by residues 12–20 ( referred to as loop 1 ) suggest themselves to be involved in the allosteric mechanism , as they strongly differ in conformation between the 1CMC ( MetJ ) and 1CMA ( MetJ-dna ) crystal structures and are in direct contact with the DNA , Fig . 1C . NMR data for these loops shows a strong quenching of ns time-scale fluctuations upon co-repressor binding ( Steve Homans , Leeds University , 2009 , personal communication ) . In good agreement with these experimental findings our simulations of the MetJ-dna system show a strong decrease in backbone RMSF for loop 1 residues upon SAM binding , as well as stiffening of helix C , Fig . 5A–C . Quenching is observed for both the MetJ and MetJ-dna system , though less pronounced for the former ( see below ) . Remarkably , principal component analysis ( PCA ) on the trajectory data reveals the dynamics of the distal loop 1 and helix C regions to be highly coupled , Fig . 5D , and the dynamics of both MetJ monomers to be highly cooperative . The lowest frequency mode ( Eigenvectors 1–3 ) for apo and holo structures of MetJ-dna describe highly similar fluctuations , yet at very different amplitudes . Strong quenching of fluctuations is reflected by a decrease of the highest Eigenvalue from 120 ( apo ) to 28 ( holo ) , Fig . S1 . These observations are supported by entropy calculations based on Schlitter's formula [32] . Upon SAM binding , we find a decrease in entropy of for MetJ-dna and for MetJ , see also Table 1 . The quantitatively different , yet qualitatively equivalent , results might be caused by the different crystal structures used , i . e the differences for loop 1 and adjacent residues . Overall , we find the stiffening effect of SAM to be independent from the presence of DNA . The question arises how the distal helix C and loop 1 regions are dynamically linked through a largely rigid core of the MetJ-DNA system . To elucidate the communication pathway , we performed PCA on residue averaged pair-wise forces , , here termed force-PCA . Again , observing forces directly has the unique advantage to allow for following the complete propagation pathway , including parts showing only subtle coordinate changes . Force-PCA on MetJ-dna revealed a network of correlated changes in pair-wise forces , Fig . 5E . The network spans through the protein core , linking helix C and loop 1 , the latter of which is connected to the rest of the network via residues Tyr11 , Ile28 , Lys31 and Glu55 . Synchronization of the fluctuations between both monomers is achieved by force propagation along helix A and the . We found the allosteric signal caused by SAM binding to target large parts of helix A , in particular Glu39 , Arg40 and Arg42 , resulting in wide-spread stiffening , Fig 4C . Helix A accounts for a large part of the network propagating fluctuations , moreover it directly is part of the link between helix C and loop 1 , Fig . 5E . In summary , SAM binding alters correlated forces linking loop 1 and helix C thus affecting the dynamics of these regions . The SAM analogue SAH has no regulatory function , i . e . no impact on the MetJ activity for binding to DNA , yet has the same binding mode and similar binding affinities as SAM ( S . Philipps , Leeds University , 2009 , personal communication ) . Based on this observation , an entirely electrostatic activation of MetJ by the positively charged SAM has been suggested [27] . We decided to elucidate differences between SAM and SAH binding , and to this end performed simulations of MetJ-dna in complex with SAH as co-repressor . We modeled the MetJ-SAH structure by removing the group from the sulfur atom of SAM in the 1CMA crystal structure used as template . The overall conformation of MetJ-dna is not affected when replacing SAM by SAH , both structures have a backbone RMSD of only 0 . 42 Å . Also , the potential energy between protein and DNA is quasi identical to the energy measured for MetJ-SAM and DNA . As for the co-repressor , our simulations show strong quenching of fluctuations upon SAH binding , yet quenching is less distinct . This is reflected by higher backbone-RMSF for MetJ-SAH throughout the protein , Fig , 5B , as well as a higher eigenvalue of 48 for the first eigenvector , what is significantly above 28 , the value measured for SAM . Both eigenvectors describe a very similar mode of fluctuation , Fig . S1C . The flexibility of the bound ligand itself is increased as well . We measured an almost twofold increase in RMSF for SAH when compared to SAM ( 0 . 89 Å vs . 0 . 57 Å ) , apparently due to the loss of backbone interactions with SAM's positive charge . Indeed , and unsurprisingly , the changes in direct interactions between the co-repressor and individual residues are significant , Fig . 6A . Removing the positive charge alters the charge distribution of SAM's whole methionine group , and we see changes in interaction even for residues as far as in helix A ( residues 39 to 43 ) , though most of the observed changes affect residues in direct proximity to the sulfur atom ( residues 59 to 67 ) . These changes lead to wide-spread alterations in the overall force propagation pattern , which are most pronounced in helix C and the proceeding loop , Fig . 6C . Interestingly , we find high changes in forces for Tyr11 and Ile28 , both of which were found to link fluctuations of loop 1 with helix C by force-PCA . However , this effect is only present in the domain with the full DNA fragment resolved ( residues 106–209 in the 1CMA structure ) , and thus further validation is necessary . As the differences in binding affinity between SAM and SAH are of primarily entropic nature , we performed entropy calculations on MetJ-dna based on Schlitter's formula [32] . Vibrational entropies were calculated on the whole trajectory data totaling 300 ns per system and are sufficiently converged to allow semi-quantitative comparisons between SAM and SAH , Fig . S3 . We found an entropy difference of between SAM and SAH as co-repressor , of which the protein dynamics with accounts for the major contribution . All values are given in Table 1 . The absolute conformational entropies of ( apo ) and ( holo ) per residue are in agreement with previous estimates for other proteins [33] , [34] . The values clearly show that there is a significant increase in entropy when substituting SAM by SAH , consistent with the observed difference in regulatory function . Both , the overall RMSF and the entropies suggest SAM to reduce MetJ flexibility more efficiently than SAH .
We have analyzed force distribution and dynamics in MetJ , a stiff allosteric protein regulated by SAM , its co-repressor . FDA allowed us to identify the network of interactions guiding force modulation within MetJ by cofactor binding . Experimental data , among others the inactivity of SAH as a co-repressor , suggest that a long range electrostatic interaction between DNA and the positive charge on SAM may exclusively explain MetJ activation [35] . Notwithstanding , there is evidence from mutagenesis experiments that charge alone cannot explain MetJ activation [28] . We here suggest strain propagation by subtle alterations of the MetJ structure as an important mode of allosteric signal propagation . The highly anisotropic distribution of internal strain leads to conformational re-adjustments at the interaction interface , mainly of Glu39 , Arg40 , Arg42 , Arg43 and residues 50–53 . Our simulations thus predict adjustments of these specific protein-DNA interactions to be an important factor for efficient DNA binding . Such a mechanism would allow MetJ to easily move along or between DNA strands until the target side is found , thereby speeding up target site location as recently proposed [36] . While the importance of this communication pathway has been experimentally probed by the loss of allosteric function upon mutation of residues identified as key residues by FDA , it is independent of the positive charge on SAM , as we find it similarly for SAH . This pathway therefore apparently causes or is complemented by an additional allosteric mechanism unique to SAM . We find the major SAM-dependent allosteric function of MetJ to come from an entropic contribution due to quenching of slow backbone and fast side chain dynamics . Only for SAM , the force network communicating the allosteric signal between loop 1 and helix C can substantially reduce correlated fluctuations . This is supported by theoretical models [37] as well as NMR data that suggest dynamics to play an important role ( Steve Homans , Leeds University , 2009 , personal communication ) . The major correlated motion that is quenched involves parts distant to each other as well as to the co-repressor binding site . Again , measuring correlated forces instead of coordinates revealed the role of the protein core in this long-range communication and allosteric regulation . We find a strong increase in entropy when substituting SAM by SAH , suggesting that the regulatory difference between SAM and SAH is of entirely entropic nature . It is the differential effect of SAM and SAH on the correlated forces involved in this motion that is likely to be responsible for the observed difference in allostery . Dynamics are increasingly revealed as a regulatory driving force [38]–[40] and have recently been found for another transcription factor , the CAP protein [9] . We here find a similar mechanism for MetJ , suggesting that changes in dynamics upon cofactor binding may be a commonly used regulation pattern . Long-range allostery in the absence of any noticeable conformational change as featured by MetJ has remained a challenge for structure-based experimental and theoretical approaches . In combination with conventional analysis of the MetJ dynamics , we find FDA an optimal tool to track an allosteric pathway in MetJ . Signal propagation was found to be largely hidden in unremarkable shifts in atomic coordinates . Yet , these mere conformational shifts , as revealed by FDA , can involve large changes in forces for strongly interacting atom pairs , resembling “stiff springs” in the protein interaction network . Monitoring forces instead of coordinates therefore renders FDA highly sensitive . Pure conformational analysis would simply overlook rearrangements of the magnitude reported here , especially as properties such as root-mean square deviations or fluctuations are easily dominated by slow sub-domain movements , as it is the case for MetJ , Fig . 5C . By considering pair-wise forces which are , by definition , dominated by strong and relatively short-ranged interactions , such large fluctuations have only minor influence . Pair-wise interactions have the additional advantage of being independent from any fitting scheme , as conventionally used for RMSD or RMSF calculations , thereby not introducing any bias by the arbitrary choice of a reference structure . The same multivariate statistical methods , such as PCA , that are used for the analysis of coordinate based trajectory data can be applied to pair-wise forces . Again , one has the advantage of being able to observe relations that would otherwise be below the sensitivity of the method . We recently determined the force bearing scaffold in a titin immunoglobulin domain , a protein mainly designed to withstand mechanical load by means of FDA [16] . Here , we present the first successful application to a stiff allosteric protein , opening the road to better understand the function of a whole class of proteins , including enzymes , by examining their internal force network . We note that FDA does not require extensive sampling of an allosteric conformational transition , which at current simulation time-scales is out of reach for most proteins . This is an unique advantage over other MD based simulation techniques used for studying protein allostery . FDA is content with monitoring the development of internal strain prior to the eventual shift in the protein conformational ensemble . We predict forces averaged over a total simulation time in the sub-microsecond range to suffice for the analysis of much slower allosteric signaling pathways . Importantly , while we here modified the Gromacs simulation suite to add FDA functionality , virtually any MD simulation package can be easily modified to include FDA at practically no additional computational expense , as pair-wise forces are anyways calculated at each time step . Our results highlight the strength of FDA as a tool supporting experimental design , as it can straightforwardly be verified by experimental studies . In particular , our results suggest Arg40 , Thr37 and Asn53 at the MetJ-DNA interaction interface to be important for allosteric function . Mutations of Arg40 and Thr37 have indeed been previously shown to abolish SAM-dependent allosteric regulation of MetJ [31] . In addition , we predict mutation of Glu59 and Arg43 , forming the salt bridge between helix A and B , and the crucial SAM interaction partners Glu39 and Arg42 to lower the co-repressor activity of SAM . | Proteins carry out most of the cellular processes , from metabolic reactions to the regulation and expression of genes . Tight and effective regulation of the executing protein machinery is commonly achieved by allostery . The only general requirement for allosteric communication is the transmission of a signal , e . g . , the binding of a cofactor , from the ligand binding site to the allosteric ( active ) protein site; in other words an internal propagation of strain . Based on molecular dynamics simulations , we recently presented a method that allows visualization of force distribution in proteins . We here applied this method to MetJ , a transcription factor whose activity is regulated by a co-repressor . Interestingly , co-repressor binding does not cause visible structural changes , yet increases DNA binding affinity manyfold . We were able to reveal a network linking fluctuations of distal parts of MetJ , including the DNA binding interface . Mechanical strain caused by SAM binding propagates to certain key residues , thereby altering fluctuations and finally resulting in increased DNA binding affinity . By directly monitoring ligand induced strain , instead of conformational changes , which might be absent or slow , our force distribution analysis suggests itself suitable to detect the mechanically crucial motifs in allosterically regulated protein machineries . | [
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] | 2009 | Dynamic Allostery in the Methionine Repressor Revealed by Force Distribution Analysis |
While innate behaviors are conserved throughout the animal kingdom , it is unknown whether common signaling pathways regulate the development of neuronal populations mediating these behaviors in diverse organisms . Here , we demonstrate that the Wnt/ß-catenin effector Lef1 is required for the differentiation of anxiolytic hypothalamic neurons in zebrafish and mice , although the identity of Lef1-dependent genes and neurons differ between these 2 species . We further show that zebrafish and Drosophila have common Lef1-dependent gene expression in their respective neuroendocrine organs , consistent with a conserved pathway that has diverged in the mouse . Finally , orthologs of Lef1-dependent genes from both zebrafish and mouse show highly correlated hypothalamic expression in marmosets and humans , suggesting co-regulation of 2 parallel anxiolytic pathways in primates . These findings demonstrate that during evolution , a transcription factor can act through multiple mechanisms to generate a common behavioral output , and that Lef1 regulates circuit development that is fundamentally important for mediating anxiety in a wide variety of animal species .
Recent work has demonstrated that innate behaviors can be highly conserved across diverse animal models [1] . Individual neuronal populations that mediate these behaviors are specified during embryogenesis by transcription factors that can also be conserved across species [2] . However , molecular signaling pathways that regulate the development of common behavioral circuits have not been identified . As brain anatomy and connectivity change through evolution , it is possible that a single pathway could act through diverse molecular and cellular targets to establish a single behavioral output , which is the ultimate constraint on gene function . Wnt/ß-catenin signaling plays important evolutionarily conserved roles in brain development , and thus represents an ideal candidate pathway to link gene regulation with the evolution of behavioral circuits . The Wnt pathway acts through Tcf/Lef transcription factors [3] , and both Wnt signaling and Lef1 are required for neurogenesis in the zebrafish hypothalamus [4] , an evolutionarily ancient brain structure that regulates innate behaviors [5] . However , the identity and behavioral function of Lef1-dependent hypothalamic neurons , and their degree of evolutionary conservation , are unknown . Here , we show that Lef1 is required for the differentiation of hypothalamic neurons that inhibit anxiety in both zebrafish and mice , but through divergent molecular and cellular mechanisms in the 2 species . Generation of neurons expressing corticotropin-releasing hormone binding protein ( crhbp ) requires Lef1 in zebrafish but not in mice , whereas neurons expressing Pro-melanin concentrating hormone ( Pmch ) are Lef1-dependent in mice but not in zebrafish . Furthermore , zebrafish and Drosophila have common Lef1-dependent crhbp expression in their respective neuroendocrine organs , consistent with an ancient conserved pathway that has diverged in mammals . Finally , the Genotype-Tissue Expression ( GTEx ) project [6] reveals a top-ranked positive correlation between CRHBP and PMCH in the human hypothalamus , suggesting co-expression and/or co-regulation . Both genes are also correlated with LEF1 expression in humans , and are expressed in the same region of the marmoset hypothalamus , consistent with a conserved regulatory pathway in primates . These findings suggest that the gene expression network regulated by a transcription factor can change during evolution while still generating a common behavioral output . Our data also suggest an anxiolytic role for Wnt signaling in the human hypothalamus , with potential implications for the etiology and treatment of anxiety disorders .
We sought to first characterize the earliest cellular defect in lef1 null zebrafish mutants [4] , so that we could perform a transcriptome analysis at that stage to identify Lef1-dependent genes . Despite grossly normal morphology , mass , and brain size , lef1 mutants have a smaller caudal hypothalamus ( Hc ) at 15 days post-fertilization ( dpf ) [4] , and we found that the size reduction occurred at as early as 3–4 dpf ( Fig 1A and S1A and S1B Fig ) . At 3 dpf the tissue already contained fewer Wnt-responsive cells [7] ( Fig 1B ) , as well as fewer serotonergic cells and ventricular GABAergic HuC/D+ neurons ( Fig 1C and S1C Fig ) . However , th2:GFP+ dopaminergic neurons [8] were unaffected ( S1D Fig ) , indicating that not all neuronal subtypes are Lef1-dependent . In addition , the number of BLBP+ cells was increased ( S1E Fig ) , confirming an inhibitory role of Wnt signaling in the formation of hypothalamic radial glia [4 , 9] . To determine the cellular mechanism underlying the decreased populations in lef1 mutants , we measured apoptosis and proliferation . We observed an increase in p53-dependent apoptosis within the Hc at 3 dpf ( Fig 1D ) , but no change in proliferation at 3 dpf and beyond ( Fig 1E and S1F–S1H Fig ) . Rescue of apoptosis by loss of p53 ( Fig 1D ) did not restore HuC/D expression in lef1 mutants ( Fig 1F ) , consistent with a primary defect in progenitor differentiation . To confirm a failure in neurogenesis , we performed BrdU pulse-chase experiments , and observed fewer newly born serotonergic and ventricular HuC/D+ cells in lef1 mutants ( S1I Fig ) . To test whether Lef1 functions cell-autonomously , we transplanted cells from lef1+/- donors into the hypothalamic anlage of lef1 mutant hosts during gastrulation , and observed rescue of ventricular HuC/D expression only in donor cells ( Fig 1G ) . Together these data suggest that Lef1 functions cell-autonomously to promote hypothalamic neurogenesis; in lef1 mutants , neural progenitors fail to differentiate and subsequently undergo cell death , leading to a smaller Hc . Our data also justified 3 dpf as the optimal time point to perform a transcriptome analysis . To identify Lef1-dependent genes , we next performed RNA sequencing ( RNA-seq ) analysis of whole hypothalami dissected from 3 dpf control and lef1 mutant zebrafish embryos , and found 144 genes with an adjusted P value ( AdjP ) <0 . 1 , among which 53 genes had a fold change >2 ( Fig 2A , S2 Table ) . Most of these genes had reduced expression in lef1 mutants ( Fig 2A ) , consistent with Lef1 functioning as a Wnt transcriptional activator [10] . Surprisingly , Ingenuity Pathway Analysis ( IPA ) identified Lef1-dependent genes as being most highly associated with anxiety and depressive disorder ( Fig 2B and S3 and S4 Tables ) . In contrast , genes associated with other hypothalamus-mediated behaviors , such as feeding ( neuropeptide Y [npy] , agouti-related protein [agrp] , and proopiomelanocortin [pomc] ) or sleep ( hypocretin [hcrt] ) , were unaffected ( S2 Table ) . We performed in situ hybridization on 3 dpf offspring of lef1+/- incrosses and confirmed that all Lef1-dependent genes with specific detectable hypothalamic expression showed predicted changes in approximately 25% of embryos , consistent with Mendelian segregation ( Fig 2C and 2D and S2A–S2C Fig ) . These included several known Wnt targets such as sp5a and sp5l [11] ( Fig 2C ) , and anxiety-related genes identified from IPA ( Fig 2B and 2D ) . Expression of neuronal markers such as crhbp and 5-hydroxytryptamine receptor 1A b ( htr1ab ) , was lost specifically in the Hc of lef1 mutants while remaining intact in the rostral hypothalamus ( Fig 2D ) , resulting in their relatively small fold change in whole hypothalamus RNA-seq analysis ( S2 Table ) . In contrast , expression of other genes , such as 2 phosphodiesterase 9a ( pde9a ) paralogs , was lost in the rostral hypothalamus and Hc of lef1 mutants ( Fig 2D and S2A Fig ) , consistent with lef1 expression in both regions ( Fig 2C ) . We also observed expression of Lef1-dependent genes in the Hc of wild-type ( wt ) adult zebrafish ( S2D Fig ) , suggesting the presence of Wnt activity and Lef1-dependent neuronal populations throughout life . Together these results suggested that lef1 mutants might have an anxiety-related behavioral phenotype . lef1 mutants raised with siblings had decreased survival and size ( S3A and S3C Fig ) . When separated at 15 dpf , mutants survived normally ( S3B and S3C Fig ) , but were still smaller than control siblings at culture densities that maximized their growth ( Fig 3A and S3D Fig ) , a phenotype potentially due to enhanced anxiety [12] . We then performed a novel tank diving test to measure anxiety-related behavior [13] . We found that lef1 mutant larvae had a longer latency to enter the upper half of a novel tank and spent less overall time in this zone during the initial exploration phase ( Fig 3B and 3C and S1 Video ) , consistent with elevated anxiety . Notably , lef1 mutants travelled less distance during this phase , partially due to more frequent freezing behavior as indicated by increased time in immobility ( Fig 3D and 3E and S1 Video ) , and again consistent with elevated anxiety . Importantly , lef1 mutants no longer displayed anxiety-related behavior after the exploration phase ( Fig 3F ) . The body growth and anxiety phenotypes in lef1 mutants could be explained by reduced expression of multiple hypothalamic genes including crhbp ( Fig 2D ) , which encodes a corticotropin-releasing hormone ( CRH ) inhibitor [14] . However , pleiotropic phenotypes in zebrafish lef1 mutants [4 , 15] could also contribute to defects in growth or motor behavior . Therefore , we sought to create a tissue-specific mouse knockout model to examine the hypothalamic function of Lef1 , and to determine whether it is evolutionarily conserved . Lef1 is expressed in the mouse Hc from embryonic day ( E ) 10 . 5 to adulthood [16 , 17] , and while previously characterized Lef1 null mutants exhibit postnatal lethality and a smaller body size , no hypothalamic phenotypes were reported [18 , 19] . We created a mouse hypothalamus knockout model using Nkx2-1Cre and Lef1flox alleles [20 , 21] . We also introduced the Cre reporter RosatdTomato [22] to create the conditional knockout allele Nkx2-1Cre/+;Lef1flox/flox;RosatdTomato/+ ( herein referred to as Lef1CKO ) and control littermates Nkx2-1Cre/+;Lef1flox/+;RosatdTomato/+ ( herein referred to as Lef1CON ) , which were used for all experiments . We confirmed successful recombination by tdTomato expression ( S5A Fig ) , and loss of hypothalamic Lef1 and Wnt reporter [23] expression in Lef1CKO mice ( S5B and S5C Fig ) , which were viable , fertile , and morphologically indistinguishable from Lef1CON littermates . However , both male and female Lef1CKO mice gained weight more slowly after weaning ( Fig 4A ) , similar to the phenotype we observed in zebrafish lef1 mutants ( Fig 3A ) , and again consistent with elevated anxiety [12] . To directly measure anxiety-related behavior , we used an elevated plus maze ( EPM ) test and found that male Lef1CKO mice spent significantly less time in the open arms and more time in the closed arms ( Fig 4B ) despite normal mobility ( S4A Fig ) . In an open field test ( OFT ) , male Lef1CKO mice spent significantly less time in the center zone ( Fig 4C ) despite normal mobility ( S4B Fig ) . These results are consistent with elevated anxiety in male Lef1CKO mice . We also observed enhanced anxiety specifically in OFT with estrous female Lef1CKO mice , but not with diestrous or all females , or with EPM testing of any females ( Fig 4B and 4C and S4A and S4B Fig ) , likely due to reported variations in anxiety-related behavior between different sexes [24] and different behavioral assays [25] . Together , these results suggest a conserved role of hypothalamic Lef1 in inhibiting anxiety . Consistent with the neurogenesis defect we observed in zebrafish , we found fewer HuC/D+ cells in the mouse hypothalamic ventricular zone in Lef1CKO embryos at E14 . 5 ( Fig 5A ) . Importantly , this effect was restricted to coronal sections in which endogenous Lef1 is expressed ( S5B Fig ) . To identify Lef1-dependent genes in the mouse hypothalamus , we performed RNA-seq analysis of hypothalami dissected from E14 . 5 Lef1CON and Lef1CKO embryos , and surprisingly identified only 1 protein-coding gene that mapped to a unique locus with an AdjP <0 . 1 and a fold change >2 , Pmch ( Fig 5B and S5 Table ) . Pmch expression normally overlaps with Lef1 in the premammillary hypothalamus , and extends into the lateral hypothalamus ( Fig 5C ) [17 , 26] . We confirmed loss of Pmch expression in E14 . 5 Lef1CKO embryos by quantitative real-time PCR ( qPCR ) and immunostaining ( Fig 5D and S5D and S5E Fig ) . The only other significantly affected protein-coding gene identified by RNA-seq , Ribosomal Protein L34 ( Rpl34 ) ( Fig 5B , S5 and S6 Tables ) , is a repetitive processed pseudogene that could not be conclusively mapped to a single genomic locus , although one copy is located adjacent to Lef1 . Reduced Pmch expression in Lef1CKO embryos was unexpected because its orthologs were not significantly affected in RNA-seq analysis of zebrafish lef1 mutants ( S2 Table ) . To determine if any Lef1-dependent genes were conserved with zebrafish later in development , we performed another RNA-seq analysis at postnatal day ( P ) 22 , when Lef1CKO mice begin to exhibit a growth defect ( Fig 4A ) . In this experiment , we identified only 2 affected protein-coding genes mapped to unique loci with an AdjP <0 . 1: Pmch and Tachykinin receptor 3 ( Tacr3 ) ( Fig 5B , S6 Table ) . Tacr3 is known to be co-expressed in Pmch+ neurons , along with CART prepropeptide ( Cartpt ) [27] . We confirmed their reduced expression in the lateral hypothalamus of P22 Lef1CKO mice by qPCR and in situ hybridization ( Fig 5D and 5E and S5E Fig ) , consistent with loss of Pmch+ neurons . Decreased body weight observed after ablating Pmch+ neurons [28 , 29] may therefore be related to an anxiolytic role for these cells [12] , which is further supported by characterization of their inputs and activity [30] . Orthologs of multiple Lef1-dependent anxiety-related genes in zebrafish are expressed near Lef1 in the mouse hypothalamus , such as Pde9a and Nitric oxide synthase 1 ( Nos1 ) at E14 . 5 [26] , and Crhbp and Histidine decarboxylase ( Hdc ) in adults [16] . However , RNA-seq analysis indicated that expression of these genes was Lef1-independent in mice ( S5 and S6 Tables ) , and we confirmed this result for Crhbp by qPCR and in situ hybridization ( Fig 5D and S5E and S5F Fig ) . In addition , we confirmed that expression of zebrafish pmch orthologs [31] does not depend on Lef1 at either 3 dpf or 15 dpf ( S6A–S6C Fig ) . While we cannot rule out the possibility that our RNA-seq analysis of the mouse hypothalamus lacked the sensitivity to identify other conserved Lef1-dependent genes , it is clear that the identity of Lef1-dependent neurons relevant for anxiety differs between zebrafish and mice . Interestingly , many Lef1-dependent genes in zebrafish encoding components of anxiety-mediating transmitter pathways , such as GABA , 5-HT , and CRH ( Fig 2B ) , have a conserved function in Drosophila anxiety-like behavior [1] . Therefore , we hypothesized that hypothalamic Lef1-dependent neurons in zebrafish may represent an evolutionarily ancient pathway . The Drosophila pars intercerebralis ( PI ) and pars lateralis ( PL ) represent neuroendocrine organs equivalent to the vertebrate rostral hypothalamus and Hc , respectively [32] . In Drosophila , a single Lef/Tcf family member , pangolin ( pan ) , functions as a Wnt activator [33 , 34] . Consistent with our hypothesis , we detected specific pan expression at stage 14 and the crhbp ortholog CG15537 expression at stage 16 in the Drosophila PL primordium [32] ( Fig 6A–6C ) . Furthermore , we observed a loss of crhbp expression in the PL of pan mutants [34] at stage 16 , despite intact expression in the PI and normal PL morphology ( Fig 6C–6E ) . Drosophila crhbp in the PL may also be anxiolytic by inhibiting CRH/CRH-like diuretic hormone in the PI [1 , 32 , 35] , thus these results support a relationship between neuroendocrine Lef1 function and the development of anxiolytic Crhbp+ neurons dating back to a common bilaterian ancestor . By contrast , Pmch is a vertebrate specific gene , and Lef1-dependent Pmch+ neuronal circuitry in mice may reflect a more recent mammalian divergence that co-evolved with new brain structures [36] . Our animal models suggest that in humans Lef1 may also regulate the formation of Pmch+ and/or Crhbp+ hypothalamic neurons . To test this hypothesis , we compared the hypothalamic RNA-seq transcriptomes of 96 human individuals from the GTEx project [37] ( S7 Table ) . Despite the fact that these data did not include prenatal samples , we found that expression of PMCH and CRHBP are both moderately correlated with LEF1 , which is expressed at a relatively low level in the adult human hypothalamus ( Fig 7A and 7B ) . Notably , PMCH and CRHBP were both within the top 100 LEF1-correlated genes , along with known Wnt targets such as Sal-like protein 4 ( SALL4 ) [38] and SP5 [11] ( Fig 7C and S8 Table ) . In the course of this analysis , we noticed similar correlation profiles for CRHBP and PMCH ( Fig 7A and 7B ) , suggesting a possible expression correlation between these 2 genes . Surprisingly , we found CRHBP and PMCH to be the most highly correlated genes with each other ( Fig 7D–7F and S8 Table ) , a relationship that has never been reported previously . Among the top 200 PMCH- or CRHBP-correlated genes , we also found 2 Wnt ligands and 1 Wnt co-activator: R-Spondin 1 ( RSPO1 ) [40] ( Fig 7D and 7E ) . As a comparison , AGRP is the most highly correlated gene with Neuropeptide Y ( NPY ) ( Fig 7G and S8 Table ) , consistent with their co-expression in the same hypothalamic neurons [41] . Interestingly , while Pmch and Crhbp are expressed in different regions of the mouse hypothalamus [16] , they are expressed in the same hypothalamic nuclei in another primate , the marmoset according to the Marmoset Gene Atlas ( https://gene-atlas . bminds . brain . riken . jp ) . Importantly , the results of all our correlation analyses are recapitulated on GeneNetwork ( www . genenetwork . org ) [42] , which imported an older version of GTEx’s datasets and calculated Pearson correlation across a population ( See Materials and methods ) . Together these data suggest co-expression of PMCH and CRHBP in the primate hypothalamus and potential regulation by LEF1-mediated Wnt signaling in humans .
In this study , we demonstrate that Lef1-mediated hypothalamic Wnt signaling plays an evolutionarily conserved role in regulating the formation of anxiolytic neurons ( See Fig 8 for summary ) . In zebrafish lef1 mutants , neural progenitors fail to differentiate and undergo apoptosis , resulting in a smaller Hc ( alternatively named the hypothalamic posterior recess , the posterior part of the paraventricular organ , or the caudal zone of the periventricular hypothalamus [4 , 43 , 44] ) . Any or all of the 20 anxiety-related genes that are misregulated in the zebrafish mutant ( Fig 2B ) may contribute to the behavioral phenotypes that we observe . Likewise , our data do not conclusively prove that crhbp+ neurons , or indeed any individual Lef1-dependent neuronal populations , mediate the effect of Lef1 on anxiety . Such a conclusion would require either rescue of the lef1 mutant phenotype by restoration of missing neurons , or phenocopy by specific ablation of the cells . However , the specific loss of Pmch+ neurons in our mouse conditional knockout ( Fig 5B ) , combined with the unexpected expression correlation between PMCH and CRHBP in the human hypothalamus ( Fig 7F ) , is consistent with a common role for these 2 genes in behavior . While we also cannot rule out the possibility that Lef1 mutants may have other behavioral defects , genes that are known to regulate other hypothalamus-driven behaviors , such as Npy , Agrp , Pomc , and Hcrt , are unaffected in our mutants ( S2 , S5 and S6 Tables ) . In addition , pure assessment of other behaviors cannot distinguish a direct phenotype from an anxiety-related secondary phenotype . While the major product of Pmch , melanin-concentrating hormone ( MCH ) , is an anxiolytic factor in teleosts [45] , studies in mammals have reported it to be either anxiolytic , anxiogenic or having no effect [46 , 47] . In addition , the Pmch propeptide makes at least 2 more neuropeptides , neuropeptide-glutamic acid-isoleucine ( NEI ) , and neuropeptide-glycine-glutamic acid ( NGE ) , which are also involved in stress response and anxiety [48] . Germline Pmch mouse knockouts gain weight more slowly than controls , a phenotype originally attributed to decreased food intake [49] . However , on a different background strain , the same group reported that the knockout mice were not hypophagic , while retaining a growth phenotype [50] . Interestingly , all rodent models ablating Pmch [49–53] or Pmch+ neurons [28 , 29] exhibit a reduced growth rate . One possible underlying mechanism could be enhanced anxiety [12] , which was not directly tested in any of these studies . Therefore , we hypothesize that in Lef1CKO mice , loss of hypothalamic Pmch+ neurons is responsible for elevated anxiety , leading to a secondary growth phenotype . Our data suggest that the gene expression and neuronal subtypes dependent on Lef1 can change during evolution while maintaining a common behavioral output . While transcriptional networks can undergo rapid rewiring at the level of enhancer binding sites during yeast , insect and mammalian evolution [54 , 55] , the direct transcriptional targets of Lef1 mediating hypothalamic neurogenesis are still unknown . We have identified Tcf/Lef consensus binding sites in zebrafish and mouse Crhbp and Pmch loci , but it remains important to determine whether these 2 genes are direct targets of Lef1 , or are instead lost as a secondary result of neurogenesis defects in mutants . In either case , it will also be useful to understand the circuitry of Lef1-dependent neurons . While the targets of Crhbp+ neurons in Drosophila and zebrafish are unknown , the projections of Pmch+ neurons in the hypothalamus of mice and other mammals are well characterized , and the regulation of these circuits by Lef1 in these species may be linked to anatomical and functional expansion of target brain regions such as the cortex [36] . Importantly , the coordinated expression of CRHBP and PMCH in the human hypothalamus suggests that they may be co-expressed in a single neuronal cell type . Loss of other genes important for hypothalamic neurogenesis has been shown to affect behavior [2] . Interestingly , mice lacking hypothalamic Dbx1 also exhibit a loss of Pmch+ neurons along with other populations [56] . In that study , Lef1-expressing hypothalamic nuclei were hypothesized to regulate innate behaviors outside the hypothalamic-pituitary-adrenal ( HPA ) axis , partly due to the observation of expanded Wnt activity in Dbx1 knockout animals . However , because our work demonstrates that Lef1 is in fact required for the genesis of Pmch+ neurons and for HPA-related behaviors , an alternative explanation is that Dbx1 functions in a parallel pathway to Lef1 . Together these results identify Wnt signaling as a link between brain development and function that allows essential behaviors to be maintained even as anatomical structures change through evolution . In addition , given the function for hypothalamic Wnt signaling in regulating postembryonic zebrafish neurogenesis [4] , and the continuous expression of Lef1 in the hypothalamus of fish ( S2D Fig ) and mammals [16] throughout life , it would be interesting to test a possible contribution to adult behavior using temporal conditional knockout models . While Wnt signaling in the mammalian hippocampus and nucleus accumbens has been associated previously with anxiety and depression [57 , 58] , our data demonstrate a novel requirement for pathway activity in a brain region that is highly conserved throughout the vertebrate lineage , and may prove useful for the diagnosis and treatment of hypothalamus-related anxiety disorders .
All experimental protocols were approved by the University of Utah Institutional Animal Care and Use Committee and were in accordance with the guidelines from the National Institutes of Health . Approval number: 16–09011 . Zebrafish were euthanized by ice water immersion . Mice were euthanized by CO2 or ketamine/xylazine . Zebrafish ( Danio rerio ) were bred and maintained in a 14:10 hour light/dark cycle as previously described [59] . Zebrafish per tank were fed with similar amount of food and treated by the staff who were blinded to the experiments . Wt strains were *AB . The following mutant and transgenic strains were used: lef1zd11 [4] , Tg ( top:GFP ) w25 [7] , Tg ( dlx6a-1 . 4dlx5a-dlx6a:GFP ) ot1 [60] , Tg ( h2afv:GFP ) kca6 [61] , Tg ( th2:GFP-Aequorin ) zd201 [8] , p53e7 [62] . lef1-/- homozygous mutants were identified between 3 dpf and 10 dpf by DASPEI staining as described previously [15] and at or after 15 dpf by loss of caudal fin [4]; wt and heterozygous siblings were used as controls . All the zebrafish were from at least 1 single-pair breeding . Genotyping was done as described before for lef1zd11 [4] and p53e7 [63] , except primers used for lef1zd11 ( forward primer: 5ʹ-CACTCTCTCCAGCCCAACATT-3ʹ , reverse primer: 5ʹ-TGTTACTGTTGGGACTGATTTCTG-3ʹ ) . Male and female C57BL/6J mice ( Mus musculus ) were group-housed with 2–5 mice per cage in a reverse 12 hour light/dark cycle with ad libitum access to food and water . Mice were 19–20 and 15–20 weeks old at the time of behavioral tests for male and female animals , respectively . Ai9 reporter RosatdTomato ( line 007905 ) [22] , Nkx2-1Cre ( line 008661 ) [21] , and TCF/Lef:H2B-GFP mice ( line 013752 ) [23] were purchased from Jackson Laboratories . Lef1flox/flox mice were provided by HHX [20] . All strains were maintained on a C57BL/6J background except TCF/Lef:H2B-GFP mice , which were originally on a C57BL/6 × 129 background . Male Nkx2-1Cre/Cre;Lef1flox/+ and female Lef1flox/flox;RosatdTomato/tdTomato mice were used to generate conditional knockout ( Lef1CKO: Nkx2-1Cre/+;Lef1flox/flox;RosatdTomato/+ ) and control ( Lef1CON: Nkx2-1Cre/+;Lef1flox/+; RosatdTomato/+ ) offspring . Females breeders were maintained by inbreeding . Male breeders were maintained by interbreeding Nkx2-1Cre/Cre;Lef1+/+ and Nkx2-1Cre/Cre;Lef1flox/+ for no more than 5 generations to avoid potential artifacts caused by Cre homozygous inbreeding [64] . In occasional litters , Ai9 reporter expression was observed throughout the body of approximately 10% of experimental animals , consistent with published literature [21]; such animals were not used for experiments . All the mice were from at least 3 litters unless otherwise noted . Sex at E14 . 5 was determined by genotyping by Jarid 1c [65] . When generating experimental mice for body weight measurement and behavioral tests , each litter was culled to 8 pups at P0 . Genotyping for RosatdTomato and TCF/Lef:H2B-GFP animals was done according to available Jackson Laboratory protocols for these strains . Genotyping for Nkx2-1Cre mice was done using primers for Cre recombinase detection ( forward primer: 5ʹ-ATGCTTCTGTCCGTTTGCCG-3ʹ , reverse primer: 5ʹ-CCTGTTTTGCACGTTCACCG-3ʹ ) . Genotyping for Lef1flox mice was done using primers contributed by HHX ( forward primer: 5ʹ-GCAGATATAGACACTAGCACC-3ʹ , reverse primer: 5ʹ-TCCACACAACTAACGGCTAC-3ʹ ) . Canton-S wild-type and pan2 mutant ( BL4759 ) Drosophila melanogaster strains were obtained from Bloomington Stock Center . At the sphere stage , 10–50 blastula cells from donor embryos were transplanted using a glass micropipette into the dorsal side of shield stage host embryos , 20–40 degrees from the animal pole , representing the hypothalamus anlage [66] . Embryos were then raised to 5 dpf for immunohistochemistry . Donor and host embryos were retained for genotyping to identify lef1 mutants . Four dpf zebrafish embryos were incubated in E3 media containing 10 mM BrdU ( Sigma-Aldrich , St . Louis , MO ) at 28 . 5°C for indicated time before being washed in E3 media for at least 3 times . Embryos and larvae were fixed in 4% paraformaldehyde ( PFA ) for 3 hours at room temperature ( RT ) or overnight ( O/N ) at 4°C followed by brain dissection . Brains were either dehydrated in methanol and stored at −20°C , or immediately processed for immunohistochemistry . For 3 dpf embryos , 5% sucrose was included in the fixative to ease dissection . Brains were treated with 0 . 5 U dispase ( Gibco #17105–041 ) in 2% PBST ( PBS/2% Triton X-100 ) for 60 minutes at RT . For BrdU , PCNA , pH3 or Caspase-3 staining , brains were washed in water for 5 minutes twice , followed by incubation in 2 N HCl for 60 minutes at RT , followed by 2 more water washes . Brains were then blocked in 5% to 10% goat serum in 0 . 5% PBST for 60 minutes at RT . Embryos were incubated in primary antibodies in block O/N at 4°C and secondary antibodies and Hoechst 33342 ( Life Technologies , H3570 ) in block O/N at 4°C before mounting in Fluoromount-G ( SouthernBiotech , Birmingham , AL ) with the ventral hypothalamus facing the coverslip . Primary antibodies were all used at 1:500 dilution except as noted: chicken anti-GFP ( Aves Labs , GFP-1020 ) , rabbit anti-GFP ( Molecular Probes , A11122 ) , mouse anti-HuC/D ( Molecular Probes , A21271 ) , rabbit anti-5-HT ( ImmunoStar , 541016 ) , rabbit anti-pH3 ( 1:400 , Cell Signaling , 9713 ) , rabbit anti-active Caspase-3 ( BD Pharmingen , 559565 ) , rabbit anti-BLBP ( Abcam , ab32432 ) , mouse anti-PCNA ( Sigma , P8825 ) , and chicken anti-BrdU ( ICL , CBDU-65A-Z ) . Secondary antibodies were all used at 1:500 dilution: goat anti-mouse Alexa Fluor 448 ( Invitrogen , A11001 ) , goat anti-rabbit Alexa Fluor 488 ( Invitrogen , A11008 ) , donkey anti-chicken Alexa Fluor 488 ( Jackson ImmunoResearch , 703-545-155 ) , goat anti-rabbit cy3 ( Jackson ImmunoResearch , 111-165-003 ) , goat anti-mouse cy3 ( Jackson ImmunoResearch , 115-165-003 ) , goat anti-mouse Alexa Fluor 647 ( Invitrogen , A21235 ) , goat anti-rabbit Alexa Fluor 647 ( Invitrogen , A21244 ) , and goat anti-chicken Alexa Fluor 647 ( Invitrogen , A21449 ) . Hoechst 33342 ( 1:10 , 000 ) was used to stain nuclei . All the primary antibodies were validated previously [4 , 67] . E14 . 5 embryo heads were dissected in PBS and fixed in 4% PFA at RT for 1 . 5 hours or O/N at 4°C . Brains were dissected and cryoprotected in 15% and then 30% sucrose , embedded in OCT , and stored at −80°C . Brains were cryosectioned at a thickness of 16 μm , air dried and stored at −80°C . Air-dried sections were then washed in PTW ( PBS+0 . 1% Tween 20 ) 3 times , followed by permeabilization in 0 . 25% PBST for 5 minutes and blocking in 10% goat serum in PTW for 60 minutes . Sections were incubated in primary antibodies in blocking solution O/N at 4°C and secondary antibodies in blocking solution for 2 hours at RT , followed by Hoechst 33342 stain for 10 minutes at RT before mounting in Fluoromount-G . Antibodies used were as described above except rabbit anti-LEF1 ( 1:200 , Cell Signaling , 2230 ) , goat anti-PMCH ( 1:500 , Santa Cruz , sc14509 ) and donkey anti-goat Alexa Fluor 647 ( 1:400 , Invitrogen , A21447 ) . All primary antibodies were validated by absence of staining in Lef1CKO animals . For HuC/D staining , incubation for 30 minutes in 0 . 5 U dispase was performed in 0 . 25% PBST . Drosophila immunohistochemistry was performed as previously described [68] except that a fluorescent secondary antibody was used . Antibodies used were as described above except mouse anti-FasII ( 1:5 , DSHB , 1D4 ) , which was validated previously [32] . In situ hybridization probes were made by a clone-free method as described previously [69 , 70] , with DNA templates purified using Zymo Research DNA Clean & Concentrator-5 kit . Primers were designed by Primer-BLAST [71] except for mouse genes with primer sequences available from the Allen Brain Atlas ( ABA ) [16] or GenePaint Atlas [26] . A full list of primers used to make probes is in S9 Table . cDNA made from 3 dpf zebrafish embryos , P2 , and P60 mouse hypothalamus , and adult Drosophila ( gift from C . Thummel ) was used as the initial template for PCR to generate T7 promoter-containing DNA . RNA probes for zebrafish lef1 [72] and axin2 [73] were previously described . The RNA probe for Drosophila pan was generated from the Drosophila Gene Collection T7 promoter-containing cDNA GM04312 [74] . Zebrafish whole mount in situ hybridization was performed as described previously [75] except that 15 dpf and adult zebrafish were fixed in 4% PFA O/N at 4°C followed by washing in PBS and brain dissection . All tissues were treated for 30 minutes with 10 μg/ml Proteinase K . Pigmented embryos were bleached in 1% H2O2/5% formamide/0 . 5× SSC O/N at RT after in situ hybridization . 3 dpf embryos and postembryonic brains were imaged in 100% glycerol and PBS , respectively . For automated whole mount in situ hybridization , all steps following probe hybridization and before color reaction were performed using a BioLane HTI ( Intavis , Chicago , IL ) . Twenty-five μm brain cryosections were collected and post-fixed as previously described [76] ( http://developingmouse . brain-map . org/docs/Overview . pdf ) . In situ hybridization was then performed as described [77] . Drosophila whole mount in situ hybridization was performed as described previously [68] . Zebrafish from a single home tank were anesthetized using tricaine ( Sigma-Aldrich , St . Louis , MO ) in shallow water . Images were acquired of immobilized , non-overlapping fish with a ruler for scale . Body length was calculated by measuring the distance between the mouth and the anterior edge of the tail fin , using ImageJ . Five fish from lef1+/- incrosses were raised per tank starting at 5 dpf . lef1 mutants and controls were separated at 15 dpf . Novel tank diving tests [13] were performed on 16 dpf larvae during the early afternoon of the same days , before lef1 mutants start to display surfacing behavior at 20 dpf . Novel rectangular tanks ( 16 . 6 cm × 9 . 5 cm × 12 . 3 cm ) were illuminated by a centered white light , and videos were acquired with a mounted Nokia Lumia 640 phone 1080p camera . For each experiment , single mutant and control larvae were netted and then removed simultaneously from their home cages and transferred to novel tanks with identical water volume . The order of netting mutant and control fish was rotated between trials . Videos were viewed in MPlayerX to manually analyze the latency of larvae to enter the upper half of the tank after initial sinking . Videos were then imported and analyzed using Ethovision XT version 11 . 5 ( Noldus , Leesburg , VA ) during the initial exploration phase , with a tracking period of 2 minutes beginning 1 minute after release into the novel tank to decrease water agitation resulting from netting . Videos were also analyzed after the initial exploration phase with a tracking period during the 4 to 6 minute interval . Tracks were analyzed for distance travelled , time in upper half of the tank and time of immobility . All pups were weaned at P21 immediately following the first weighing . Pups weighing less than 6 . 5 g were excluded from analysis . All mice were weighed during the morning of the same days of the following weeks . Group-housed mice were allowed to acclimate to the animal facility for behavioral tests 9 days after an on-campus transfer . Each mouse was handled daily for 2 minutes , during midmorning for 7 days before commencement of behavioral testing using the cupped hand method [78] . To avoid behavioral variation caused by female estrous cycle [79] , a vaginal lavage procedure was done after daily handling for estrous phase evaluation for 7 days , as previously reported [80] . Female mice in their proestrus or estrus phases were collectively grouped as “Estrus” and females in their metestrus and diestrus phases were collectively grouped as “Diestrus . ” All mice were acclimated to the behavior room for 1 hour under red light ( 69 lux ) before commencement of tests . Open field and EPM behavioral tests were performed in order , once daily for 2 days , from 9 am to 5 pm . The experimenter was blinded to genotype . Each mouse was placed in a circular plexiglass chamber ( 4 . 5” diameter × 3” height ) located inside an illuminated ( 330 lux ) circular open field arena ( 110 cm diameter ) and allowed to acclimate for 1 minute to decrease movement bias resulting from experimenter handling . After 1 minute , the plexiglass chamber was removed from the arena , and the mouse was allowed to freely explore the arena for 10 minutes . Movement was video recorded and analyzed using Ethovision version 9 ( Noldus , Leesburg , VA ) . The EPM apparatus was elevated 60 cm from the floor , having 2 open arms ( 35 cm × 5 cm ) and 2 closed arms ( 35 cm × 16 cm ) connected by a central platform ( 5 cm × 5 cm ) . The EPM was illuminated by a white light ( 205 lux ) at the center platform . Each mouse was placed in a rectangular opaque white plexiglass chamber ( 2” × 3” × 5” ) located on the center platform , and allowed to acclimate for 1 minute before commencement of the test . The white chamber was removed and the mouse was allowed to freely explore the EPM for 5 minutes . Behavior was video recorded and analyzed using Ethovision version 9 ( Noldus , Leesburg , VA ) . Embryos were fixed for 1 . 5 hours in 4% PFA/5% sucrose in PBS at RT , followed by whole hypothalamus dissection with super-fine forceps ( FST , 11252–00 ) . For each biological replicate , 28 to 38 dissected hypothalami were pooled for lef1 mutant and control samples from at least 1 single-pair breeding . RNA was extracted using a RecoverAll Total Nucleic Acid Isolation Kit for FFPE ( Ambion , AM1975 ) according to the manufacturer’s instructions . Three biological replicates were obtained on different days from offspring of different breedings . A total of 300 ng RNA per sample was submitted to the High Throughput Genomic Core at the University of Utah for RNA quality control by High Sensitivity R6K ScreenTape , RNA concentration by vacuum drying , cDNA library prep by Illumina TruSeq Stranded RNA Kit with Ribo-Zero Gold and sequencing by HiSeq 50 Cycle Single-Read Sequencing version 3 . RNA-seq was analyzed by the Bioinformatics Core at the University of Utah . A transcriptome reference was created by combining GRCz10 chromosome sequences with Ensembl build 84 splice junction sequences generated with USeq ( version 8 . 8 . 8 ) MakeTranscriptome . RNA-seq reads were mapped to the GRCz10 zebrafish transcriptome reference using Novoalign ( version 2 . 08 . 03 ) . Splice junction alignments were converted back to genomic space using USeq SamTranscriptomeParser . USeq DefinedRegionDifferentialSeq was used to generate per gene read counts , which were used in DESeq2 to determine differential expression . RNA-seq graph in Fig 2A was made by IPython Notebook with package NetworkX . E14 . 5 and P22 nonweaned male Lef1CON and Lef1CKO hypothalami were dissected using a fluorescent microscope in ice-cold PBS , while tail tissue was retained for genotyping . E14 . 5 tissues were immediately immersed in RNAlater ( Thermo Fisher , Waltham , MA ) and stored at 4°C for up to 7 days until RNA extraction . P22 tissues dissected from at least 2 litters were immediately homogenized in TRIzol ( Thermo Fisher , Waltham , MA ) and stored at −80°C . Three biological replicates were prepared from either 5 pooled hypothalami ( E14 . 5 ) or a single hypothalamus ( P22 ) from Lef1CON and Lef1CKO mice , and RNA was extracted on the same day using TRIzol followed by purification with an RNeasy Mini Kit ( Qiagen , Hilden , Germany ) and on-column DNase digestion ( Sigma-Aldrich , St . Louis , MO ) . One μg of RNA per sample was submitted to the High Throughput Genomic Core at the University of Utah for RNA quality control with Agilent RNA ScreenTape , cDNA library prep with Illumina TruSeq Stranded RNA Kit with Ribo-Zero Gold , and sequencing using HiSeq 50 Cycle Single-Read Sequencing version 4 . RNA-seq reads were mapped to GRCm38 . Differential gene expression analysis and graph plotting were carried out using the same methods as for zebrafish RNA-seq . Three biological replicates of RNA from male and female mice were prepared as described above for RNA-seq . Two and a half μg RNA was used for cDNA synthesis with a SuperScript III Reverse Transcriptase kit ( Invitrogen , Carlsbad , CA ) . qPCR was performed in triplicate using Platinum SYBR Green master mix ( Invitrogen , Carlsbad , CA ) on 96-well CFX Connect ( Bio-Rad , Hercules , CA ) plates or 384-well QuantStudio 12K Flex ( Life Technologies , Durham , NC ) plates at the Genomics Core at the University of Utah , according to manufacturer’s instructions . Gapdh was used to normalize quantification , and reverse transcriptase was omitted for controls . qPCR analysis was performed with the ΔΔCt method to determine relative expression change [81] . Dissociation curve analysis was performed to confirm the specificity of amplicons . qPCR primers were designed from PrimerBank [82] as follows ( forward primer first , reverse primer second , in 5ʹ to 3ʹ orientation with PrimerBank ID in the parentheses ) , Pmch ( 12861395a1 ) : GTCTGGCTGTAAAACCTTACCTC , CCTGAGCATGTCAAAATCTCTCC; Tacr3 ( 10946720a1 ) : CTGGGCTTGCCAGTGACAT , CGCTTGTGGGCCAAGATGAT; Crhbp ( 162287189c2 ) : CTTACCCTCGGACACTTGCAT , GGTCTGCTAAGGGCATCATCT . Fluorescent images of dissected zebrafish and mouse brains were obtained with an Olympus FV1000 confocal microscope at the Cell Imaging Core at the University of Utah . Z-stack images were all maximum intensity z-projections of 3 μm slices; single- or double-labeled cells were manually counted in FV1000 ASW 4 . 2 Viewer . All the zebrafish and mouse in situ hybridization images were obtained with an Olympus SZX16 dissecting microscope except those in Fig 5E , S2C Fig and S6B Fig , which were obtained with an Olympus BX51WI compound microscope . Two months post-fertilization ( mpf ) zebrafish images ( S3A and S3B Fig ) were acquired using a Leica MZ16 microscope . Drosophila in situ hybridization images were obtained with a Zeiss Axioskop . IPA ( QIAGEN , Redwood City , CA ) was performed with 129 mouse orthologs of the 138 zebrafish protein-coding genes identified from RNA-seq with AdjP <0 . 1 ( S4 Table ) . Analysis was performed by the Bioinformatics Core at the University of Utah according to QIAGEN's instructions and “diseases and functions” were extracted from the software ( S3 Table ) . Publically available GTEx raw datasets were downloaded from www . gtexportal . org in April 2017 as a single file: GTEx_Analysis_v6p_RNA-seq_RNA-SeQCv1 . 1 . 8_gene_rpkm . gct . gz . Ninety-six hypothalamic samples were identified according to their specific strong PMCH expression , and extracted into S7 Table by IPython Notebook with packages gzip and xlwt . Pearson correlation was calculated by gene reads per kilobase of transcript per million mapped reads ( RPKM ) using IPython Notebook with function scipy . stats . stats . pearsonr , followed by result writing into S8 Table by IPython Notebook with package xlwt . The same Pearson correlation r values were confirmed using Excel’s CORREL function . A similar correlation result was obtained when searching for the top 200 correlated genes by Pearson on GeneNetwork ( www . genenetwork . org ) in April 2017 . Several differences are noted between our analyses and GeneNetwork’s analyses . First , GeneNetwork imported an older version of GTEx’s datasets ( GTEXv5 Human Brain Hypothalamus RefSeq [Sep15] RPKM log2 ) . Second , GeneNetwork calculated Pearson correlation using RPKM log2 rather than RPKM in our case . Third , GeneNetwork calculated Pearson’s sample correlation across a population , with an adjustment across the genome , and also based on the number of the top correlated genes requested by the users; in our case , we calculated Pearson correlation between 2 genes , and simply ranked all the genes by their Pearson’s r values calculated for the gene of interest . Lastly , GeneNetwork’s imported older GTEx datasets had 102 hypothalamic samples , 6 among which were left out in current GTEx’s server . The complete overlapping of the 96 samples further confirmed our successful extraction of hypothalamic datasets from the GTEx project . No statistical methods were used to predetermine sample size . For behavioral assays , sample size was determined based on accepted practice . The experiments were not randomized . Due to visible phenotypes , the investigators were not blinded to outcome assessment except for whole mount in situ hybridization of zebrafish lef1+/- incrosses , Drosophila pan+/- incrosses , and mouse body weight and behavioral assays . Two-tailed unpaired Student t tests were performed for all statistical analysis , except mouse body weight ( 2-way ANOVA with repeated measures ) , using GraphPad Prism software version 6 . Outliers were identified by Grubbs’ test for behavioral assays with significance assigned at P < 0 . 05 ( alpha = 0 . 01 ) . All the criteria for excluding data points were established prior to data collection . | Humans , mice , fish , and even flies exhibit anxiety-like behavior despite the fact that their brain anatomy varies widely . This study reveals another common thread that runs through these diverse animals: the molecular origins of their shared behavior . Gene knockout experiments in mouse and zebrafish show that the molecular signal Wnt acts through the transcription factor Lef1 to inhibit anxiety in both species . The pathway is required for formation of anxiolytic neurons in a highly conserved brain region , the hypothalamus . From there , however , the process diverges . In the fish , the pathway triggers genes including corticotropin-releasing hormone binding protein ( crhbp ) , but in mice the same pathway calls into action a different gene , Pro-melanin concentrating hormone ( Pmch ) . By comparison , the fruit fly Drosophila activates crhbp , similar to zebrafish . Furthermore , CRHBP and PMCH show extraordinarily coordinated expression in the primate hypothalamus , indicating that they may act together downstream of Wnt and Lef1 to regulate human behavior . This work reveals the surprising finding that conserved signaling pathways can regulate common behavioral outputs through diverse brain circuits during evolution . | [
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] | 2017 | Lef1-dependent hypothalamic neurogenesis inhibits anxiety |
Many proteins contribute to the contractile properties of muscles , most notably myosin thick filaments , which are anchored at the M-line , and actin thin filaments , which are anchored at the Z-discs that border each sarcomere . In humans , mutations in the actin-binding protein Filamin-C result in myopathies , but the underlying molecular function is not well understood . Here we show using Drosophila indirect flight muscle that the filamin ortholog Cheerio in conjunction with the giant elastic protein titin plays a crucial role in keeping thin filaments stably anchored at the Z-disc . We identify the filamin domains required for interaction with the titin ortholog Sallimus , and we demonstrate a genetic interaction of filamin with titin and actin . Filamin mutants disrupting the actin- or the titin-binding domain display distinct phenotypes , with Z-discs breaking up in parallel or perpendicularly to the myofibril , respectively . Thus , Z-discs require filamin to withstand the strong contractile forces acting on them .
Arguably , the most complex actin-related cellular structure is the sarcomere , the basic contractile unit of muscle cells . The sarcomere consists of antiparallel actin thin filaments and myosin thick filaments . The thin filaments are anchored to a big protein complex termed the Z-disc at both ends of the sarcomere . In the center of the sarcomere is the M-line , another giant protein complex , that docks the thick filaments . The Z-disc is part of the I-band region , characterized by the absence of myosin . The M-line is at the center of the H-zone region , devoid of actin [1] . The sliding of thick filaments along the thin filaments pulls the Z-disc towards the M-line , representing the basis of muscle contraction [1] . The giant protein titin serves as a molecular spring and provides the passive elasticity of muscles . Titin , which can be as long as 1 μm , extends half a sarcomere and links the myosin thick filaments and the Z-disc [2] . During sarcomere assembly , titin guides thick and thin filament assembly , controls the structure and size of thick filaments and the length of the relaxed sarcomere [2] . Finally , thin filaments from adjacent sarcomeres are crosslinked by α-actinin at the Z-disc creating an array of tandemly arranged sarcomeres [1–3] . Due to the profoundly complex nature of the sarcomere and despite the huge amount of research devoted to it , many aspects of sarcomere assembly have remained elusive . Notably , many sarcomeric proteins are associated with human myopathies and despite their clinical relevance the exact function that many of these proteins play in the sarcomere is not clear . Filamin was the first actin filament crosslinking protein identified in nonmuscle cells [4] . Filamins are large homodimers that associate at their carboxy termini through a conserved hydrophobic pocket [5] . Each filamin consists of an N-terminal actin-binding domain ( ABD ) followed by 22–24 immunoglobulin-like ( Ig ) repeats , the last of which is the dimerization domain [6–8] . The Ig repeats are further subdivided into an extended rod 1 domain , and a more globular rod 2 domain , which can unfold in response to mechanical force and contains most of the binding sites for around 90 binding partners identified to date [6–12] . Vertebrates have three filamin proteins , FLNa , FLNb , and FLNc . FLNa and b are widely and similarly expressed throughout development , whereas FLNc is restricted largely to cardiac and skeletal muscles [8] . Mutations in filamins result in a wide variety of congenital anomalies , but due to its expression , only mutations in FLNc result in muscle disorders , including muscular dystrophies , myofibrillar myopathy , distal myopathy and cardiomyopathy [13] . FLNc localizes to Z-discs and FLNc-deficient mice exhibit reduced muscle mass and structural defects , like loss of distinct Z-disc components [14–16] . Despite the clinical relevance , the exact function of FLNc in muscles has remained elusive . The Drosophila gene encoding filamin was first identified because of its critical role in the assembly of ovarian ring canals , and was therefore called cheerio ( cher ) [17–19] . Drosophila filamin is highly conserved with vertebrate filamins lacking only two Ig repeats in the rod 1 domain [10] . Cheerio recruits actin filaments to the ring canal and likely tethers the ring canal to the plasma membrane [18–20] . Cher also functions as part of a perinuclear actin meshwork that connects actin cables to the nuclei , ensuring proper localization [21] . Like FLNa and FLNb , Cheerio plays important roles in enhancing tumor malignancy [22] . In the nervous system , it is required for proper peripheral motor axon guidance , in memory formation , and at the neuromuscular junction as a signaling hub [23–25] . Cheerio was also uncovered in a genome-wide screen for genes required in muscles [26] , but has not been further analyzed in muscles except as an interaction partner of small heat shock protein CryAB [27] . Here we investigate filamin function in muscles , through detailed phenotypic analysis of Cher in the indirect flight muscles ( IFM ) . We focused on the IFM because of their structural similarities to vertebrate skeletal muscles and because they have the most structurally stereotyped sarcomeres , allowing the detection of subtle defects [28] . We show that loss of filamin results in distinct sarcomere phenotypes . Mainly , we observe the detachment of actin thin filaments from the Z-disc both perpendicular and parallel to the sarcomere axis . We show that filamin actin-binding is required for keeping thin filaments anchored to the Z-disc , while filamin binding to the titin homolog Sallimus ( Sls ) is required for stabilizing the position of thin filaments perpendicular to the myofibril axis . Our data provide an explanation for the function of filamin in muscle and a framework for understanding some human FLNc myopathies [29] .
The cher gene spans a 34 kb region and produces at least 10 different transcript isoforms whose encoded proteins can be divided according to FlyBase into 4 molecular size groups: the small isoforms , here referred to as CherA and B ( 90–100 kD ) ; a medium-sized isoform , CherC ( 150 kD ) , and CherD , containing all the big isoforms ( 240–260 kD ) ( Fig 1A ) [30] . To assess the localization of Cher in the IFM we used 4 different protein trap lines that introduce a 30 kD Venus Flag-tagged artificial exon directly into the cher gene [31 , 32] . Due to their specific location inside the cher gene , cherCPTI1399 tags all protein isoforms , cherCPTI847 and cherGFSTF tag CherC and CherD isoforms; while cherCPTI1403 tags only CherD isoforms ( Fig 1A and S1D Fig ) . cherCPTI847 and cherCPTI1403 proteins are not fully functional , because homozygotes cause female infertility . cherCPTI1399 and cherGFSTF are homozygously fertile . We first evaluated by immunoblotting the presence of Cher isoforms in the adult thorax using cherCPTI1399 , because this line should tag most predicted isoforms of cher . All Cher isoform groups were detected using this assay ( Fig 1B ) . To test the specificity of the detected bands we knocked down cher expression specifically in muscles using an RNAi directed against all isoforms under the control of the Mef2 expression pattern using the Gal4-UAS system: Mef2>cher-JF RNAi . Consistently , Flag-positive bands were no longer detected indicating that all bands correspond to Cher isoforms ( Fig 1B ) . We then analyzed the localization of these protein traps in the IFM using heterozygotes . While direct Venus fluorescence was barely detectable , anti-Flag staining was detected at the Z-disc , colocalizing with the peak of the actin signal ( Fig 1C and 1D , S1A Fig ) . We also used line scans to better assess their localization; all Cher-GFP trap lines show identical localization profiles peaking at the Z-disc , indicating that all filamin isoforms colocalize at the Z-disc ( S1C Fig ) . All three Cher protein traps localize to the Z-disc , suggesting that the small CherA isoforms contain the Z-disc localization information . To further test this , we expressed the smallest CherA isoform fused to GFP in IFM . Consistently , CherA-GFP also localizes to the Z-disc ( S1B Fig ) . Thus , all Cher isoforms are components of the Z-disc and CherA , containing the last 8 Ig-like domains is sufficient for Z-disc localization . There are three available RNAi lines directed against all cher isoforms . We first assessed the efficiency of these lines by testing their ability to render flies flightless . Two of these lines , cherJF02077 and cherKK107451 , in combination with the muscle-specific driver Mef2-Gal4 led to a completely penetrant flightless phenotype . We then evaluated the IFM sarcomeric defects underlying the flying defects by first quantifying sarcomere numbers visible in confocal images . The number of recognizable sarcomeres is reduced by half in individual RNAi lines cherKK107451 and cherJF02077 , and is even further reduced in the double knock-down ( Fig 1E and S1E and S1F Fig ) , indicating strong defects in myofibril assembly or maintenance . A similar yet less severe sarcomere reduction phenotype is observed in other cher mutants ( Fig 1E ) . We proceeded to analyze the sarcomeres of cherJF02077 flies in more detail . In control flies , individual IFM sarcomeres are highly stereotyped regular structures with a perfectly defined Z-disc , revealed by the highest intensity peak of actin staining colocalizing with the Z-disc marker Kettin ( Fig 1F ) . In Mef2>cherJF02077 flies the stereotypical sarcomere pattern is often lost , resulting in shredded myofibrils . We also observed almost normal sarcomeres , but with less actin at the Z-disc , which we call a widened Z-disc phenotype ( arrowheads in Fig 1G ) . Infrequently , sarcomeres also exhibit very high actin accumulation at the Z-disc ( middle asterisk in Fig 1G ) . Similar defects were seen in Mef2>cherKK107451 flies , confirming the specificity of the phenotype ( S1E Fig ) . When both RNAi lines are combined , Mef2>cherJF02077 cherKK107451 flies display a complete loss of sarcomeric structure , where actin staining is diffuse and continuous and Kettin staining is lost ( S1F Fig ) . To better analyze the IFM phenotype upon Cher depletion , we analyzed the sarcomere phenotype at a higher resolution using transmission electron microscopy ( TEM ) . Control sarcomeres show an electron-dense structure corresponding to the Z-disc with a lighter surrounding area , together corresponding to the I-band . The highly ordered parallel actin thin filaments are readily seen , except in the center of the sarcomere , the H-zone , where actin is excluded ( Fig 2A ) . In Mef2>cherJF02077 sarcomeres three distinct phenotypes were observed: 1 ) a widened Z-disc , 2 ) a smaller or fractured Z-disc , and 3 ) actin incorporation into the H-zone ( numbers in Fig 2 ) . The CherD isoforms correspond to the big Cher isoforms , the only isoforms containing the CH actin-binding domain ( ABD ) . Given that Cher depletion results in thin filament disorganization mostly at the Z-disc , we hypothesized that CherD , through the ABD , might serve as a secondary attachment of thin filaments to the Z-disc . To test this , we used the cherΔ5 mutant bearing a 2 . 4 kb deletion uncovering the CherD transcription start site thus reducing CherD protein levels while leaving the other isoforms intact [20 , 24] ( Fig 1A and S2A Fig ) . TEM of cherΔ5 mutant sarcomeres revealed three phenotypes: 1 ) a widened Z-disc resulting from a splitting or opening of the Z-disc ( Fig 3A and 3A’ ) , 2 ) actin accumulation at the H-zone ( blue arrow in Fig 3A and 3B ) and 3 ) a smaller or fractured Z-disc ( Fig 3A and 3A’ ) . These phenotypes are similar to the spectrum of phenotypes observed in Mef2>cherJF02077 sarcomeres , but of lower penetrance ( see Fig 1E ) , suggesting that C-terminal filamin isoforms can rescue some aspects of filamin function at the Z-disc . We then used confocal microscopy to analyze other cher mutants that disrupt the CherD isoforms . First , we evaluated cherΔ5 mutant IFM stained for Kettin and actin . In contrast to a control staining ( Fig 1F ) , cherΔ5 IFM display a widened Z-disc phenotype , evident by the lack of actin staining ( Fig 3C ) . Actin accumulation at the H-zone was also occasionally observed by confocal microscopy ( S3B Fig , compare to S3A Fig ) . Both phenotypes agree with our TEM data . As the widened Z-disc phenotype was easier to distinguish by confocal microscopy we used it as a readout for analyzing other cher mutants . We first tested one of the original cher alleles to be isolated , cher1 , that has been shown to greatly reduce CherD protein levels without affecting CherA/B [18 , 25] . Confocal microscopy images of cher1 mutant IFM stained for Kettin and actin confirmed the widened Z-disc phenotype , suggesting that the large CherD isoforms account for this phenotype ( Fig 3D ) . CherD isoforms are the only isoforms containing an actin-binding domain begging the question of whether actin binding alone is responsible for the widened Z-disc phenotype . To test this hypothesis , we used the cherCPTI1403 protein trap that inserts a Venus-Flag tag into the first CH domain , leaving a Cher protein with severely compromised actin binding [22 , 31] ( S2D Fig , compare to S2B and S2C Fig ) . In cherCPTI1403 mutants widened Z-discs were evident , together with a widening of Kettin staining and loss of actin staining . CherDCPTI1403 staining at the Z-disc also appeared wider than normal ( Fig 3E ) . As for Mef2>cherJF02077 , sarcomeres with very high actin accumulations at the Z-disc were also observed occasionally ( Fig 3E ) . These data together support the notion that the ABD only found in CherD isoforms is required for keeping actin thin filaments stably attached at the Z-disc . The Actin88F gene produces the main actin protein in the IFM and its expression is also largely restricted to the IFM . Due to these two characteristics , Act88F homozygous null mutants result in viable flightless flies with complete absence of actin in the IFM [33 , 34] . Act88F null heterozygous mutants display a sensitive dominant flightless phenotype suitable for assessing genetic interactions . In Act88FKM88 heterozygotes IFM sarcomeres appear relatively normal except for the occasional splitting of myofibrils ( Fig 4A ) . In contrast , in Act88FKM88 cherΔ5 transheterozygotes sarcomeres are damaged; frayed sarcomeres and widened I-bands are regularly observed ( Fig 4B ) . To quantify the genetic interaction between Act88F and cher , we counted the number of recognizable sarcomeres compared to areas where myofibrils are too frayed to distinguish individual sarcomeres ( Fig 4C ) . Consistently , cherΔ5 , cher1 and cher1403 enhance the Act88F dominant phenotype , that is , reduce the number of recognizable sarcomeres , whereas cherQ1415sd , a C-terminal filamin mutant , does not genetically interact with Act88F . The specific genetic interaction of the cher ABD mutants with actin further confirms the role of the filamin ABD in tethering actin thin filaments to the Z-disc . The depletion of all Cher isoforms leads to severe sarcomere disintegration ( Fig 1E ) . However , cher1 and cherΔ5 mutants which completely abolish the CherD isoform , only show milder sarcomere defects , suggesting that the remaining isoforms can rescue some aspects of filamin function . To test this hypothesis , we analyzed two mutants that introduce early stop codons at molecularly defined positions: cherQ1415sd introduces a premature stop codon after Ig 15 leaving the last 7 Ig domains untranslated and cherQ1042x introduces a stop codon after Ig 11 . Due to the complexity of cher alternative splicing and internal transcription start sites , cherQ1042x only affects the CherD and C isoforms while cherQ1415sd affects all isoforms ( Fig 1A and S2A Fig ) . TEM images of cherQ1415sd revealed 2 phenotypes in common with Mef2>cherJF02077: 1 ) a smaller or fractured Z-disc , and 2 ) actin invasion of the H-zone ( Fig 5A and 5A’ ) . TEM images of cherQ1042x revealed a single phenotype: Z-disc fragments located at the H-zone ( Fig 5B and 5B’ ) . The widened Z-disc phenotype was not observed in any of these mutants , suggesting that the N-terminal region before the premature stop codon in cherQ1415sd is able to rescue the widened Z-disc phenotype . To confirm this result we quantified the number of widened Z-disc sarcomeres in different cher mutants using confocal microscopy . Indeed , widened Z-discs were observed with statistical significance only in the ABD mutants cherΔ5 and cher1403 , but not in cherQ1042x or cherQ1415sd ( Fig 5C ) . Thus , ABD and C-terminal filamin mutants have distinct phenotypes . We noticed that cher mutants were not entirely flightless , in contrast to Mef2>cherJF02077 knock-down flies . Consistent with our previous data , this observation argues that some remaining Cher function is present in our cher mutants . To quantify the flight ability of cher mutants we used an infrared tachometer to monitor the wing beat frequency of tethered flies ( Fig 5D ) . While control animals can fly at a constant speed , around 200 Hz for several minutes without interruption , cher mutants are not able to sustain flight for more than a couple of seconds ( Fig 5E ) , demonstrating the physiological relevance of all mutants . Resting elasticity of the sarcomere in striated muscle is determined by the giant modular protein titin . In vertebrates , titin reaches from the Z-disc across half the sarcomere up to the M-line . In invertebrates , the function of titin is split between Sls and Projectin . While Projectin associates with the thick filaments in the A-band , Sls spans from the Z-disc to the edge of the A-band [35–37] . Sls isoforms vary highly in size , from 2000 kD to 350 kD , the smallest being Zormin . The main isoform in the IFM is the 500 kD isoform called Kettin [36] . We previously noticed that Kettin staining is affected in some Cher-depleted sarcomeres as well as in Act88F cherΔ5 transheterozygotes ( Figs 1G , 3C–3E and 4B ) . These results suggest that Kettin may be involved in Cher function at the IFM . We first analyzed slsZCL2144 , which bears a GFP exon trap , predominantly labels Kettin in IFM and localizes to the Z-disc . It has no muscle phenotype heterozygously ( Fig 6E ) , but shows IFM defects homozygously [38] . We first recombined slsZCL2144 with Df ( 3R ) Exel6176 , a deficiency uncovering the cher locus . This transheterozygous combination led to mild sarcomere defects consisting of actin accumulation at the H-zone , evident by the lack of separation of individual sarcomeres in actin stainings ( Fig 6A and 6G ) . These actin-related phenotypes suggested that Cher together with Sls/Kettin might be involved in keeping actin anchored at the Z-disc . To further test this , we analyzed the homozygous mutant conditions of our cher mutants . Without the addition of slsZCL2144 , H-zone actin accumulation is not visible by confocal microscopy in cherQ1415sd or cherQ1042x mutants and present only at a low frequency in cherΔ5 mutants ( S3 Fig ) . Interestingly , cherQ1042x in combination with slsZCL2144 resulted in a highly significant enrichment of actin at the H-zone ( Fig 6B and 6G ) . cherΔ5 and cherQ1415sd in combination with slsZCL2144 also led to strong H-zone actin accumulation ( Fig 6C , 6D and 6G ) . In many sarcomeres , the H-zone now looks like the Z-disc , if only the actin staining is considered . We therefore confirmed the location of the Z-disc by using Zasp52 as a second Z-disc marker , showing that Sls/Kettin remains at the Z-disc ( Fig 6F ) . Our data indicate that Sls/Kettin genetically interacts with Cher and together with Cher mediates actin anchorage to the Z-disc . To extend our genetic interaction analysis we tested other sls alleles . First , we asked whether removal of one copy of sls would affect the Mef2>cherJF02077 phenotype . To do so we first analyzed the IFM from sls1 and slsj1D7 heterozygotes . Both sls1 and slsj1D7 appear normal and can fly ( Fig 7A and 7B ) . Yet , when these alleles were combined with Mef2>cherJF02077 , most myofibrils fray and sarcomeres disappear ( Fig 7C and 7D ) . This genetic interaction is highly significant for both sls1 and slsj1D7 ( Fig 7E ) . We also measured the lethality in Mef2>cherJF02077 alone or in combination with different sls alleles . Consistently , all sls alleles significantly enhance the lethality associated with depletion of Cher ( Fig 7F ) . Lastly , we tried a subtler approach , measuring the interaction using our infrared laser tachometer in transheterozygous flies . The cher mutant heterozygotes as well as slsj1D7 heterozygotes can sustain flight for more than 30 seconds ( Fig 7G–7J ) . Yet , in slsj1D7 cherΔ5 and slsj1D7cherQ1415sd transheterozygotes flying ability was severely affected , with flies unable to sustain flight for more than a couple of seconds ( Fig 7K and 7L ) . Surprisingly , slsj1D7 cherQ1042x could normally sustain flight ( Fig 7M ) . Heterozygotes and transheterozygotes showed no obvious sarcomere defects , when analyzed by confocal microscopy ( S4 Fig ) . Thus , cher and sls strongly genetically interact across multiple allelic combinations and enhance different cher phenotypes , suggesting that the interaction of titin and filamin is of crucial importance to sarcomere structure . As Cher and Sls/Kettin colocalize at the Z-disc in the IFM and our genetic interaction data indicate they function jointly , we decided to assess the physical interaction of these proteins . As a starting point , we used the cherCPTI1399 ( Flag-Venus tag ) allele in combination with slsZCL2144 ( His/EGFP-tag ) , which allows us to immunoprecipitate Sls as bait protein using Ni-beads and detect all interacting Cher isoforms using Flag antibody . In this approach , all Cher isoforms were enriched in Sls-containing beads , compared to the control beads ( Fig 8A ) . This suggested that all Cher isoforms are in a complex with Sls . To test if Kettin , the most common Sls isoform in the IFM , is responsible for Cher binding , we immunoprecipitated cherCPTI1399 with anti-Flag affinity beads and tested for Kettin presence . Again , Kettin was enriched in the Cher-containing beads , suggesting that Cher can bind Kettin in the IFM ( Fig 8B ) . To independently confirm this interaction , we analyzed the localization of filamin in a titin mutant . In slsZCL2144 heterozygotes bearing a Flag-tagged allele of Cher ( cher1399/+ ) sarcomeres appear normal with both Sls:GFP and Cher:Flag colocalizing at the Z-disc ( Fig 8C ) . However , in slsZCL2144 homozygotes Sls is reduced at the Z-disc and accumulates into large ectopic protein aggregates ( Fig 8D ) . Intriguingly , Cher-Flag is no longer observed at the Z-disc , but is instead recruited to the large ectopic Sls aggregates ( Fig 8D ) . These results support the notion that Sls binds and recruits Cher into the Z-disc . We then wondered if we could further narrow our protein-protein binding analysis . Both Sls and Cher are huge proteins not amenable to standard protein purification protocols . However , recent advances in computational approaches to study protein-protein interactions have shown that evolutionarily persistent protein complexes tend to leave a covariation signature [39] . Since protein complexes can be tightly bound , mutations in one component may be compensated with mutations in other components . This evolutionary covariation signature can be thought of as the correlated changes that appear in the coding sequence of two proteins [39–42] . Furthermore , the strength of the correlation tends to be higher at contact areas [43] . Therefore , we relied on finding the peak of positive evolutionary covariation between cher and sls as a proxy for their potential contact sites ( see Materials and methods ) . We first divided the coding regions of both genes in alignment blocks and then used those blocks to obtain partial covariation signatures . We divided cher and sls in 4 and 250 blocks respectively and calculated the covariation between all the possible combinations . A highly-correlated area was found , comprising 2 cher blocks , encoding Ig 8–14 and Ig 15–22 and the N-terminus of Sls corresponding to the Kettin isoform ( Fig 9A and 9B ) . These results suggest the C-terminal half of Cher binds to Kettin . Next , we wanted to know which of the two identified Cher areas mediates the interaction with Sls/Kettin . We therefore expressed and purified two regions of Cher matching the highly coevolving blocks , Cher Ig 9–11 and Cher Ig 19–22 and used them to test for binding against Sls protein purified from thorax extracts . Cher Ig 19–22 , but not Cher Ig 9–11 interacts with Sls-GFP ( Fig 9C ) . Thus , the computational results agree with the biochemical data , indicating an interaction of Kettin with the C-terminal Ig 19–22 domains of Cher .
The giant elastic protein titin is a 1 μm long flexible filament that spans half the sarcomere . In Drosophila the titin homolog Sls spans from the Z-disc to the edge of the A-band , providing elasticity between the thick filaments and the Z-disc , as a molecular spring [37 , 48] . Elasticity comes initially from the extension of the PEVK region , with the lowest mechanical stability , followed by the N2B region and finally the unfolding of the Ig and Fibronectin domains [49 , 50] . Titin is believed to function as a massive protein scaffold . Consistently , at least 24 direct human titin ligands have been found , with 8 of them in the Z-disc/I-band region including actin , ɑ-actinin , nebulin and FLNc [51] . Filamin is likewise a large scaffold protein . We show that the link previously described biochemically between filamin and titin is conserved in invertebrate muscles and required for the stability of the Z-disc . First , using four different assays we show that cher and sls display a strong genetic interaction , suggesting Sls and Cher have a common function in muscles . We then tested for a physical interaction . His-tagged Sls can precipitate all Cher isoforms and inversely , Flag-tagged Cher co-immunoprecipitates Kettin , the most common IFM isoform of Sls . Common to all Cher isoforms is the last C-terminal region containing the last 8 Ig domains . Experiments with bacterially purified Cher Ig domains demonstrate the requirement of the last four Ig domains of Cher for this interaction . Thus , Sls-Cher binding happens near the dimerization domain of Cher . A similar in vitro binding has been shown between the Zis1-2 region of human titin to the Ig 20–24 domains of both FLNa and FLNc [52] . While we cannot rule out an indirect binding , because Sls could only be purified from thorax extracts , our results add that filamin-titin binding is crucial for sarcomere stability and indicates that this interaction is conserved . Lastly , we provide direct evidence for the function of Cher-Sls binding . Removing the last C-terminal 8 Ig domains of Cher with the cherQ1415sd mutant , thereby removing the Sls-binding site , results in a smaller and fractured Z-disc , and actin incorporation into the H-zone . A smaller Z-disc is a very representative phenotype . Apart from cher , it has only been reported for sls [38] . In summary , Cher binds the I-band region of Sls/Kettin via C-terminal Ig domains; cher and sls genetically interact; and removal of the Sls/Kettin-binding region in Cher leads to a sls-like phenotype resulting in Z-discs that are smaller or fractured along the perpendicular axis ( Fig 10D ) . Our results therefore strongly suggest that filamin and titin are part of a complex mediating Z-disc stability . Maintaining actin thin filaments aligned and anchored is the central function of the Z-disc protein complex . Thus , many Z-disc proteins directly bind actin . Not surprisingly , filamin , a well-studied actin-binding protein localizes at the Z-disc where it binds actin through a conserved N-terminal ABD , composed of two CH domains . We now show that filamin-actin association is critical for Z-disc cohesion , by showing a widened Z-disc phenotype for different cher mutants that specifically affect the isoform containing the ABD or by specifically disrupting the ABD . Further , we show a genetic interaction between these cher alleles and Act88F , confirming a functional filamin-actin link in muscles . The widened Z-disc phenotype is unique compared to other IFM phenotypes described , in agreement with the proposed specific filamin-actin function in the Z-disc . The Z-disc widens and an actin-free area appears in place of the Z-disc ( Fig 10C ) . However , the adjacent sarcomeres are still well organized and the phenotype is also unevenly distributed across myofibrils . This suggests that filamin is not required for the initial assembly of myofibrils , but rather for maintaining sarcomere structure during repetitive contractile load . In line with this notion , the Z-disc widens parallel to the vector of sarcomere contraction . A somewhat similar widened Z-disc phenotype is seen upon stretching isolated myofibrils using a piezoelectric micromotor [37] . Further , a filamin mutation in medaka , which leads to myofibril degeneration , can be rescued by inhibiting muscle contraction [53] , and filamin has been shown to localize to sarcomeric microlesions formed upon strong contraction and mediate repair [54] . This again supports the proposal that stretching forces caused by muscle contractions produce the Z-disc widening phenotype in CherD mutants . This phenotype is specific to mutations of the filamin ABD , while maintaining Z-disc cohesion perpendicular to the sarcomere is likely mediated by both filamin-actin and filamin-titin binding . Z-discs with higher levels of actin at the Z-disc were sometimes seen upon disruption of Cher ABD-containing isoforms . One explanation for this could be the better accessibility of epitopes for binding of phalloidin in the widened Z-disc sarcomeres . It is well documented that antibodies cannot penetrate IFM myofibrils well , e . g . the myosin antibody stains only at the A/I-junction and the M-line [55] . Tearing and widening of Z-discs may occasionally expose additional epitopes on thin filaments leading to the appearance of higher levels of actin . Finally , we show that both mutating the ABD in cherΔ5 or the Sls-binding/dimerization domains in cherQ1415sd leads to actin filaments invading the H-zone . We propose that in both cases individual thin filaments are no longer stably anchored at the Z-disc , and can therefore be occasionally moved by myosin power strokes into the H-zone . Cher provides thin filaments with the necessary anchorage and elastic support to remain attached to the Z-disc . Finally , as both cherΔ5 and cherQ1415sd mutants share this phenotype and because the addition of a mutated copy of sls greatly enhances this phenotype , we propose that both titin-binding and actin-binding are required for keeping thin filaments anchored ( Fig 10C and 10D ) . Importantly , we show a direct structural role for filamin in addition to the signalling and mechanosensing role ascribed to filamin in muscles so far [7] . We have shown for the first time that filamin crosslinks parallel actin filaments with the widened Z-disc phenotype , in addition to the previously demonstrated perpendicular crosslinking in nonmuscle cells [9] . Related to the Z-disc structure , our data show that Z-discs require much more actin crosslinking than just by ɑ-actinin to withstand the strong contractile forces acting on them .
Unless specified , all crosses were done at 25°C . The following fly stocks were obtained from the Bloomington Drosophila stock center: Mef2-Gal4 , cherCPTI1399 , cherCPTI847 and cherCPTI1403 [31]; cherMI07480-GFSTF . 0 , an insertion that integrates a Flag and an EGFP tag [32]; the RNAi transgene cherJF02077; the slsZCL2144 allele ( Sls-GFP ) is a protein trap that incorporates a His-Tag and a EGFP sequence into all annotated sls transcripts [38]; sls1 ( D-Titin14 ) is an EMS amorph mutant and slsj1D7 is a P{lacW} insertion into an exon encoding part of the PEVK-2 domain that fails to complement sls1 and sls-uncovering deficiencies [56]; the deficiency line Df ( 3R ) Exel6176 uncovers the entire cher locus and was used in combination with all cher alleles . The RNAi transgene cherKK107451 was obtained from the Vienna Drosophila RNAi Center . The cherΔ5 mutant is a deletion that removes the CherD transcription start site , was made by imprecise excision of a P-element; cher1 , cherQ1415sd and cherQ1042X are EMS mutants; cherQ1415sd and cherQ1042X introduce a stop codon in positions 1415 and 1042 , respectively; all were a kind gift from Lynn Cooley . Cher Protein Trap lines cherCPTI1399 , cherCPTI847 and cherCPTI1403 were obtained from the Drosophila Genomics and Genetic Resources at Kyoto Institute of Technology [31] . The UAS-CherA-GFP stock was a kind gift from Sven Huelsmann [27] . IFM dissection was done as previously described [57 , 58] . Half thoraces were glycerinated ( 20 mM Na-Phosphate pH 7 . 2 , 2 mM MgCl2 , 2 mM EGTA , 5 mM DTT , 0 . 5% Triton X-100 , 50% glycerol ) overnight at -20°C . IFMs were dissected , washed and then fixed with 4% paraformaldehyde in relaxing solution ( 20 mM Na-Phosphate pH 7 . 2 , 2 mM MgCl2 , 2 mM EGTA , 5 mM DTT , 5 mM ATP ) with protease inhibitors ( Roche ) . The following primary antibodies were used: rat anti-Actinin MAC276 ( 1:100 , Babraham Bioscience Technologies ) , mouse anti-Flag 1:400 ( Sigma-Aldrich ) , rat anti-Kettin KIg16 MAC155 ( 1:400 , Babraham Bioscience Technologies ) . Primary antibody incubation was carried out overnight in PBS-0 . 1% Triton X-100 and secondary antibodies of the Alexa series ( ThermoFisher Scientific ) used at a 1:400 dilution and TRITC-phalloidin were incubated in PBS for 2 hours . Samples were mounted in ProLong Gold antifade solution ( ThermoFisher Scientific ) . All images were acquired using a 63x 1 . 4 NA HC Plan Apochromat oil objective on a Leica SP8 confocal microscope . Properly stained muscles were manually selected and aligned so that fibers run left to right . Once a muscle fiber was selected and aligned , random areas were imaged at 9x further magnification corresponding to 420 μm2 at an image resolution of 1024 x 1024 pixels . All quantifications were done at the same magnification and resolution to assure homogeneity . We used 488 nm-20 mW , 552 nm-20 mW , and 638 nm-30 mW lasers . Emitted light was detected with PMT and HyD detectors . Laser power was typically set between 1% and 3% , the pinhole was set to 1 airy unit and the gain was set between 700 V and 900 V ( PMT ) or between 10 V and 100 V ( HyD ) . Smart offset was kept at 0% . Scanning speed was set to 400 Hz . Comparable settings were used for all image acquisitions . Missing sarcomeres were estimated as a decrease in the total number of distinguishable sarcomeres per image . Actin accumulation was obtained by measuring phalloidin-TRITC staining grayscale values in the H-zone divided by the same measurement in the zone between the H-zone and the I-band , where actin is normally present . To ensure homogeneity all measurements were done using a 0 . 9 x 0 . 9 μm region of interest . Cher intensity at the Z-disc was measured using the ImageJ plot profile tool line , which displays the intensities of pixels along a line . The X-axis represents distance ( μm ) . To better compare pixel intensities from different flies , the pixel intensities were first normalized for each individual image . Average profiles for each Cher trap line were computed from 10 images and plotted with RStudio . Embryos bearing the correct genotype were selected for the absence of GFP fluorescence ( TM3 , twist-Gal4 , UAS-GFP , Sb ) and incubated at a controlled temperature . The resulting adults were counted and the lethality ratio was calculated . Statistical significance in all figures was assessed using one-way ANOVA followed by post-hoc Tukey tests ( GraphPad Prism 7 ) and plotted as a box plot with notches representing 95% confidence intervals using the boxplot{graphics} function in R software . 50 adult fly thoraces were homogenized in lysis buffer ( 20 mM Tris- HCl pH 8 , 100 mM NaCl , 1 mM MgCl2 , 1 mM DTT , 5% glycerol , 0 . 5% Triton X-100 and complete EDTA-free protease inhibitor; Roche ) . Protein extracts were then incubated with prewashed anti-FLAG M2 affinity resin ( Sigma-Aldrich ) or Ni NTA agarose beads for 3 hours at 4°C . After incubation , the beads were washed twice with wash buffer ( 20 mM Tris-HCl pH 8 , 150 mM NaCl , 5% glycerol , 0 . 2% Triton X-100 ) . Bound proteins were eluted by boiling in 2x SDS sample buffer . Eluates were analyzed by SDS-PAGE and by immunoblotting . For Sls-GFP purification , 100 adult fly thoraces were homogenized in lysis buffer . Protein extracts were then incubated with prewashed anti-GFP magnetic beads ( ChromoTek ) for 3 hours at 4°C . After incubation , the beads were washed twice with wash buffer ( 20 mM Tris-HCl pH 8 , 250 mM NaCl , 5% glycerol , 0 . 2% Triton X-100 ) . Sls-containing beads were then incubated with bacterially expressed proteins . Following incubation , the beads were again washed twice with wash buffer and bound proteins were eluted by boiling in 2x SDS sample buffer . Cher Ig 19–22 and Cher Ig 9–11 were cloned into the pGEX-5X-1 vector between EcoRI and XhoI for bacterial expression . E . coli strain BL-21 bacteria expressing GST-tagged recombinant proteins were lysed by sonication in 20 mM Tris-HCl pH 8 , 200 mM NaCl , 1 mM MgCl2 , 1 mM DTT , 5% glycerol , 0 . 2% Triton X-100 , 1 mg/ml lysozyme and complete EDTA-free protease inhibitor ( Roche ) . Immunoblotting antibodies were used at the following dilution: rat anti-Kettin KIg16 MAC155 ( 1:4000 , Babraham Bioscience Technologies ) ; mouse anti-FLAG antibody at 1:5000 ( Sigma-Aldrich ) . The immunoreaction was visualized by ECL ( Millipore ) . CherD protein structure was calculated through the RaptorX protein structure prediction server using Cher-PA as query and FLNA , B or C as templates . The best resulting structure was analyzed and colored using Chimera UCSC software . To generate CherD847 and CherD1403 structures , a predicted protein sequence based on Cher-PA and Venus-GFP was generated according to the protein trap insertion site . CherD847 introduces a Venus trap in between Ig 11 and Ig 12 , while in CherD1403 , the Venus trap is inserted into the first CH domain . The resulting sequences were modelled using RaptorX [59] . Protein covariation was calculated as previously described [40–42] . Briefly , exon-coding regions for each gene were obtained through the Table Browser at the UCSC genome browser ( genome . ucsc . edu ) . BED files containing Augustus exon predictions for each gene from the R5/dm3 genome assembly were sent to the Galaxy website ( www . usegalaxy . org ) . Alignments from 12 Drosophila species were directly obtained from Galaxy-stored MultiZ alignments in MAF format . MAF files were converted to Fasta format and imported to R software using read . dna{ape} . Pairwise distances were calculated for each species pair using dist . dna{ape} and transformed into their relative distances [60] . Finally , the Pearson correlation coefficient was used to compare relative distances using cor{stats} and plotted using corrplot{corrplot} . The wing beat frequency of a fly was determined using an optical tachometer as previously described [58] . In this study , 5-7-day old flies were first glued on pipette tips , followed by measurement of wing beat frequency for 5 minutes using an infrared laser tachometer ( Model UT372 , Uni-Trend Technology ) . A 30-second continuous flight window was selected from the 5-minute flight record . Thoraces were treated with 5 mM MOPS pH 6 . 8 , 150 mM KCl , 5 mM EGTA , 5 mM ATP , 1% Triton X-100 for 2 hours at 4°C , followed by overnight incubation in the same buffer without Triton X-100 but 50% glycerol . Samples were then washed in rigor solution ( 5 mM MOPS pH 6 . 8 , 40 mM KCl , 5 mM EGTA , 5 mM MgCl2 , 5 mM NaN3 ) and fixed in 3% glutaraldehyde , 0 . 2% tannic acid in 20 mM MOPS pH 6 . 8 , 5 mM EGTA , 5 mM MgCl2 , 5 mM NaN3 for 2 hours at 4°C . Secondary fixation and embedding were as described before [57] . Images of recognizable sarcomeres were acquired on a Tecnai 12 BioTwin 120 kV transmission electron microscope with an AMT XR80C CCD camera ( FEI ) . | The Z-disc is a macromolecular complex required to attach and stabilize actin thin filaments in the sarcomere , the smallest contractile unit of striated muscles . Mutations in Z-disc-associated proteins typically result in muscle disorders . Dimeric filamin organizes actin filaments , localizes at the Z-disc in vertebrates and causes muscle disorders in humans when mutated . Despite its clinical relevance , the molecular function of filamin in the sarcomere is not well understood . Here we use Drosophila muscles and an array of filamin mutations to address the molecular and cell biological function of filamin in the sarcomere . We show that filamin mainly serves as a Z-disc cohesive element , binding both thin filaments and titin . This configuration enables filamin to act as a bridge between thin filaments and the elastic scaffold protein titin from the adjacent sarcomere , maintaining sarcomere stability during muscle contraction . | [
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] | 2017 | Filamin actin-binding and titin-binding fulfill distinct functions in Z-disc cohesion |
The TLO genes are a family of telomere-associated ORFs in the fungal pathogens Candida albicans and C . dubliniensis that encode a subunit of the Mediator complex with homology to Med2 . The more virulent pathogen C . albicans has 15 copies of the gene whereas the less pathogenic species C . dubliniensis has only two ( CdTLO1 and CdTLO2 ) . In this study we used C . dubliniensis as a model to investigate the role of TLO genes in regulating virulence and also to determine whether TLO paralogs have evolved to regulate distinct functions . A C . dubliniensis tlo1Δ/tlo2Δ mutant is unable to form true hyphae , has longer doubling times in galactose broth , is more susceptible to oxidative stress and forms increased levels of biofilm . Transcript profiling of the tlo1Δ/tlo2Δ mutant revealed increased expression of starvation responses in rich medium and retarded expression of hypha-induced transcripts in serum . ChIP studies indicated that Tlo1 binds to many ORFs including genes that exhibit high and low expression levels under the conditions analyzed . The altered expression of these genes in the tlo1Δ/tlo2Δ null mutant indicates roles for Tlo proteins in transcriptional activation and repression . Complementation of the tlo1Δ/tlo2Δ mutant with TLO1 , but not TLO2 , restored wild-type filamentous growth , whereas only TLO2 fully suppressed biofilm growth . Complementation with TLO1 also had a greater effect on doubling times in galactose broth . The different abilities of TLO1 and TLO2 to restore wild-type functions was supported by transcript profiling studies that showed that only TLO1 restored expression of hypha-specific genes ( UME6 , SOD5 ) and galactose utilisation genes ( GAL1 and GAL10 ) , whereas TLO2 restored repression of starvation-induced gene transcription . Thus , Tlo/Med2 paralogs encoding Mediator subunits regulate different virulence properties in Candida spp . and their expansion may account for the increased adaptability of C . albicans relative to other Candida species .
Candida albicans is a commensal yeast commonly recovered from mucosal surfaces in humans . C . albicans is also a versatile opportunistic pathogen , responsible for a variety of superficial infections as well as more severe , life threatening infections in severely immunocompromised patients . Phenotypic versatility is an important characteristic of C . albicans and this flexibility allows C . albicans to adapt to this wide range of niches in the human host [1]–[3] . Over the past decade , transcriptional profiling of C . albicans using microarray and RNA-seq technologies has revealed that rapid adaptation to local environmental conditions involves elaborate , programmed shifts in transcription pattern . The biochemistry of transcriptional regulation has been studied intensively in the model yeast S . cerevisiae . Most genes transcribed by RNA polymerase II ( PolII ) require a carefully orchestrated series of events to initiate transcription . The formation of the pre-initiation complex and recruitment of PolII requires the function of Mediator , a large multi-subunit protein complex [4] , [5] . Mediator is thought to be required to bridge DNA-bound transcription factors with the rest of the transcriptional machinery [6] , [7] . The complex is generally divided into three modules: a head , middle and tail . The tail region includes Med2 , Med3 and Med15/Gal11 and is thought to be the part of Mediator that interacts directly with transcription factors such as Gal4 and Gcn4 [8] . Most components of Mediator are highly conserved between S . cerevisiae and C . albicans [9] . However , completion of the C . albicans genome sequence [10] , [11] revealed a family of up to 15 telomeric ( TLO ) genes that were subsequently found to encode a domain with homology to the Mediator tail subunit Med2 of S . cerevisiae [9] , [12] , [13] . This TLO gene expansion is unique to C . albicans , as most of the less pathogenic non-albicans Candida species such as C . tropicalis and C . parapsilosis have only one TLO gene . C . dubliniensis , the closest relative to C . albicans has two TLO genes , namely TLO1 and TLO2 . TLO1 ( Cd36_72860 ) is located internally on chromosome 7 and TLO2 ( Cd36_35580 ) is at a telomere-adjacent locus on the right arm of chromosome R [14] . C . dubliniensis TLO2 appears to be the ancestral locus , based on synteny with , and homology to , the single C . tropicalis orthologue CTRG_05798 . 3 [14] . All candidal TLO genes share the Med2-like domain of the C . albicans TLOs and appear to be the MED2 orthologs in these species . Indeed , biochemical studies have shown that like the S . cerevisiae Med2 protein , Tlo proteins co-purify with the C . albicans orthologs of mediator tail module components Med3 and Med15 [9] . The 15 TLO genes in the C . albicans type strain SC5314 can be classified into four distinct clades based on gene structure , including the highly expressed TLOα clade ( 6 members ) , a single TLOβ gene , the poorly expressed TLOγ clade ( 7 members ) and a single TLOψ gene which is a pseudogene [12] . Biochemical studies have shown that the levels of Tloα and Tloβ proteins is in vast excess to the amount necessary to be a stoichiometric subunit of Mediator ( 9 ) and stands in stark contrast to the situation in S . cerevisiae , where the Med2 subunit is expressed at roughly equivalent amounts to other subunits of the complex . Due to the large numbers of TLO genes in C . albicans , functional analysis of Mediator has largely been restricted to other subunits , including Med3 ( tail domain ) , Med31 ( middle domain ) , Med20 ( head domain ) and Med13/Srb9 ( kinase domain ) . Med31 and Med20 are required for the transition of C . albicans yeast cells to filamentous hyphae and for biofilm formation , two important pathogenic traits of the organism [9] , [15] . Med31 is required for expression of the genes ALS1 and ALS3 that encode cell-surface proteins involved in biofilm production , consistent with the poor biofilms produced by med31Δ mutants . In addition , Med31 is required for the expression of genes regulated by the transcription factor Ace2 , which regulates many genes involved in cell wall remodelling during cell separation . Consistent with this , the med31Δ mutant exhibits defective cytokinesis . Deletion of MED3 resulted in a stronger filamentous growth defect , resulting in short pseudohyphae in serum or liquid media supplemented with GlcNac [9] . Deletion of Mediator subunits also has complex effects on the transcriptional circuit governing the white-opaque switch , a phenotypic transition involved in mating [16] . C . albicans is the most prevalent pathogenic fungal species . The closely related species C . dubliniensis is responsible for far fewer infections and is a minor component of the human oral flora [14] , [17] . The yeast-to-hypha transition is an important virulence trait of C . albicans and C . dubliniensis forms fewer hyphae than C . albicans in response to most in vitro and in vivo hypha-inducing stimuli [17] . C . dubliniensis requires much stronger environmental cues , including nutrient depletion , in order to activate a transcriptional response that will induce the yeast-to-hypha transition [18]–[20] . Less effective transcriptional responses may also account for the increased susceptibility of C . dubliniensis to environmental stress relative to C . albicans [21] . It has been suggested that the expansion of the TLO family in C . albicans may account for the greater transcriptional flexibility and adaptability of this species relative to C . dubliniensis and the other non-albicans Candida species [22] . However , direct analysis of TLO null mutants has not been attempted in C . albicans , due to the large number of TLO genes and the likelihood of redundancy in the TLO family . In this study , we exploited the closely related species C . dubliniensis as a model to study TLO gene function and to construct the first tlo null strains . We found that C . dubliniensis mutants lacking both TLO1 and TLO2 ( tlo1Δ/tlo2Δ ) exhibit widespread changes in the transcription of virulence-associated genes . ChIP analysis detected Tlo1 within the coding regions of ORFS , as well as subtelomeric regions and the Major Repeat Sequence ( MRS ) . Interestingly , in strains lacking only one of the two TLO genes , expression of distinct subsets of genes was altered . We propose that expansion of the TLO family , even to only two members , has facilitated the evolution of functional diversity and may be of particular importance in the evolution of an expanded set of TLO paralogs together with the increased virulence in Candida albicans .
The two Candida dubliniensis TLO genes , TLO1 and TLO2 , encode proteins of 320 and 355 amino acids , respectively , and share 58% identity . Homology to each other and to the C . albicans Tlo proteins is concentrated in the N-terminal 120 residues of the proteins . Tlo1 and Tlo2 share 81% identity in this N-terminal region , which also exhibits homology to S . cerevisiae Med2 ( Figure 1A ) . The amino acid sequence of Tlo2 is 25 residues longer and contains a central triplet repeat of the motif KAAAKVKEEQ . C . dubliniensis is a diploid organism and gene deletion studies ( see below ) and Southern blot analysis confirmed that C . dubliniensis strain Wü284 has two alleles of TLO1 ( Figure S1 ) . However , Southern blot experiments could detect only one allele of TLO2 at the telomere of chromosome R ( ChR ) in strain Wü284 , indicating that one copy of ChR in Wü284 was truncated resulting in loss of one allele of TLO2 ( Figure S1 ) . PCR analysis of the tlo2Δ strain confirmed that bases 1 to 918 of the second TLO2 allele are deleted due to this truncation , with only a small 150 bp remnant of the ORF remaining . Analysis of gene expression by QRT-PCR revealed that TLO1 mRNA is expressed over 2-fold higher than TLO2 mRNA in YEPD at 37°C ( Figure 1B ) . We first asked if C . dubliniensis Tlo is a component of Mediator . To address this , we purified Mediator from whole cell extracts of C . dubliniensis wild-type and tlo1Δ/tlo2Δ mutant cells using a dual affinity tag on the C . dubliniensis ortholog of the conserved Med8 subunit of Mediator . The resulting Mediator complex was analyzed for purity by SDS-PAGE and for composition by mass spectroscopy with untagged WT C . dubliniensis serving as a control . Biochemical analysis showed that the C . dubliniensis Mediator , like C . albicans , is composed of a complete set of orthologs of the S . cerevisiae complex ( Figure 2 ) . Mediator purified from the tlo1Δ/tlo2Δ mutant lacked tail subunits Med3 , Med15 , Med16 and Med5 subunits ( Figure 2 , Lane 3 ) . Importantly , Mediator purified from the C . dubliniensis tlo1Δ/tlo2Δ strain or from a med3Δ null strain had equivalent composition , lacking tail components Tlo1 , Med3 , Med5 , Med15 and Med16 ( Figure 2 , Lanes 3 and 4 ) . To determine the relative abundance of Tlo proteins compared to other Mediator subunits , we constructed strains carrying Tlo1-HA , Med3-HA and Med8-HA tagged derivatives . Immunoblotting of whole cell extracts revealed that C . dubliniensis Tlo1 Mediator subunit is expressed in amounts comparable to the Med3 Tail Module and Med8 Head Module subunits ( Figure S2 ) . Consistent with this finding , purification of an affinity tagged Tlo1 protein from C . dubliniensis yielded only the Mediator associated form ( Figure 2 , Lane 5 ) and no free Tlo1 protein . Thus , in C . dubliniensis Tlo1 appears to be acting solely as a component of Mediator . Deletion of TLO2 did not reduce filamentous growth significantly ( Figure 3A ) , while deletion of TLO1 resulted in reduced hyphal production ( Figure 3A ) , as reported previously [14] . However , the double tlo1Δ/tlo2Δ mutant ( tloΔΔ ) had a more severe filamentation defect than the TLO1 deletion alone , suggesting that TLO2 partly compensates for the deletion of TLO1 in the single mutant ( Figure 3A , 3D ) . The cellular morphology of the tlo1Δ/tlo2Δ mutant in 10% serum was characteristically pseudohyphal ( Figure 3D ) . In order to investigate if TLO1 and TLO2 are functionally different , we reintroduced TLO1 and TLO2 individually to the tlo1Δ/tlo2Δ mutant using a previously described integrating vector pCDRI . This strategy was employed as it allowed us to express both TLO1 and TLO2 to similar levels ( Figure 1C ) thus allowing a direct comparison of their ability to complement the phenotypes of the tlo1Δ/tlo2Δ mutant . Introduction of TLO1 could restore true hypha production to almost wild-type levels , whereas TLO2 restored hypha formation to approximately 50% of wild-type levels ( Figure 3B , 3D ) . These data show that TLO1 and TLO2 differ in their ability to activate filamentous growth . The colony morphology of the tlo1Δ , tlo2Δ and tlo1Δ/tlo2Δ mutant strains following growth on nutrient-rich solid medium ( YEPD agar ) at 37°C was indistinguishable from that of wild-type . At the cellular level , the tlo1Δ/tlo2Δ mutant grew as short chains of 3–4 cells , which is indicative of a defect in cytokinesis ( Figure S3A ) . A similar phenotype was described by Uwamahoro et al . [15] in a C . albicans med31Δ mutant . Wild-type cytokinesis was restored in the tlo1Δ/tlo2Δ mutant following reintroduction of TLO1 but not TLO2 ( Figure S3A ) . Growth defects of tlo mutants became apparent when grown on non-optimal media . On Spider agar , the wild-type strain formed smooth white colonies , typical of C . dubliniensis on this medium . However , the tlo1Δ/tlo2Δ mutant formed heavily wrinkled colonies on this medium ( Figure S3B ) . Wild-type smooth colonies could be restored by complementation of the tlo1Δ/tlo2Δ mutant with either TLO1 or TLO2 ( Figure S3B ) . Following 5 days incubation on Pal's medium , the double mutant produced extensive levels of pseudohyphae , but unlike wild-type which produced copious amounts of chlamydospores on this medium , no terminal differentiation of these pseudohyphae to chlamydospores was observed ( Figure S4A ) . The TLO1 mutant and the tlo1Δ/tlo2Δ double mutant also exhibited longer doubling times in several different liquid media , including YEPD ( Table 1 ) . When galactose was substituted for glucose in YEP medium ( YEP-GAL ) , the growth rate of the tlo1Δ and the tlo1Δ/tlo2Δ mutants was reduced with doubling times 1 . 4 to 1 . 6 times longer than wild-type doubling times , respectively ( Table 1 ) . Growth in synthetic liquid Lee's medium was also retarded , with a doubling time 1 . 6 fold higher than that of wild-type . The addition of 1% peptone restored growth to WT levels in the tlo1Δ/tlo2Δ mutant ( Figure S4B ) while the addition of carbon sources or individual amino acids to solid Lee's medium did not . Reintroduction of either TLO1 or TLO2 in the tlo1Δ/tlo2Δ double mutant background restored doubling times to wild-type levels in YEP-GAL . However , reintroduction of TLO1 had a significantly greater effect on doubling time compared to complementation with TLO2 . In contrast , either TLO1 or TLO2 restored the growth defect on Lee's medium to the same degree ( Table 1 ) . We next investigated if TLO genes affect a range of stress responses . Supplementation of YEPD agar with H2O2 ( 7 mM ) or menadione ( 100 mM ) , which generate reactive oxygen species , inhibited growth of the tlo1Δ/tlo2Δ mutant , partly inhibited growth of the tlo1Δ mutant , while growth of the tlo2Δ single mutant was largely unaffected by these oxidizing agents ( Figure 3E ) . Reintroduction of either TLO1 or TLO2 to the tlo1Δ/tlo2Δ strain restored wild type growth in the presence of both H2O2 and menadione , indicating that both TLO genes have a role in the growth of cells under oxidative stress . Tlo proteins are components of the Mediator complex , which is thought to facilitate interactions between Mediator and DNA-bound transcriptional activators [8] . As Tlo protein and Med3 are required for the stability of the Mediator tail in C . dubliniensis ( Figure 2 ) , we hypothesized that deletion of MED3 and TLO genes should result in similar phenotypic effects . To test this , we compared the phenotypes of C . dubliniensis tlo1Δ/tlo2Δ and med3Δ mutants . The C . dubliniensis med3Δ mutant , similar to the C . albicans med3Δ mutant [9] and the C . dubliniensis tlo1Δ/tlo2Δ mutant was unable to form true hyphae in 10% serum ( Figure 3C ) and exhibited extended doubling times in YEP-Gal ( Figure S5A ) and increased susceptibility to H2O2 ( Figure S5B ) . All of these properties could be restored to wild-type levels in the med3Δ mutant with the reintroduction of a wild-type copy of MED3 on pCDRI ( Figure 3C , S4A , S4B ) . Since Mediator is important for transcription regulation , we analysed RNA expression patterns in the tlo1Δ/tlo2Δ mutant relative to wild-type cells grown in nutrient-rich growth conditions ( YEPD at 37°C ) and grown in hyphal inducting conditions ( water plus 10% serum , optimal for C . dubliniensis hypha formation ) . During exponential growth in YEPD , a total of 746 genes exhibited a 1 . 5-fold or greater increase in expression and 635 genes exhibited a 1 . 5-fold or greater reduction in expression ( Q≤0 . 05; Figure 4A ) . This scale of differential gene expression observed in our tlo1Δ/tlo2Δ is similar to that seen in S . cerevisiae Mediator tail mutants [23] , [24] . In the nutrient-rich YEPD broth , the tlo1Δ/tlo2Δ mutant exhibited a transcriptional profile that resembled a response to nutrient starvation ( Figure 4B ) . The induced set of genes was enriched for processes associated with catabolism of alternative carbon and nitrogen sources such as N-acetyl-glucosamine ( NAG1 , NAG3 , NAG4 , NAG6 ) , amino acids ( e . g . GDH2 , CAR1 , PUT2 , PUT1 , LPD1 , FDH1 and FDH3 ) and fatty acids . The tlo1Δ/tlo2Δ mutant cells also up-regulated key genes of gluconeogenesis ( PCK1 and FBP1 ) and the glyoxylate cycle ( ICL1 and MDH1 ) ( Figure 4B ) . In concert with this , the tlo1Δ/tlo2Δ mutant also caused down-regulation of genes encoding glycolytic enzymes ( PFK1 , PFK2 , FBA1 , GPM1 and ENO1 ) and the glycolytic regulator TYE7 . The tlo1Δ/tlo2Δ mutant also exhibited a greater than 2-fold decrease in expression of genes encoding proteins important for sulphur amino acid biosynthesis ( e . g . SAM2 , MET1 , MET6 , MET10 , MET14 , MET16 ) and ergosterol biosynthesis ( ERG1 , ERG9 , ERG25 ) . In addition , some hypha-specific genes were induced in the tlo1Δ/tlo2Δ mutant grown in YEPD . This included IHD1 , RBT5 and SAP7 ( induced in C . dubliniensis hyphae ) as well as several regulators of biofilm and hyphal growth ( BCR1 , NRG1 , SFL1 , TEC1 and EED1 ) . Gene Set Enrichment Analysis ( GSEA ) was used to compare the tlo1Δ/tlo2Δ regulated genes with published microarray datasets . GSEA indicated that the set of genes differentially expressed in the tlo1Δ/tlo2Δ mutant was enriched for genes induced during infection of RHE [25] and genes both induced and repressed during infection of bone-marrow derived macrophages [26] ( Figure S6 ) . We also examined the transcript profile of the tlo1Δ/tlo2Δ mutant in 10% serum relative to wild-type ( Figure 4A ) . After 1 h , 868 genes were significantly up-regulated 1 . 5-fold or greater and 816 ORFs downregulated ( Q≤0 . 05 ) relative to wild-type expression at the same time point . In this medium the tlo1Δ/tlo2Δ mutant exhibited reduced expression of several key regulators of filamentous growth including RAS1 , RIM101 , EFG1 and UME6 . In addition , the hyphal growth regulating cyclins CLN3 and HGC1 were also down regulated in the tlo1Δ/tlo2Δ mutant . The tlo1Δ/tlo2Δ mutant also exhibited reduced expression of many cell wall proteins whose induction is chracteristic of the yeast-to-hypha transition in C . dubliniensis , including HWP1 , RBT1 , RBT5 and SOD5 [20] . Paradoxically the tlo1Δ/tlo2Δ mutant exhibited a more rapid induction of the hypha-specific gene ECE1 following 1 h incubation in serum , and expression remained elevated relative to wild-type at 3 h . In addition to hyphal genes , the tlo1Δ/tlo2Δ mutant exhibited reduced expression of galactose metabolic genes GAL1 , GAL7 and GAL10 . After 3 hours in 10% serum , a similar number of genes were differentially expressed 1 . 5-fold or greater ( n = 1726 ) . However , the level of differential expression at many genes had decreased by 3 h ( Figure 4A ) . In order to investigate whether TLO1 and TLO2 regulate similar or different sets of genes , we analysed the transcript profiles of the TLO1 and TLO2 complemented mutants rather than the tlo1Δ and tlo2Δ single mutants , as the complemented strains exhibited equivalent expression of TLO1 or TLO2 ( Figure 1B , 1C ) . Both TLO1 and TLO2 largely restored the transcript profile of the tlo1Δ/tlo2Δ mutant back to wild-type levels in both YEPD and 10% serum ( Figure 5A , 5B ) . However , some TLO-specific transcript patterns were also detectable . Sixty-one genes were regulated in a TLO-specific manner in YEPD ( Table S1 ) . In comparison to the TLO1-complemented strain , the TLO2-complemented strain showed a greater reduction in expression of many tlo1Δ/tlo2Δ induced genes , including many hyphal genes ( IHD1 , SAP7 and EED1 ) , negative regulators of hyphal growth ( NRG1 and SFL1 ) and some starvation-induced genes such as CAR1 , GDH3 , NAG3 and NAG4 ( Figure 5C ) . The TLO2 complemented strain was also better at restoring wild-type levels of SOD6 expression relative to the TLO1 complemented strain ( Figure 5C ) . In 10% serum , TLO1 was more effective at restoring expression of several genes required for induction of , and induced during , filamentous growth including UME6 and SOD5 ( Table S2 , Figure 5D ) . TLO1 was also a more effective inducer of GAL1 and GAL10 expression , which may explain why restoration of growth in galactose medium was more effective in the TLO1 reintegrant relative to the TLO2 complemented strain ( Figure 5D ) . In contrast , TLO2 was more effective at inducing HWP1 expression ( Figure 5D ) . More detailed analysis of hyphal-induced gene expression by QRT-PCR corroborated these microarray findings and supported the existence of a temporal defect in the induction of hypha specific genes in the tlo1Δ/tlo2Δ mutant . QRT-PCR showed that UME6 and SOD5 exhibited weak levels of induction in the tlo1Δ/tlo2Δ mutant relative to the TLO1 reintegrant ( Figure 5E , 5F ) . Although addition of TLO2 led to a minor increase in SOD5 expresssion , complementation with TLO1 restored high-level induction of SOD5 . Similarly , induction of GAL10 also occurred more rapidly in the TLO1-reintegrated strain relative to both the tlo1Δ/tlo2Δ mutant and the TLO2-reintegrated strain ( Figure 5G ) Conversely , QRT-PCR confirmed that TLO2 restored more rapid induction ( ∼10-fold ) of the HWP1 transcript following 1 h incubation in serum relative to the TLO1 reintegrant ( Figure 5H ) . However HWP1 expression in the tlo1Δ/tlo2Δ mutant and TLO1 reintegrant reached similar levels following three hours incubation in serum ( Figure 5H ) . This transcriptional analysis of the TLO1 and TLO2 complemented strains for the first time demonstrate functional diversification of Tlo proteins and show that different Tlos can regulate distinct subsets of genes . This study has shown that deletion of MED3 in C . dubliniensis results in similar phenotypes to those observed in the tlo1Δ/tlo2Δ mutant . In order to determine whether these gene deletions resulted in similar changes in transcript profile , we compared gene expression in the tlo1Δ/tlo2Δ and the med3Δ mutant in YEPD broth and in 10% serum . Hierarchical clustering was used to compare all 1 . 5-fold regulated genes in strains tlo1Δ/tlo2Δ and med3Δ during exponential growth . This comparison demonstrated the similarity of the transcriptional changes in both mutants relative to wild-type cells ( Figure 6A ) . A large number ( n = 47 ) of these commonly down-regulated genes were associated with the GO term “ribosome biogenesis” ( P = 3 . 89×10−19; FDR = 0 . 0 ) . In YEPD , both mutants exhibited aberrantly increased expression of the hypha-specific genes IDH1 , RBT5 and SAP7 as well as the regulators TEC1 and EED1 . Activation of starvation responses was also evident in the med3Δ strain , which like the tlo1Δ/tlo2Δ strain exhibited increased expression of genes involved in gluconeogenesis ( PCK1 , FBP1 ) and the glyoxylate cycle ( ICL1 , MDH1 ) . Decreased expression of glycolytic genes was less pronounced in med3Δ , however we could detect decreased expression ( >1 . 5-fold ) of PFK1 , PFK2 , CDC19 the glycolytic regulator TYE7 ( Figure S7 ) . Similar to tlo1Δ/tlo2Δ , med3Δ also exhibited increased expression of genes involved in oxidation/reduction processes , including amino acid catabolism ( Figure 6B ) in addition to reduced expression of genes involved in sulphur amino acid metabolism ( CYS3 , MET1 , 2 , 4 , 10 ) and ergosterol metabolism ( ERG1 , ERG251 ) . Following 1 h growth in water plus 10% serum the med3Δ and tlo1Δ/tlo2Δ mutants again exhibited a common set of co-regulated genes , however in serum many med3Δ mutant-specific responses could also be identified ( Figure 6B , 6C ) . The common gene set included regulators of filamentous growth , cell wall proteins and galactose metabolic genes ( Figure 6B ) . Importantly , the med3Δ mutant specifically exhibited an increase in expression of glycolytic genes ( n = 11; Figure 6C , S6 ) and genes encoding histones and proteins involved in chromatin assembly ( n = 18 ) . The med3Δ mutant also exhibited reduced transcription of genes associated with the GO terms “DNA replication” ( n = 43 ) and “telomere maintenance” ( n = 18 ) ( Figure 6C ) . These data indicate that in exponentially growing cells , Tlo proteins and Med3 are involved in regulating very similar processes . However , in response to specific environmental cues , these proteins may be required for regulation of specific sets of genes . Uwamahoro et al . [15] showed that in C . albicans deletion of MED31 , encoding a middle subunit of Mediator affected cytokinesis , filamentous growth and biofilm formation . Similarly , the C . dubliniensis tlo1Δ/tlo2Δ mutant described here also grew as chains of cells , typical of mutants with defects in cytokinesis ( Figure S3A ) . However , the C . albicans med31Δ mutant was capable of filamentous growth in response to serum , whereas the C . dubliniensis tlo1Δ/tlo2Δ mutant is incapable of forming true hyphae in serum . GSEA analysis of our C . dubliniensis tlo1Δ/tlo2Δ mutant transcript data identified a significant enrichment for genes that are also affected in a C . albicans med31Δ mutant [15] . Interestingly , while the tlo1Δ/tlo2Δ and the med31Δ deletions affected similar genes , the C . dubliniensis tlo1Δ/tlo2Δ mutant showed increased expression of genes that were both Med31-activated and -repressed in C . albicans ( Figure 7A ) . Inspection of these differentially expressed genes identified several genes required for biofilm formation that were downregulated in the C . albicans med31Δ mutant and induced in the C . dubliniensis tlo1Δ/tlo2Δ mutant , including ALS1 , TEC1 and SUC1 . Uwamahoro et al . [15] showed that reduced ALS1 expression in the C . albicans med31Δ mutant was largely responsible for the defect in biofilm formation . As the C . dubliniensis tlo1Δ/tlo2Δ mutant exhibited increased expression of ALS1 , we examined whether this mutant was affected in biofilm formation . In contrast to the med31Δ mutant phenotype , deletion of TLO1 and TLO2 in C . dubliniensis enhanced biofilm growth on plastic surfaces ( Figure 7B ) . In this case , complementation with TLO2 reduced the amount of biofilm formation more than complementation with TLO1 ( ANOVA P<0 . 05 ) ( Figure 7B ) . Thus the mediator middle domain has a different effect on biofilm formation than does the mediator tail domain . The genetic and biochemical data presented here and by Zhang et al . [9] support a hypothesis that Tlo proteins are the candidal orthologs of S . cerevisiae Med2 . In order to support this , we decided to compare the transcript profile of the C . dubliniensis tlo1Δ/tlo2Δ mutant and the S . cerevisiae med2Δ mutant to identify if there is an evolutionarily conserved core regulon controlled by these proteins [27] . Although the experimental details of the two studies vary , a comparison of all genes regulated 1 . 5-fold or greater in both mutants could identify a small but significant overlap in regulated genes ( Figure S8 ) . Carbohydrate metabolism genes that were downregulated in both mutants included the carbohydrate metabolism master regulator TYE7 and genes regulating carbohydrate catabolism and glycerol biosynthesis ( PFK1 , FBA1 , GPH1 , GDB1 ) . One of the signatures of the tlo1Δ/tlo2Δ mutant profile was the downregulation of sulphur amino acid metabolism and in S . cerevisiae , we could also detect down regulation of MET6 and MET13 as well as several other conserved genes regulating amino acid catabolism ( GCV1 , GCV2 , SHM2 ) . Both mutants exhibited a general increase in the expression of genes encoding enzymes involved in oxidation-reduction processes ( Figure S8 ) . In order to characterise Tlo1-DNA interactions , we carried out chromatin immunoprecipitation and high-density C . dubliniensis microarray ( ChIP-chip ) of HA-tagged Tlo1 from cells grown in YEPD broth at 30°C . On a genome wide level , we found extensive binding of Tlo1 to all chromosomes . The regions exhibiting the greatest enrichment included subtelomeric and telomeric regions and the Major Repeat Sequence ( MRS ) ( Figure 8A ) . Outside of these regions , the interaction of Tlo1 was closely associated with the coding regions of genes ( Figure 8A ) . Excluding subtelomeric regions , only 45 intergenic regions exhibited a significant enrichment peak not associated with a coding region . In addition , we did not observe any association between Tlo1 and 57 putative Pol III transcribed tRNAs and snRNAs . Tlo1 was enriched at 1 , 617 ORFs in the C . dubliniensis genome ( 27% of all nuclear genes; Ringo Peak Score 0 . 8 ) . Due to recent discussions in the literature regarding artefacts in ChIP data [28]–[30] , we restricted detailed analysis to a group of 367 genes that exhibited highly significant Tlo1-enrichment ( Ringo peak score 0 . 9; Table S3 ) . The region of Tlo-enrichment was centred on the coding region with extension into 5′ and 3′ flanking non-coding regions ( Figure S9A ) . The level of Tlo-enrichment at the 5′ region was variable and was generally <100 bp in length , however extensions up to 1000 bp in length were observed ( Figure S9B ) . QRT-PCR analysis of DNA generated in replicate ChIP experiments confirmed the high levels of enrichment observed at PUT1 and ACT1 and low enrichment levels observed at non-enriched genes such as CLN1 and Cd36_51290 and an intergenic region on chromosome 5 ( bp 521680–521729 ) ( Figure 8B , 8C ) . Due to the repetitive nature of the repeat sequences in the MRS , it was not possible to accurately determine the level of enrichment in this region by QRT-PCR . Using fluorescence intensity data extracted from our gene expression microarrays , we compared the expression levels of the Tlo1-associated ORFs relative to non-Tlo1 associated ORFs . Tlo1-associated ORFs were found to exhibit higher expression levels ( average 1 . 8-fold ) in YEPD compared to those genes that are not occupied ( Figure S9C ) . However , a plot of enrichment score versus expression level did not identify a direct correlation between Tlo1 enrichment and expression . Importantly , the most highly enriched genes ( n = 367; Ringo peak score 0 . 9 ) covered a broad spectrum of expression levels , including genes expressed at high and low levels in YEPD ( Figure 9A ) . We analysed the GO terms associated with these Tlo1-enriched genes and identified significant numbers of highly expressed genes associated with the GO categories glycolysis ( n = 7; P = 0 . 003 ) and the TCA cycle ( n = 7; P = 0 . 048 ) ( Figure 9A ) . Manual inspection of the gene list also identified highly expressed genes involved in translation ( n = 5 ) and sulphur amino acid metabolism ( n = 4 ) . Tlo1-enriched genes that are poorly expressed in YEPD were associated with the GO categories glyoxylate cycle ( n = 3; P = 0 . 09 ) , GlcNac catabolic process ( n = 6 , P = 0 . 094 ) , amino acid catabolic process ( n = 10; P = 0 . 018 ) and hexose transport ( n = 9; P = 0 . 057 ) . Manual inspection also identified poorly expressed genes encoding nitrogen scavenging transporters ( e . g . FRP6 , MEP21 , MEP22 , DAL1 , UGA6 ) and genes involved in gluconeogenesis ( PCK1 and FBP1 ) . Many YEPD repressed , hypha-specific genes were also enriched including EED1 , RBT1 , HWP1 , HWP2 and IHD1 . These data show that Tlo1 is present at many highly expressed metabolic genes in addition to genes normally repressed in YEPD ( starvation , hyphal genes ) indicating a dual role in transcriptional activation and repression . In order to support this hypothesis , we analysed the transcription of these categories of Tlo1-enriched genes in our tlo1Δ/tlo2Δ mutant . This analysis found that the highly expressed genes involved in glycolysis and sulfur amino acid metabolism exhibited significantly reduced expression ( ANOVA; P<0 . 05 ) in the tlo1Δ/tlo2Δ mutant relative to wild type cells , indicating that Tlo1 occupancy is required for maintaining the high level of expression of these genes ( Figure 9B ) . The exceptions to this finding were the genes encoding enzymes of the TCA cycle , which although highly expressed in wild-type cells , exhibited increased expression in the tlo1Δ/tlo2Δ mutant . In addition , highly Tlo1-occupied , low expression genes exhibited increased expression in the tlo1Δ/tlo2Δ mutant , including genes involved in starvation responses , nitrogen scavenging mechanisms , N-acetyl glucosamine catabolism and the hyphal GPI-anchored proteins ( Figure 9B ) . These data indicate that Tlo1 and the Mediator complex are involved in both transcriptional activation and repression of different classes of genes . The fact that Tlo1-enriched genes exhibit regulation in the tlo1Δ/tlo2Δ mutant also provides support for hypothesis that Tlo proteins interact with these ORFs .
Despite the importance of Mediator in transcriptional regulation , few studies have investigated the role of Mediator in regulating virulence-associated characteristics in pathogenic eukaryotic microrganisms . The discovery of a widely dispersed telomeric gene family with homology to Med2 in the pathogenic yeast C . albicans has accelerated the level of interest in Mediator in this fungus . Published data suggest important roles for Mediator in regulating important biological processes in C . albicans , including filamentous growth ( Med3 ) , biofilm formation ( Med31 , Med20 ) and white-opaque switching ( multiple subunits ) [9] , [15] , [16] . Because functional analysis of the 15 TLO paralogs in C . albicans remains technically challenging , we turned to C . dubliniensis , which shares many characteristics with C . albicans but contains only two TLO orthologs , thereby providing an ideal genetic background in which to investigate the degree to which TLO gene function ( s ) have diverged between the two TLO paralogs . It has been hypothesized that in C . albicans , Mediator could form variant complexes that contain different Tlo/Med2 subunits that could be differentially recruited to activate or repress different subsets of genes in response to specific stimuli [9] , [12] . C . dubliniensis , which has only two Tlo proteins that appear to associate primarily with Mediator , provides an invaluable tool to understand first , how different TLO genes may affect Mediator function and second how the presence of multiple variant TLO genes contributes to virulence . In C . dubliniensis , tlo1Δ deletion mutants are defective in hyphal growth [14] . Here , we constructed a null tlo1Δ/tlo2Δ double mutant strain of C . dubliniensis . The tlo1Δ/tlo2Δ strain exhibits a wide range of phenotypic defects , indicating that they play a central role in global transcriptional control . Consistent with a role in regulating hyphal growth , deletion of both TLO genes reduced expression of many of the key regulators of morphogenesis including signal transducers ( RAS1 , RIM101 ) transcriptional regulators ( EFG1 , UME6 ) and kinases ( HGC1 ) , resulting in largely pseudohyphal growth phenotypes with reduced expression of key hyphal genes ( SOD5 , RBT5 , HWP1 ) . The tlo1Δ/tlo2Δ mutant also exhibited increased susceptibility to oxidative stress and reduced growth in the presence of alternative carbon sources . These data demonstrate that the TLOs of Candida spp . have evolved to regulate many key processes , including those important for virulence and growth in the host . In addition , the importance of Tlo in mediating transcriptional responses important for pathogenicity is shown in the similarity of our transcript profiles to those observed during infection of RHE and bone marrow-derived macrophages ( Figure S6 ) [25] , [26] . Indeed , a tlo1Δ/tlo2Δ mutant grown in YEPD mounts a transcriptional response akin to that observed following phagocytosis by macrophages [1] . Recent studies have also shown that Mediator is an important regulator of the Candida-macrophage interaction [31] . One key observation regarding the transcriptional changes observed in the tlo1Δ/tlo2Δ mutant was the alteration in the kinetics of induction of hypha-specific transcripts . Complementation of the tlo1Δ/tlo2Δ mutant with Tlo1 resulted in more rapid and higher levels of transcriptional induction of SOD5 , UME6 and GAL10 . Some induction of these transcripts could be observed in the tlo1Δ/tlo2Δ mutant , however the amplitude of the response was greatly retarded relative to the complemented strains . These data suggest that Tlo proteins play an important role in maximizing the speed and magnitude of transcriptional changes in response to environmental cues . This characteristic is likely to be of great importance in the host where rapid responses could be important for survival , e . g . following phagocytosis or translocation to a different part of the gastrointestinal tract . In S . cerevisiae , the Mediator tail regulates stress responses and the metabolism of carbohydrates and amino acids [23] , [24] , [27] . A direct comparison of the genes regulated in our tlo1Δ/tlo2Δ mutant and an S . cerevisiae med2Δ mutant supports this evolutionarily conserved role for Mediator tail in eliciting transcriptional responses to deal with changing environments [27] . In S . cerevisiae this effect has been shown to be centred on SAGA-regulated genes [24] . In this study , we also explored the relationship of Tlo proteins with other subunits of Mediator . A defect in filamentous growth was described by Zhang et al . in C . albicans following deletion of Med3 , encoding another Mediator tail subunit that interacts with Tlo proteins [9] . Biochemical analysis of the Mediator complex in the tlo1Δ/tlo2Δ null mutant and the C . dubliniensis med3Δ mutant revealed the absence of Med5 and Med16 in Mediator purified from both mutants , suggesting that the entire tail complex is destablilized by loss of Tlo or Med3 proteins . Consistent with this idea , the C . dubliniensis med3Δ and tlo1Δ/tlo2Δ null mutants exhibited remarkably similar phenotypes and transcriptional profiles when cultured in YEPD . However , in response to hyphal stimulating conditions , many differences in transcriptional responses could be observed in the tlo1Δ/tlo2Δ strain and the med3Δ mutant strain ( Figure 6 ) , suggesting that Mediator complexes with different tail substituents may exist in vivo in response to specific environmental conditions . Alternatively , the deletion of either Med3 or Tlo protein may release the remaining Tail module Mediator subunits to form a sub-complex that can interact with cellular components independently of the remainder of the complex . We next examined the relationship of Tlo with the Mediator middle subunit , Med31 , described by Uwamahoro et al . [15] . The C . dubliniensis tlo1Δ/tlo2Δ null mutant exhibited a defect in cell separation and CHT3 expression , which is consistent with the defect in cytokinesis described in the C . albicans med31 mutant by Uwamahoro et al . [15] . These data support previous studies that suggest that regulation of cytokinesis and possibly Ace2-regulated genes is a conserved function of mediator in yeast [15] , [32] . However , a med31Δ mutant was still capable of true hyphal growth in liquid medium whereas the tlo1Δ/tlo2Δ null mutant was completely unable to form true hyphae under the same conditions . The most striking difference in phenotype was the increased ability of the C . dubliniensis tlo1Δ/tlo2Δ mutant to form biofilm . The C . albicans med31Δ mutant exhibits reduced biofilm growth relative to wild-type and decreased expression of the biofilm regulator TEC1 and the adhesin ALS1 ( Figure 7A ) . The C . dubliniensis tlo1Δ/tlo2Δ null mutant exhibited increased expression of many med31Δ downregulated genes , including TEC1 and ALS1 , although the roles of these genes in biofilm formation in C . dubliniensis have yet to be confirmed . These data illustrate the very different and possibly opposing roles that mediator tail and middle subunits may have in transcriptional regulation in Candida spp . Our ChIP analysis of Tlo1 for the first time indicates the level of Tlo and Mediator occupancy across the Candida genome . Although Mediator tail subunits are unlikely to interact directly with DNA , they associate closely with DNA-bound transcription factors and histones and have been successfully isolated in association with DNA in other yeasts [8] , [33] . We identified extensive Tlo1 binding across all C . dubliniensis chromosomes . Tlo1 was closely associated with Pol II transcribed ORFs as well as the telomeres and the MRS . Binding of Mediator to telomeres and sub-telomeric genes has been described in S . cerevisiae and Mediator has been shown to have an additional role in heterochromatin maintenance [33] , [34] . Our data suggests that Mediator could play a similar role in Candida spp . The significance of Tlo1-enrichment at the MRS is more difficult to explain as the function of this repeat element is unknown . Further studies will be required to verify this interaction . In order to limit the numbers of possible ChIP artefacts , often associated with highly expressed genes , we have used a very stringent cut off to define highly enriched genes . This Tlo1-enriched gene set includes repressed and moderately expressed genes , supporting the assumption that our data set is not biased for highly expressed ORFs . In addition , the highly expressed genes identified in our ChIP analysis are regulated following deletion of Tlo , supporting a direct , functional interaction with these ORFs . The association of Mediator subunits with the coding regions of Pol II transcribed ORFs has been observed in Mediator ChIP localization experiments in other yeasts and suggests that Tlo1 may be involved in transcript elongation or in chromatin interactions at coding regions [35] , [36] . We observed that although Tlo1-occupied genes on average were expressed higher than non-occupied genes ( average 1 . 8-fold ) , Tlo1 was associated with ORFs having a range of expression levels . To further investigate the significance of Tlo1 ORF occupancy and Tlo1-dependent gene expression , we compared the regulation of specific classes of Tlo1 occupied genes in our tlo1Δ/tlo2Δ mutant . Highly expressed genes encoding enzymes involved in amino acid synthesis , glycolysis and translation required Tlo1 to maintain high levels of expression . In contrast , genes that exhibited low levels of expression under the conditions analysed were repressed by Tlo1 . These data suggest a dual role for Tlo1 in mediating transcriptional activation and repression . Specifically , Tlo1 was required to maintain transcription of genes involved in glycolysis and translation and to repress starvation responses ( glyoxylate cycle , gluconeogenesis ) and several hypha-specific genes . Localisation of Mediator to both expressed and repressed genes has been observed in S . cerevisiae and the ability of the complex to carry out opposing regulatory functions is likely due to its ability to adopt different modular conformations [36] . Specifically , the kinase module in S . cerevisiae , consisting of Cdk8 , Cyclin C , Srb8 and Srb9 has been shown to associate with Mediator to repress transcription [36] . A specific role for the kinase module in transcriptional repression has yet to be determined in Candida spp . The C . dubliniensis genome harbors two TLO orthologs , two alleles of TLO1 and one allele of TLO2 . One of the central goals of our study was to determine whether different TLO genes could regulate different processes . To compare the effects of single copies of TLO1 and TLO2 , we complemented the tlo1Δ/tlo2Δ mutant independently with either TLO1 or TLO2 . Each of the TLO genes complemented tlo1Δ/tlo2Δ mutant phenotypes , albeit to different degrees; TLO1 was better at restoring true hyphal growth and growth in galactose . TLO2 supressed biofilm growth to a greater extent than TLO1 . With regard to TLO2 , it should be noted that the level of expression of the reintegrated allele was significantly higher than the wild-type allele , which may have resulted in higher levels of Tlo2 activity . However , despite this , the reintegrated TLO2 allele did not restore filamentous growth to the same extent as wild-type TLO1 . Support for these functional differences was obtained when the transcript profiles of the TLO1 and TLO2 complemented strains were compared . It was observed that TLO1 could restore GAL gene induction to a greater extent than TLO2 . During hyphal growth , TLO1 also restored expression of UME6 and SOD5 to a greater degree than TLO2 . In contrast , during growth in YEPD TLO2 was required for repressing transcription of aberrantly expressed hyphal genes such as IHD1 , SAP7 and EED1 and the negative regulators of hyphal growth SFL1 and NRG1 . TLO2 was also required for suppression of many starvation induced genes involved GlcNac metabolism ( NAG3 , NAG4 ) and amino acid metabolism ( CHA1 , GDH3 ) . The data suggest that the two TLO paralogs in C . dubliniensis have evolved to regulate overlapping as well as distinct subsets of genes . Under standard growth conditions , both TLO1 and TLO2 are expressed in C . dubliniensis , suggesting that two distinct pools of Mediator exist at any one time , with the ability to regulate different subsets of genes . The implications of our study for C . albicans biology , where there are potentially 15 different Tlo-Mediator complex varieties with differing transcriptional activating activities , are immense . It is tempting to speculate that this variety in Mediator activity , or a varying pool of non-Mediator associated Tlo protein , could contribute to the phenotypic plasticity and adaptability of this fungus and may go towards explaining why its incidence as a commensal and opportunist is far greater compared to its close relatives C . tropicalis and C . dubliniensis which do not have this expanded repertoire of Med2 paralogs . Over the last decade , a wealth of studies have described the global transcriptional responses of C . albicans to environmental stress , pH changes and nutrient availability amongst others . It is clear from these studies that one of the characteristics of C . albicans is its ability to rapidly respond to environmental change by mediating rapid transcriptional responses . The current study suggests a vital role for the Tlos in mediating the speed and scale of these responses , which are associated with characteristics required for commensalism ( nutrient acquisition , metabolism ) and pathogenicity ( starvation responses , filamentation ) indicating a key role for Tlo-regulated responses in the lifestyle of C . albicans . This , coupled with the potential diversity in the activity of the different Tlo paralogs may have contributed to the ability of C . albicans to colonise many diverse niches and thus to evolve to be a highly successful commensal and pathogen .
All Candida strains were routinely cultured on yeast extract-peptone-dextrose ( YEPD ) agar , at 37°C . Solid Lee's medium and Spider medium were used as described previously [37] , [38] . Pal's agar medium was also used for chlamydospore induction as described previously [39] . For liquid culture , cells were grown shaking ( 200 rpm ) in YEPD broth at 30°C or 37°C . In order to determine the doubling time of each strain , the optical densities ( 600nm ) of cultures in the exponential phase of growth were plotted and analysed using the exponential growth equation function in Graphpad Prism ( GraphPad , CA , USA ) . Doubling times and r2 values in Table 1 were calculated from three replicate growth curves . Glucose ( 2% w/v ) was substituted with galactose ( 2% w/v ) where indicated . Hyphal induction was carried out in sterile Milli-Q H2O supplemented with 10% ( v/v ) fetal calf serum at 37°C . Samples were randomized and the proportion of germ-tubes or true-hyphae in 300 cells was assessed at intervals by microscopic examination of an aliquot of culture with a Nikon Eclipse 600 microscope ( Nikon U . K . , Surrey , U . K . ) . Experiments were performed on at least three separate occasions . Genotypes of strains used in this study are listed in Table S4 . Gene disruption of TLO1 ( Cd36_72860 ) and TLO2 ( Cd36_35580 ) was achieved through use of the SAT1 flipper cassette system [40] . Deletion constructs were created by PCR amplifying the 5′ flanking regions of TLO1 or TLO2 with the primer pairs CTA21KF/CTA1X and CTA22KF/CTA2X respectively and the 3′ flanking regions with the primer pairs CTA1S/CTA21SIR and CTA2S/CTA22SIR , respectively ( Table S4 ) . Ligation of these products in the corresponding restriction sites in plasmid pSFS2A resulted in the construction of plasmids pTY101 ( TLO1 ) and TY102 ( TLO2 ) as described [19] . The deletion construct was used to transform C . dubliniensis Wü284 as described previously [19] and deletion of genes was confirmed by Southern blotting ( Figure S1 ) . A single transformation with TY102 was sufficient to delete TLO2 in strain Wü284 which was found to have a truncation in one copy of ChrR , resulting in the presence of only one copy of TLO2 . Deletion of MED3 was carried out using a primer tailing method with the primers MED3M13F/MED3M13R as described [41] . Reintroduction of wild-type TLO genes or MED3 was achieved by PCR amplification of the entire ORF plus their upstream and downstream regulatory sequences using primer pairs CdTLO1FP/CdTLO1RP for CdTLO1 , CdTLO2FP/CdTLO2RP for CdTLO2 and MED3FP/MED3RP for MED3 ( Table S5 ) and ligation of these into the C . dubliniensis integrating vector pCDRI to yield plasmids pCdTLO1 , pCdTLO2 , and pCdMED3 respectively . These plasmids were transformed as described previously [19] . Mediator was purified from C . dubliniensis strains containing Mediator subunits tagged at their C-terminus with either HA or 6His-FLAG . CdMED8- and CdTLO1-tagging cassettes were amplified from pFA-3HA-SAT1 or pFA-6His3Flag-SAT1 using the primer pairs pZL420/pZL421 and pZL422/pZL423 , respectively , as described previously [9] . Whole-cell extracts were made from strains that were untagged or had a single copy of Tlo1 or other Mediator subunit tagged with a HA or 6His-FLAG and subjected to multiple chromataographic separations as described previously [9] . The elutions from the immobilized metal affinity chromatography ( IMAC; Talon Kit , Clontech Laboratories , CA , USA ) step were analyzed by SDS-PAGE as indicated and proteins were revealed by staining with silver as described previously [9] . Biofilm mass was determined using an XTT reduction assay to assess metabolic activity . Prior experiments with planktonic cells indicated that wild-type and tloΔ mutant strains exhibited similar rates of XTT reduction . To induce biofilm growth , a suspension of 1×106 cells of each strain was prepared in 10% ( v/v ) foetal calf serum from 18 h cultures grown in YEPD broth at 30°C with shaking at 200 rpm A 100 µl volume of each suspension was added to triplicate wells of a 96-well , flat-bottomed polystyrene plate ( Greiner BioOne ) with lid . Plates were then incubated for 24 h or 48 h in a static incubator ( Gallenkamp ) set to 37°C . Growth in the presence of 5% ( v/v ) CO2 was carried out in a static tissue culture incubator with 5% ( v/v ) relative humidity ( Gallenkamp ) . All wells were then washed 5 times with 200 µl sterile PBS to remove non-adherent cells . After the final wash , 200 µl of 200 µg/ml XTT supplemented with 50 µg/ml CoEnzyme Q ( Sigma-Aldrich ) was added to each well and incubated at 37°C in the dark for 50 min . A 100 µl aliquot of this suspension was transferred to a fresh plate and the absorbance measured at 480 nm using a Tecan Plate Reader system ( Tecan ) . Results were analysed and graphed using Microsoft Excel ( Microsoft ) . The C . dubliniensis whole genome oligonucleotide microarray used in this study was previously described [20] . RNA was extracted from cells grown to an OD 600 nm of 0 . 8 in YEPD broth at 37°C or from cells ( 1×106 ) inoculated in 10% ( v/v ) fetal calf serum at 37°C . Cell pellets were snap frozen in liquid N2 and disrupted using the Mikro-Dismembrator S system ( Sartorius Stedim Biotech , Göttingen , Germany ) . RNA was prepared using the Qiagen RNeasy mini-kit . A 200 ng aliquot of total RNA was labelled with Cy5 or Cy3 using the Two-Color Low Input Quick Amp labeling Kit ( Agilent Technologies ) according to the manufacturer's instructions . Array hybridization , washing , scanning and data extraction was carried out in GenePix Pro 6 . 1 ( Axon ) as described [20] . For each condition , four biological replicate experiments were performed , including two dye swap experiments . Raw data were exported to GeneSpring GX12 and signals for each replicate spot were background corrected and normalized using Lowess transformation . Log2 fluorescence ratios were generated for each replicate spot and averaged . Genes differentially expressed ( 1 . 5-fold ) relative to wild-type were identified from those that passed a t-test ( P≤0 . 05 ) and satisfied a post-hoc test ( Storey with Bootstrapping ) with a corrected Q value ≤0 . 05 . Hierarchical clustering was used to compare gene expression in each condition using the default settings in Genespring GX12 . The Gene Set Enrichment Analysis ( GSEA ) PreRanked tool ( available to download at www . broadinstitute . org/gsea/index . jsp ) was used to investigate whether our data sets were enriched for particular genes present in published data sets [42] . This analysis required the use of a database of publicly available genome-wide data sets , constructed by Andre Nantel ( National Research Council of Canada , Montreal ) , that can be downloaded from the Candida Genome Database ( CGD ) . This database included 171 lists of up- and down-regulated genes from microarray experiments , in vivo promoter targets derived from ChIP-chip experiments performed on 36 transcription factors , members of 3 , 601 Gene Ontology ( GO ) term categories , and 152 pathways , as curated by CGD , amongst others , and is fully described by Sellam et al . [43] . Genes in our expression data sets were first ‘ranked’ based on Log2 values from highest to lowest . The GSEA PreRanked tool was then used to determine if particular gene sets in the database were enriched towards the top or bottom of this ranked list . Enrichment plots show the level of enrichment towards the top ( red ) or bottom ( blue ) of the ranked list . The normalized enrichment score ( NES ) indicates the level of enrichment at the top ( positive NES ) or bottom ( negative NES ) of the ranked list and is supported by a p value and a False Discovery Rate ( FDR ) value to indicate the likelihood of false positive results . Microarray data have been submitted to Gene Expression Omnibus ( GEO ) , accession number: GSE59113 . RNA for QRT-PCR was isolated using the RNeasy Mini-kit ( Qiagen ) . Cells were disrupted using a FastPrep bead beater ( Bio101 ) . RNA samples were rendered DNA free by incubation with Turbo-DNA free reagent ( Ambion , Austin , TX ) . cDNA synthesis was carried out using the Superscript III First strand synthesis sytem for RT-PCR ( Life technologies ) as described by Moran et al . [19] Reactions were carried on an ABI7500 Sequence Detector ( Applied Biosystems , Foster City , CA ) using MicroAmp Fast Optical 96-well Reaction plates ( Applied Biosystems ) in 20 µl reactions using 1X Fast SYBR Green PCR Master Mix ( Applied Biosystems ) , 150 nM of each oligonucleotide ( Table S4 ) and 2 µl of diluted template ( 10 ng ) . Cycling conditions used were 95°C for 20 sec , followed by 40 cycles of 95°C for 3 sec and 60°C for 30 sec , the latter of which was the point of detection of fluorescence . This was followed by a melt curve stage as a quality control point . Gene expression levels were normalized against the expression levels of the constitutively expressed ACT1 gene in the same cDNA sample . Gene specific primers are shown in Table S5 . Each gene-specific set was shown to amplify at a similar efficiency ( within 10% ) to the control ACT1 primer set in primer optimization experiments . C . dubliniensis strain Tlo1-HA , derived from strain Wü284 , was grown at 30°C degrees and harvested at an OD 600 nm of 2 . 0 . A secondary cross-linking strategy was employed to fix chromatin that involved initial treatment of cells with dimethyl adipimidate ( 10 mM ) for 45 min with agitation at room temperature followed by cross-linking with 1% formaldehyde . Cells were washed twice in PBS prior to spheroplasting in 5 ml of spheroplasting buffer ( 1 . 2 M sorbitol , 20 mM 4- ( 2-hydroxyethyl ) -1-piperazineethanesulfonic acid [HEPES] pH 7 . 4 ) containing 0 . 4 mg/ml Zymolyase 100T ( Seikagaku Corp . , Japan ) and incubated for 2 . 5 h 30°C with rotation . DNA isolation and fragmentation was carried out as described in Ketel et al [44] using a Branson sonifier to yield DNA fragments of 500–600 bp . Chromatin immunoprecipitation ( ChIP ) was carried out as described by Ketel et al . [44] using an anti-HA mouse monoclonal antibody ( clone 12CA5 ) which was incubated overnight at 4°C on a rotating wheel . The following day , 70 µl of Protein A agarose beads ( Santa Cruz Biotech , California , USA ) were added and mixed at 4°C for 4 h . DNA was recovered as described by Ketel et al [44] and resuspended in 80 µl TE buffer for downstream applications . Microarray chip analysis ( CGH Microarray ) was carried out as described in the Agilent Yeast ChIP-on-chip analysis protocol handbook ( Version 9 . 2 , 2007 ) . Competitive hybridization was carried out on a whole genome C . dubliniensis oligonucleotide microarray consisting of 175 , 758 unique 60mer probes , spaced at 20 bp intervals ( Agilent Technologies ) . Experiments were performed with triplicate biological replicate samples . The IP samples were labelled with Cy3 and input samples ( total lysate control ) were labelled with Cy5 . One sample was dye swapped . Array hybridisation and washing were carried out using standard Agilent Technologies CGH hybridisation buffers and washes . Scanning of the array was carried out on an Agilent G2565B rotating multi-slide hyb-scanner ( Agilent Technologies ) using a 5 µm resolution and PMT settings at 100% for both channels ( Green/Red ) . The scan area was set to 61×21 . 6 mm . The raw images were processed using Agilent Feature Extraction software suite v 10 . 5 . 1 . 1 . and feature extraction was automatically carried out using the ‘CGH automatic’ programme to align and normalise fluorescent probe spots on the array . Data were read into the Bioconductor [45] package Ringo [46] and pre-processed with the “nimblegen” method . This was followed by merging of replicates and smoothing with a window half size of 500 bp . ChIP-enriched regions were calculated with an enrichment threshold based firstly on a 0 . 8 percentile , followed by a more stringent 0 . 9 percentile which was used for the analysis presented here . Genes were assigned to an enrichment region if they overlapped up to 500 bp away from the gene boundaries . For visualisation of the data a JBrowse [47] instance was set up on http://bioinf . gen . tcd . ie/jbrowse/ ? data=cdub . The ChIP data have been submitted to GEO , accession number GSE60173 . | Candida albicans and C . dubliniensis are fungal pathogens of humans . Both species possess TLO genes encoding proteins with homology to the Med2 subunit of Mediator . The more virulent pathogen C . albicans has 15 copies of the TLO gene whereas the less pathogenic species C . dubliniensis has only two ( TLO1 and TLO2 ) . In this study we show that a C . dubliniensis mutant missing both TLO1 and TLO2 is defective in virulence functions , including hyphal growth and stress responses but forms increased levels of biofilm . Analysis of gene expression in the tlo1Δ/tlo2Δ mutant revealed extensive differences relative to wild-type cells , including aberrant expression of starvation responses in nutrient-rich medium and retarded expression of hypha-induced transcripts in serum . Tlo1 protein was found to interact with genes and this was associated with both gene activation and repression . TLO1 was found to be better at restoring hyphal growth compared to TLO2 and but was less effective than TLO2 in supressing biofilm formation in the tlo1Δ/tlo2Δ strain . Thus , Tlo proteins regulate many virulence properties in Candida spp . and the expansion of the TLO family in C . albicans may account for the increased adaptability of this species relative to other Candida species . | [
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] | 2014 | Telomeric ORFs (TLOs) in Candida spp. Encode Mediator Subunits That Regulate Distinct Virulence Traits |
Mammary gland branching morphogenesis and ductal homeostasis relies on mammary stem cell function for the maintenance of basal and luminal cell compartments . The mechanisms of transcriptional regulation of the basal cell compartment are currently unknown . We explored these mechanisms in the basal cell compartment and identified the Co-factor of LIM domains ( CLIM/LDB/NLI ) as a transcriptional regulator that maintains these cells . Clims act within the basal cell compartment to promote branching morphogenesis by maintaining the number and proliferative potential of basal mammary epithelial stem cells . Clim2 , in a complex with LMO4 , supports mammary stem cells by directly targeting the Fgfr2 promoter in basal cells to increase its expression . Strikingly , Clims also coordinate basal-specific transcriptional programs to preserve luminal cell identity . These basal-derived cues inhibit epidermis-like differentiation of the luminal cell compartment and enhance the expression of luminal cell-specific oncogenes ErbB2 and ErbB3 . Consistently , basal-expressed Clims promote the initiation and progression of breast cancer in the MMTV-PyMT tumor model , and the Clim-regulated branching morphogenesis gene network is a prognostic indicator of poor breast cancer outcome in humans .
Mouse mammary gland morphogenesis begins during mid-gestation with the development of two bilateral epithelial ridges along the ventral epidermis that form invasive , multipotent stem cell-enriched placodes migrating into the underlying mesenchyme , later branching to form a rudimentary ductal tree by birth . The structure remains relatively quiescent until hormonal stimuli at puberty initiate the formation of stem cell-enriched terminal end buds ( TEBs ) that rapidly proliferate and invade into the mammary fat pad , frequently bifurcating to generate a ductal network . The resulting ducts consist of luminal epithelial cells surrounded by a basal myoepithelial cell layer . Basal and luminal cells communicate signals to each other through paracrine and direct cell-cell interactions to regulate proper morphogenesis [1] , [2]; the nature of these complex cell-cell interactions remains to be fully defined . Mammary stem cells ( MaSCs ) coordinate ductal morphogenesis and homeostasis of the luminal and basal cell compartments in the adult mammary gland . Two models have been proposed for the function of MaSCs in the mammary gland: either committed unipotent luminal and basal epithelial stem cells maintain their respective compartment [3] , or bipotent MaSCs in the basal cell compartment give rise to both lineages [4] . CD49fHiCD29HiCD24+ basal epithelial cells maintain a small population of basal stem cells ( BSCs ) with the potential to regenerate a functional mammary gland [5] , [6] , while luminal stem cells ( LSCs ) , enriched in the CD49fL°CD29L°CD24+ luminal epithelial cell population , maintain unipotent potential to preserve the luminal cell population [3] , [4] and cannot regenerate the mammary gland . Transcription factors that control the maintenance of stem cells and lineage specification along the mammary epithelial cell ( MEC ) hierarchy are best characterized in the luminal cell compartment [7] . However , the current knowledge of transcriptional regulation of BSCs and their differentiation is limited . The LIM domain , a tandem zinc finger motif that serves as an interface for protein-protein interactions , is found in a variety protein families , including the LIM-homeodomain ( Lhx ) and LIM-only ( LMO ) transcription factors [8] . Originally discovered as a co-activator of Lhx and LMO [9]–[12] , the co-factor of LIM domains ( CLIM/LDB/NLI ) coordinates the organization of transcriptional complexes through two key regulatory domains: the amino-terminal homodimerization domain and the carboxy-terminal LIM-interacting domain ( LID ) [9] , [13] , [14] . Additionally , Clims can associate with other DNA-binding proteins , including GATA , bHLH , and Otx family members [15] , [16] . It is through these higher order transcriptional complexes that Clims mediate enhancer-promoter interactions to influence gene regulation [17]–[19] . The Clim family consists of two highly conserved members: the ubiquitously expressed Clim2 ( Ldb1/Nli ) , and the more regionally expressed Clim1 ( Ldb2 ) [10] . Germline deletion of Clim2 in the mouse causes embryonic lethality by day 9 . 5 [20] , while Clim1 knockout mice display no developmental defects ( as indicated by the Mouse Genome Informatics website ) possibly due to compensatory effects by Clim2 . The function of Clims in mammary gland development has not been investigated . However , in breast cancer , Clim expression correlates with estrogen receptor ( ER ) positivity where Clims are involved in coordinating transcriptional networks through ERα [21] . Because of the lethality of germline Clim2 deletion and the possible overlap in function between Clim1 and Clim2 , we investigated the role of Clims in the mammary gland using the Keratin 14 ( K14 ) promoter to express a dominant-negative Clim ( DN-Clim ) , consisting of the highly conserved LID fused to a nuclear localization signal and Myc-tag [22] , directing its expression to basal MECs . The DN-Clim molecule , which targets all Clim family members , interacts with LIM domain proteins through the LID , but lacks the dimerization domain essential for coordinating enhancer-promoter interactions [18] , thereby inhibiting Clim-mediated transcriptional regulation . Using the K14-DN-Clim mouse model we discovered novel transcriptional regulatory programs coordinated by Clims in the basal cells of the mammary gland that promote branching morphogenesis through the maintenance of BSCs , in addition to controlling basal cell-derived transcriptional programs that preserve luminal cell identity . Transcriptional networks within the stem cell-enriched TEB and differentiated duct correlate with human breast cancer gene expression and patient outcome in an opposing fashion; a significant proportion of these networks are regulated by Clims . Furthermore , Clim-mediated transcriptional machinery in the basal cell population permits tumor initiation and progression in the MMTV-Polyoma Middle T ( PyMT ) breast cancer model . Altogether , these data describe novel mechanisms of transcriptional regulation in BSCs and their role in breast cancer .
Among the two Clim family members , Clim2 is preferentially expressed throughout mammary development ( Figure 1A ) , localized to basal and luminal MECs ( Figure 1B–C , S1A ) . To study the role of Clims in MECs we used the K14-DN-Clim mouse model , targeting DN-Clim expression to the K14-positive basal MECs , as indicated by Myc-tagged DN-Clim expression ( Figure 1D ) . DN-Clim is only expressed in basal cells of ducts and TEB cap cells with rare expression in the TEB body cells , consistent with K14 expression in a subset of these cells [23] . Abnormalities in branching morphogenesis of DN-Clim mammary glands appear as early as 4 weeks of age and continue through adulthood and late pregnancy ( Figure 1E ) , featuring delayed ductal elongation , reduced branching frequency , and smaller TEBs ( Figure 1F–H ) . While the delayed ductal penetration dissipates by early adulthood , the reduced branching frequency and smaller TEBs persist throughout development . Apart from the reduction in size , the TEBs of DN-Clim mammary glands appear to be morphologically normal . While branching morphogenesis is typically completed by week 10 , as denoted by the absence of TEBs , small DN-Clim TEBs persist beyond 10 weeks of age ( Figure 1H ) . Collectively , these data demonstrate an important role for basal cell-expressed Clims in mammary gland branching morphogenesis through enhancement of ductal penetration , branching frequency , and TEB size . During pregnancy , DN-Clim mammary glands at day 14 . 5 and 17 . 5 exhibit reduced epithelial density ( Figure 1E and 1I ) ; however , this could be a result of the low ductal density prior to pregnancy , rather than defective side branching and alveologenesis . Additionally , while DN-Clim females produce litters comparable in size to normal mice , they can only support a limited number of pups beyond postnatal day 2 ( Figure S1B ) , likely due to decreased milk availability from the underdeveloped mammary epithelial tree , as the surviving pups from DN-Clim females grow at a normal rate ( Figure S1C ) and wild type lactating females can support full litters derived from DN-Clim females . Thus , Clims are required for a fully functional lactating mammary gland . To determine the cellular mechanism for impaired mammary gland development in DN-Clim mice we measured bromodeoxyuridine ( BrdU ) incorporation in proliferating cells , observing significant decreases in BrdU+ cells in DN-Clim TEB and ducts ( Figure 1J ) . Consistent with the expression pattern of DN-Clim in TEB cap cells , we observed the most drastic reduction of proliferation in the cap cells , while the body cells exhibit only a moderate , yet significant , decrease in proliferation . The fact that TEB body cells , which rarely express DN-Clim , have reduced proliferative potential suggests a non-autonomous mechanism of Clim-mediated cell proliferation through signals that originate from the cap cells . Thus , Clims promote proliferation in both the TEB and duct during branching morphogenesis . The current knowledge of the molecular mechanisms that regulate branching morphogenesis is limited , especially regarding the transcriptional programs within the stem cell-enriched TEB and differentiated duct structures . To elucidate these transcriptional programs , and specifically those controlled by Clims , we profiled gene expression in TEB and duct cells from wild type ( WT ) and DN-Clim mice , using laser capture microdissection to isolate these structures from four , six , eight , and ten week old mice . Consistent with its preferential expression during branching morphogenesis , Clim2 was expressed higher than Clim1 in both the TEB and duct cells ( Figure S2A ) . To define developmentally regulated genes over the time course we applied the Bayesian Estimation of Temporal Regulation ( BETR ) algorithm [24] identifying 8 , 030 and 6 , 488 probe sets ( 7 , 318 and 5 , 870 genes ) temporally regulated in TEB and duct cells , respectively , with 1 , 313 TEB and 815 duct genes changing 1 . 5-fold or greater in two or more time points ( Figure 2A–C , Dataset S1 and S2 ) . TEB genes are associated with proliferation , while the duct genes are associated with differentiation ( Figure 2D–E ) . Because the TEB represents a stem cell-enriched population [25] , and because of our direct comparison of TEB and duct cells , these gene signatures are representative of mammary stem cell and differentiated MECs , respectively . The hyperproliferative and invasive nature of the TEB bears similarity to molecular properties observed in aggressive breast and other adenocarcinomas . In addition , poorly differentiated basal-like tumors are more aggressive and possess stem cell-like transcriptional characteristics compared to the differentiated luminal subtypes [26] . This prompted us to determine whether the less differentiated TEB and fully differentiated duct gene signatures can be used as predictors of prognosis in human breast cancer . Utilizing three data sets that profile the transcriptome and classify the intrinsic subtypes of primary human breast tumors [27]–[29] we found the TEB signature is highly expressed in the poorly differentiated claudin-low and basal-like breast cancer subtypes , while the duct signature is more highly expressed in the differentiated luminal subtypes ( Figure 2F ) . High TEB signature expression predicted poor overall survival in breast cancer patients ( Figure 2G ) . An opposite trend was observed with the duct signature ( Figure 2H ) , suggesting the expression of differentiation genes suppresses tumorigenesis . These trends are also observed within the different subtypes of breast cancer ( Figure 2I ) . Even after removal of proliferation genes , which significantly contribute to prognosis prediction [30] , the TEB signature retains prognostic relevance , albeit with reduced power ( Figure 2G ) . The duct signature , which is poorly enriched for proliferation genes , was relatively unaffected by removal of these genes ( Figure 2H ) . Thus , these developmental signatures hold prognostic value in breast cancer and may be useful to identify novel genes of interest for understanding the biology of breast cancer in relationship to normal development . To determine genes regulated by Clims during branching morphogenesis we used the CyberT algorithm [31] to define differentially expressed genes ( DEGs ) in the DN-Clim TEB and duct: 66 TEB and 137 duct genes were significantly differentially expressed in at least two time points ( Figure 3A–B , Dataset S3 and S4 ) , a sizable proportion of which are differentially expressed in both TEB and duct cells ( Figure S2B ) . Each DEG set is significantly enriched with genes from their respective developmental signature ( Figure S2C–D ) , indicating an important role for Clims in regulating branching morphogenesis . Using the Molecular Signature Database [32] to characterize the biological properties of the Clim-regulated TEB and duct gene sets revealed that both have functions in mammary stem cells , pubertal mammary gland development , several breast cancer subtypes , and Wnt-signaling in the mammary gland ( Figure 3C–D ) . Additionally , TEB DEGs participate in metallopeptidase activity and possess serum response factor ( SRF ) and androgen receptor ( AR ) transcription factor binding motifs , while duct DEGs are enriched with smooth muscle and mammary basal epithelial cell genes , participate in FGF signaling , and possess TCF3 and SOX9 transcription factor binding motifs . Furthermore , high expression of Clim-regulated genes in breast cancer is an indicator of poor prognosis ( Figure 3E ) . Collectively , these data support the involvement of Clims in gene expression networks important for both stem cell function and breast cancer . To determine genes differentially expressed in basal and luminal cell compartments we sorted MECs into Lin−CD29HiCD24+ and Lin−CD29L°CD24+ populations , respectively ( Figure S3A–D ) . Basal and luminal cell purity from WT and DN-Clim MECs was confirmed by qPCR for K8 , K14 , and the K14-DN-Clim transgene expression ( Figure S3E–G ) ; as expected , the K14-DN-Clim transgene is only expressed in basal cells , and not luminal cells . Basal markers ( K5 , K14 , and smooth muscle actin ) and luminal markers ( K8 , K18 , Gata3 , estrogen and progesterone receptors ) are significantly overexpressed ( CyberT , p-value<0 . 001 ) in their respective cell types , indicating effective basal and luminal cell separation ( Dataset S5 ) . DN-Clim basal and luminal cells differentially express 422 ( 221 up and 201 down ) and 227 ( 139 up and 88 down ) genes with at least 1 . 5-fold change , respectively ( Dataset S6 and S7 ) , indicating both cell and non-cell autonomous mechanisms of gene regulation coordinated by Clims; a significant proportion of basal- and luminal-specific DEGs overlap with the DEGs identified in the TEB and duct populations ( Figure S3H–I ) . Basal-specific DEGs participate in mammary gland morphogenesis processes , including cell proliferation , adhesion , and stem cell properties ( Figure 3F and S3J ) . Interestingly , DEGs in the luminal cell population are enriched with keratinization and squamous epithelial , specifically esophageal and epidermal , differentiation genes ( Figure 3G and S3K ) , suggesting a transepidermal differentiation of the luminal cells in the DN-Clim mammary gland . Thus , Clims coordinate gene expression in the basal cell compartment that ultimately help maintain the identity of luminal cells . Several genes and pathways related to mammary gland stem cell biology are differentially expressed in the DN-Clim mammary gland throughout the developmental time course and within the basal and luminal cell compartments ( Figure 3C–D , and S3J–K ) . Ontology analyses revealed involvement of Clim-regulated genes in mammary stem cells and the Wnt signaling pathway . Lgr5 , a marker of stem cells in CD29HiCD24+ basal cells [33] , is consistently downregulated in basal cells and both the TEB and duct ( Dataset S3 , S4 , and S6 ) . Additional Clim-regulated genes that have known roles in maintenance of the stem cell enriched TEB and branching morphogenesis are the Fgfr2 gene [34] , [35] and the luminal cell-specific ErbB2 and ErbB3 genes ( Figure 3G and S4 ) [36] , [37] . Given this data , we hypothesized that Clims promote branching morphogenesis through gene regulation within stem cells of the basal MEC compartment . The reduced size of the stem cell-enriched TEB in DN-Clim mice and the enrichment of mammary gland stem cell genes in our DEG sets , along with previous evidence that Clim2 affects stem cells in the hair follicle [22] , prompted us to examine the stem cell populations in the DN-Clim mammary gland . Quantification of the basal and luminal MEC populations revealed a reduction of the CD29HiCD24+ BSC-enriched population in the DN-Clim mammary gland ( Figure 4A ) , while the proportion of the CD29L°CD24+ LSC-enriched population remains unchanged . No differences were observed in the luminal progenitor cell population when using CD61 as a marker for these cells ( Figure S5A ) [38] . Limiting dilution transplant analysis with CD29HiCD24+ cells revealed a nearly complete ablation of DN-Clim mammary repopulating units ( MRU ) ( Table 1 , Figure 4B ) ; only two DN-Clim transplants resulted in mammary outgrowths , both of which developed poorly structured mammary trees ( Figure S5B ) . Thus , Clims are of paramount importance in the maintenance of BSCs . Mammary colony forming cell ( MaCFC ) and mammosphere functional assays confirmed the depleted basal stem/progenitor cell population . Unsorted Lin− bulk and sorted CD29HiCD24+Lin− DN-Clim primary epithelial cells have fewer MaCFCs and form smaller colonies , while a slight and insignificant decrease in MaCFCs is observed in the sorted CD29L°CD24+Lin− population ( Figure 4C–D ) . Consistently , Lin− bulk and CD29HiCD24+Lin− sorted DN-Clim primary mouse MECs grown in suspension produced significantly fewer and smaller mammospheres ( Figure 4E–F ) , while CD29L°CD24+Lin− cells exhibit comparable sphere forming efficiency to WT cells . Serial passaging of the mammospheres every seven days resulted in enhanced depletion rates of Lin− bulk and CD29HiCD24+Lin− DN-Clim stem/progenitor cells . Collectively , these results suggest Clims maintain the number and proliferative potential of the basal stem/progenitor cell population . To further confirm the requirement of Clims in promoting stem-like features , Clim1 and Clim2 were knocked down by siRNA , both independently and together , in the MCF10A and MCF7 mammary epithelial cell lines ( Figure S5C–D ) . Clim2 knockdown resulted in significantly reduced mammosphere forming efficiency in both cell lines , while Clim1 knockdown resulted in significant decreases in MCF10A cells , but not MCF7 cells ( Figure 4G ) . Consistently , we observed a synergistic decrease in mammosphere formation efficiency in the combined siClim1/2 in MCF10A cells , but not MCF7 cells . These cell type-specific differences in promoting stem-like properties may be attributed to higher levels of Clim1 in MCF10A cells than MCF7 cells . Altogether these data further support the role of Clims in promoting stem-like features of MECs . Clim2 is known to interact with LMO4 in MECs; LMO4 promotes mammary gland morphogenesis and breast cancer [39]–[42] . To determine whether Clim2 may be acting through LMO4 to maintain the mammary stem/progenitor cell population we transiently knocked down LMO4 gene expression individually and in conjunction with Clim2 in MCF10A and MCF7 cells ( Figure S5E ) . We found similar reduction in mammosphere forming efficiency with individual and combined knockdowns ( Figure 4G ) , suggesting these two transcription factors may target similar mechanisms to promote stem cell features , consistent with these two factors forming a tight complex [43]–[45] . Thus , Clim2 may regulate gene expression in these cells through the organization of transcriptional complexes involving LMO4 . Fgfr2 is significantly downregulated in the DN-Clim mammary gland throughout the developmental time course ( Figure 5A ) as validated by qPCR in the TEB and duct cells from 6 week old mice ( Figure 5B ) . Fgfr2 expression was also decreased in the sorted basal and luminal cells from DN-Clim mice ( Figure 5C ) . While the decrease in basal cells is likely due to cell autonomous effects of DN-Clim , the decreased Fgfr2 expression in luminal cells is likely non-cell autonomous as the DN-Clim molecule is not expressed in these cells . Since Fgfr2 is essential for maintaining the BSC population [46] , and DN-Clim is localized to the K14-positive basal MECs , we hypothesized that Clims directly regulate the expression of Fgfr2 to maintain BSCs . Indeed , the FGFR2 protein is decreased in DN-Clim basal cells ( Figure 5D–E ) . Transient knockdown of Fgfr2 in the Fgfr2-high MCF7 cell line impairs mammosphere forming efficiency ( Figure 5F ) , while stable overexpression of Fgfr2 in the Fgfr2-low MCF10A cell line with transient knockdown of Clims partially rescues mammosphere forming efficiency ( Figure 5G ) . These results suggest Clims regulate Fgfr2 expression to maintain stem-like features . To determine if Clims directly bind the Fgfr2 promoter , we performed ChIP assays for Clim2 , DN-Clim ( Myc-tag ) , H3K4me3 ( a marker for actively transcribed genes ) , and the Clim-interacting partner LMO4 , followed by qPCR targeting multiple sites in the 2 kb region surrounding the Fgfr2 transcriptional start site ( TSS ) . Clim2 binds to a region 538 bp to 708 bp upstream of the TSS in the WT mammary gland , with enriched H3K4me3 surrounding the TSS ( Figure 6A ) , while DN-Clim binds the same region upstream of the TSS in DN-Clim mammary glands , with a drastic reduction in H3K4me3 enrichment ( Figure 6B ) . Additionally , LMO4 is enriched at the same region in WT mammary glands , but is absent in the DN-Clim mammary gland , suggesting a necessity for Clim2 to recruit LMO4 to the promoter . To determine if Clim-binding activates transcription , we cloned the promoter-binding region ( 433 to 1010 bp upstream of the Fgfr2 TSS ) into the pGL3-promoter vector . In Clim2-positive MCF10A cells , this region of DNA enhances transcription of the luciferase reporter , and DN-Clim inhibits its transcription ( Figure 6C ) . Furthermore , Fgfr2 expression is downregulated in MCF10A and MCF7cells upon knockdown of Clim1/2 and LMO4 ( Figure 6D ) . Altogether , these data suggest that Clim2 promotes the maintenance of the BSC population by directly binding the Fgfr2 promoter to drive transcription of the gene . The CLIM protein is expressed in ER-positive breast tumors [21] , and we show here that the Clim-regulated gene network is associated with poor prognosis in breast cancer ( Figure 3E ) . Furthermore , its interacting partner LMO4 is upregulated in breast cancer and promotes breast tumorigenesis [40] , [42] , [47] , [48] . We examined the expression of Clims in the molecular subtypes of breast cancer and observed highest expression of Clim2 in the less differentiated claudin-low and basal-like breast cancer subtypes , while Clim1 is most highly expressed in the differentiated luminal cell types ( Figure 7A–B ) . Accordingly , Clim2 expression alone predicts poor prognosis in breast cancer , while Clim1 expression displays an opposite trend ( Figure S6 ) . The combined expression level of both Clim genes is a strong predictor of poor outcome ( Figure 7C ) , suggesting Clim2 is a more powerful predictor of breast cancer outcome than Clim1 . To determine if Clims play a role in breast tumorigenesis , we bred the K14-DN-Clim gene into the MMTV-PyMT mouse model . PyMT mice develop palpable tumors in nearly every mammary gland , whereas DN-Clim/PyMT mice only develop tumors in no more than six mammary glands ( Figure 7D ) . Additionally , DN-Clim/PyMT mice exhibit a significant delay in the development of palpable tumors ( Figure 7E ) and the growth rate of these tumors is drastically impaired ( Figure 7F ) . While tumor development is rapid and frequent in PyMT mice , with consecutive palpable tumors developing within days of each other , there was a significant delay in the time required for the second palpable DN-Clim/PyMT tumor to develop and the growth rate of the second tumor was significantly slower than the first ( Figure 7E–F ) . Taken together , these data demonstrate that Clim promotes the initiation and progression of breast cancer in the PyMT model . To determine the stage at which Clim may be promoting breast cancer , we collected whole mounts of mammary glands from as early as 3 weeks to as late as 3 months . Hyperplastic lesions appear in the mammary glands of 3-week-old PyMT and DN-Clim/PyMT mice ( Figure 7G ) . However , by 7 weeks entire arms of the mammary tree display hyperplastic lesions in PyMT mice , while the DN-Clim/PyMT mice only exhibit sparse lesions ( Figure 7G ) . Consistently , after removal of the primary tumor from mammary glands of 3 month old mice we observed a decrease in the area of the epithelial tree covered in tumor tissue . The drastic difference in the number of hyperplastic nodules developing from the mammary tree suggest that Clims act during the early stages of tumorigenesis . IHC staining of 6 week old mammary glands demonstrated that these tumors primarily express K8 , and not K14 ( Figure 7H ) , confirming they are luminal tumors [49] . PyMT tumors exhibit a rare population of K14-expressing cells at the leading , basal edge of the tumor , which also express DN-Clim in the DN-Clim/PyMT tumors ( Figure 7H ) . These data demonstrate the necessity of Clim-mediated , basal cell-specific gene expression for robust PyMT-mediated tumorigenesis , and suggest a functional role for Clim-dependent mechanisms in basal MECs during the initiation and progression of luminal breast tumors .
Transcriptional networks regulating the development and maintenance of the mammary gland luminal cell compartment are well characterized , with GATA3 , Notch1 , Elf5 , and STAT5a being key factors [38] , [50]–[52] . However , transcriptional regulation within the basal cell compartment remains poorly understood . Here , we describe a Clim-driven developmental transcriptional network in the basal cell compartment that maintains the number and proliferative potential of BSCs . Interfering with this transcriptional network leads to defective branching morphogenesis and impaired PyMT-mediated tumorigenesis . In addition , Clim-dependent basal cell-derived transcriptional programs support the expression of key luminal growth factor receptors and maintain the identity of luminal cells ( Figure 8 ) . The widely expressed Clims regulate unique transcriptional programs in diverse cell types . In part , this likely depends on Clim's tissue specific interactions with LIM domains of distinct LHX and LMO family members , such as LMO2 and LHX2 in erythroid cells [12] , [53] , LHX3 in the neural tube [54] , LHX2 in hair follicles [22] , [55] , [56] , and LMO4 in the mammary gland [40] . Despite this diversity in interactions , Clims seem to play a general role in promoting stem cell maintenance as previously shown in intestinal and hair follicle epithelia [22] , [57] , blood [58] , and ES cells [59] . How Clims maintain stem cell features is largely unexplained . We address this question in the mouse mammary gland with the K14-DN-Clim mouse model that targets both Clim family members in the basal cell population . DN-Clim , effectively disrupts Clim/LIM domain-mediated transcriptional complexes in vivo , as has been demonstrated in Drosophila , Zebrafish , and mouse [22] , [60] . Additionally , knockout of Lhx2 , the Clim-interacting partner in hair follicle stem cells , gives a similar phenotype as expression of DN-Clim [22] , [55] . Lacking the dimerization domain , DN-Clim binds LIM domain proteins to inhibit their interactions with endogenous Clims , thereby preventing Clim-mediated looping involved with higher order transcriptional complexes . We still observed DN-Clim at the promoter of the Fgfr2 gene , indicating that these interactions still allow the binding of LIM domain transcription factors to their target promoters . The N-terminal dimerization domain may have other functions not inhibited by DN-Clim , including possible interactions with other non-LIM domain transcriptional regulators [15] , [16] and interactions with the RLIM ubiquitin ligase that leads to degradation of associated LIM domain proteins [61] . DN-Clim has been demonstrated to stabilize nuclear LIM domain proteins [62] , which may result in increased levels of these proteins in the basal cells of the mammary gland . In the mammary gland , several lines of evidence argue that Clims maintain basal stem cells by stimulating expression of Fgfr2 . Expression of a DN-Clim molecule in the mouse mammary gland and knockdown of Clims in human MECs lead to downregulation of Fgfr2 . Also , Fgfr2 overexpression can rescue the impaired mammosphere formation after Clim knockdown in MECs . Furthermore , we demonstrate that Clims associate with and regulate the Fgfr2 promoter in MECs . This model is consistent with work showing that FGF signaling is essential to branching morphogenesis in several epithelial organs , including the drosophila trachea and the mammalian mammary gland , lung , kidney , and salivary gland [63] . FGF signaling promotes mammary embryonic placode formation [64] , and Fgfr2 is necessary to maintain the TEB structure and promote primary branching [34] , [35] . In addition , Fgfr2 is required for maintenance of the CD29HiCD24+ basal cell population and accordingly the maintenance of regenerative BSCs within this population [46] . The defects in branching morphogenesis , basal epithelial cell frequency , and MRU capacity observed in these Fgfr2 transgenic mouse models closely resemble the DN-Clim mammary gland phenotype . Thus , Clim acts immediately upstream of Fgfr2 signaling in the basal cell population through direct regulation of the Fgfr2 gene to maintain BSCs and promote branching morphogenesis . Our data suggest that Clim associates with its known mammary gland binding partner LMO4 to regulate the Fgfr2 gene . We find that LMO4 binds to the same region of the Fgfr2 promoter , and knockdown of LMO4 also leads to decreased Fgfr2 gene expression . Furthermore , we find that LMO4 knockdown leads to decreased mammosphere formation in MECs . That these proteins function as a complex is supported by experiments showing that the combined Clim/LMO4 knockdown does not lead to further decrease in mammosphere formation over individual knockdowns . Furthermore , functional studies of LMO4 in the mammary gland demonstrate its role in promoting branching morphogenesis and lobuloalveoar development by sustaining cell proliferation [39] , [41] . Altogether , these observations suggest Clims act through LMO4 in the mammary gland to promote BSC maintenance and development . In addition to regulating the number and replicative potential of BSCs , we find evidence that Clims regulate a basal cell program that maintains luminal cell fate . Since one of the models of stem cell hierarchy in the mammary gland proposes that BSCs give rise to the luminal cells [4] , this program may be established cell autonomously in the basal progenitors and persists in the luminal cells . Alternatively , the Clim-controlled program may act through basal-to-luminal cell signaling; there is previous evidence for crosstalk between the basal and luminal compartments through paracrine and cell adhesion-mediated mechanisms [1] , [2] . Irrespective of the underlying mechanisms , since DN-Clim is selectively expressed in the basal cell compartment , our gene expression data in basal and luminal cell compartments clearly reveals an effect of basal-specific Clim function on gene expression in luminal cells . In particular , DN-Clim mice exhibit downregulation of key growth factor receptors ErbB2 and ErbB3 , and upregulation of several epidermal differentiation genes . These observations demonstrate an essential role for Clims in the basal cell compartment to coordinate gene expression programs in the luminal cell compartment , and for the first time demonstrate the importance of basal cell factors on regulating the expression of key luminal regulators in the EGFR family . These mechanisms may contribute to the abnormal mammary development and decreased breast tumorigenesis in the DN-Clim/PyMT mice . The role of Clims is not limited to mammary gland development as our work also suggests they act within basal cells during the early stages of tumorigenesis . Expression of DN-Clim within the basal cell compartment significantly impairs the ability of PyMT to initiate neoplastic lesions . In the murine MMTV-PyMT tumor model Fgfr2 is overexpressed in the CD29HiCD24+ tumor initiating cell population and is necessary to initiate tumorigenesis in these mice [65] . As Clims are direct regulators of Fgfr2 in the mammary gland , the reduced tumorigenicity observed in DN-Clim/PyMT mice could in part be explained by Clim-mediated regulation of Fgfr2 in cancer stem-like cells . The MMTV promoter directs PyMT expression to both luminal and basal cells [66] , and while the histology and gene expression patterns group PyMT breast tumors with the luminal subtype of breast cancer , their cell of origin remains unknown [67] . Clims could be promoting early tumorigenesis through one of two scenarios: 1 ) Clims specifically maintain a basal cell-derived tumor initiating cell , or 2 ) Clims coordinate the communication of basal-derived tumorigenic signals to the luminal cell compartment , such as promoting the expression of Her2 in luminal cells . These signals would have to occur during the early stages of tumorigenesis when K14-positive basal cells are still present in premalignant lesions [68] . While basal cells have previously been assumed to act as inhibitors of breast cancer progression [69] , the restricted tumorigenicity observed in the DN-Clim/PyMT mammary gland suggests basal cells may permit tumorigenesis . Corresponding to these functional mouse experiments , we observed differential expression of Clims in the molecular subtypes of breast cancer , with high expression leading to poor outcome . It is well known that Clims are expressed in luminal cells and coordinate transcriptional programs through ERα in human breast cancer [21] . However , our K14-DN-Clim mouse model suggests an additional function for Clims during breast tumorigenesis in the ER-negative basal cell population , either through direct regulation of the basal cell or through indirect effects on the luminal cell . Furthermore , we find that a Clim-regulated gene module correlates with poor breast cancer prognosis arguing , for relevance of the mouse experiments for human breast cancer . It is possible that Clims may in part promote breast cancer through activation of Fgfr2 expression . Deregulation of FGF signaling is observed in breast cancer [70] , with FGFR2 amplification and overexpression observed in 5–10% of human breast cancers , correlating with poor prognosis [71] , [72] . In addition , single point mutations in the FGFR2 gene are associated with increased risk in human breast cancer [73] . In conclusion , we have demonstrated that Clims are a necessary factor in mammary gland branching morphogenesis , maintaining the BSC compartment . Clim2 coordinates with LMO4 to govern these processes through direct regulation of the Fgfr2 gene . In a breast cancer mouse model Clims act within the basal cells to permit tumorigenesis , possibly through the maintenance of cancer stem-like cells . As Clims are expressed in human breast cancer and correlate with poor differentiation of ER-positive tumors , elucidating Clim targets at a global scale may give insight into the transcriptional mechanisms that maintain primitive cancer cells . The developmental transcriptome of the TEB and duct populations reported will provide a valuable resource for delineating the molecular relationships between development and tumorigenesis , in addition to identifying novel prognostic and therapeutic targets .
Generation and maintenance of K14-DN-Clim mice were as previously described [22] . MMTV-PyMT mice ( Jackson Labs ) were maintained on the K14-DN-Clim mixed background . Fox Chase SCID Beige mice ( Charles River ) were used as recipients for transplant experiments . All experiments conform to the regulatory guidelines approved by the International Animal Care and Use Committee of the University of California , Irvine . Mammary glands 4 and 9 were dissected and either whole mounted , paraffin embedded , or frozen in O . C . T . For proliferation assays , mice were injected with BrdU ( 50 µg/g , Sigma-Aldrich ) 4 hours prior to sacrificing . Adult ( 8 to 12 week ) mammary glands for single-cell suspensions were generated according to Stem Cell Technologies protocol . When collecting RNA , the collagenase/hyaluronidase digestion was reduced to 1 . 5 hours . Single-cell suspensions were incubated with Propidium Iodide ( 2 µg/mL , Sigma-Aldrich ) , CD31-APC , CD45-APC , TER119-APC , CD24-PE ( BD Biosciences ) and analyzed by flow cytometry on FACSCalibur ( BD Biosciences ) or sorted on FACSAriaII ( BD Biosciences ) . Bulk primary MECs for in vitro culture were incubated with biotinylated CD31/CD45/TER119 cocktail ( Stem Cell Technologies ) and magnetically separated to remove lineage cells . FACS-sorted CD29HiCD24+ cells were transplanted into cleared fat pads of 3 week old immunocompromised SCID Beige recipients . Mammary glands were evaluated 8 weeks after transplantation . Limiting dilution analysis was performed on the ELDA Web-based tool ( http://bioinf . wehi . edu . au/software/elda/ ) . RNA was collected from laser capture microdissected ( Leica LS-AMD ) or FACS sorted MECs with the RNeasy Mini Kit ( Qiagen ) . Gene expression was measured with Affymetrix Mouse Gene 1 . 0ST array . Data were analyzed with PLIER algorithm . Differential expression was determined with the CyberT algorithm [31] and time course developmental gene expression analysis was performed with the BETR algorithm [24] . Detailed methods of data analysis are described in the supplemental methods accompanying this manuscript . The gene expression microarray data has been submitted to GEO ( http://www . ncbi . nlm . nih . gov/geo/ ) under the accession number GSE57724 . Additional details for the above methods are presented in Text S1 , as well as methods for IHC , immunofluorescence , whole mount , ChIP , RT-qPCR , mammosphere and colony forming assays , siRNA transfection , lentiviral expression , luciferase assays and microarray analysis . | Recent advancements in mammary gland biology demonstrate conflicting models in maintenance of basal and luminal cell compartments by either unipotent or bipotent mammary stem cells . However , the molecular mechanisms underlying control of the basal cell compartment , including stem cells , remain poorly understood . Here we explore the currently unknown transcriptional mechanisms of basal stem cell ( BSC ) maintenance , in addition to addressing the role of the basal cell compartment in preserving luminal cell fate and promoting development of human breast tumors of luminal origin . We discover a novel function for the Co-factor of LIM domains ( Clim ) transcriptional regulator in promoting mammary gland branching morphogenesis and breast tumorigenesis through maintenance of the basal stem cell population . The transcriptional networks coordinated by Clims in basal mammary epithelial cells also preserve the identity of luminal epithelial cells , demonstrating a crosstalk between these two cellular compartments . Furthermore , we correlate developmental gene expression data with human breast cancer to investigate the role of developmental pathways during the initiation and progression of breast cancer . The gene regulatory networks identified during development , including those specifically coordinated by Clims , correlate with breast cancer patient outcome , suggesting these genes play an important role in the progression of breast cancer . | [
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] | 2014 | The Co-factor of LIM Domains (CLIM/LDB/NLI) Maintains Basal Mammary Epithelial Stem Cells and Promotes Breast Tumorigenesis |
Schistosoma mansoni and Schistosoma japonicum are the most frequent causative agents of human intestinal schistosomiasis . Approximately 200 million people in the world are infected with schistosomes . Diagnosis of schistosomiasis is often difficult . High percentages of low level infections are missed in routine fecal smear analysis and current diagnostic methodologies are inadequate to monitor the progress of parasite control , especially in areas with low transmission . Improved diagnostic methods are urgently needed to evaluate the success of elimination programs . Recently , a magnetic fractionation method for isolation of parasite eggs from feces was described , which uses magnetic microspheres to form parasite egg – magnetic microsphere conjugates . This approach enables screening of larger sample volumes and thus increased diagnostic sensitivity . The mechanism of formation of the conjugates remains unexplained and may either be related to specific surface characteristics of eggs and microspheres or to their magnetic properties . Here , we investigated iron localization in parasite eggs , specifically in the eggshells . We determined the magnetic properties of the eggs , studied the motion of eggs and egg-microsphere conjugates in magnetic fields and determined species specific affinity of parasite eggs to magnetic microspheres . Our study shows that iron is predominantly localized in pores in the eggshell . Parasite eggs showed distinct paramagnetic behaviour but they did not move in a magnetic field . Magnetic microspheres spontaneously bound to parasite eggs without the presence of a magnetic field . S . japonicum eggs had a significantly higher affinity to bind microspheres than S . mansoni eggs . Our results suggest that the interaction of magnetic microspheres and parasite eggs is unlikely to be magnetic in origin . Instead , the filamentous surface of the eggshells may be important in facilitating the binding . Modification of microsphere surface properties may therefore be a way to optimize magnetic fractionation of parasite eggs .
Schistosomiasis is a helminth infection representing a major health burden for humans in tropical and developing nations . Some 200 million people are infected , and 600 million are currently estimated to be at risk of infection [1] . Recently , schistosomiasis control efforts have been increasingly focused on mass drug administration in endemic areas to alleviate morbidity in affected individuals [2] . Although it has been acknowledged that the goal to regularly administer chemotherapy to at least 75% of school-age children at risk of morbidity was not achieved by 2010 , many countries are controlling schistosomiasis with increasing success using a combination of therapeutics and improved sanitation [3] , [4] . Sustained and effective drug therapy has the effect of pushing the disease into a state of low endemicity , where individuals carry low-level infections that are very difficult to detect . As a result , new efforts are required for parasite surveillance in regions of low endemicity [5] . It has become increasingly recognized that the evaluation and monitoring of control and elimination efforts for schistosomiasis is hindered by the lack of appropriate diagnostic techniques [5] , [6] . With a diagnostic limit of approximately 100 eggs per gram feces , the current WHO recommended test , the Kato-Katz method of fecal examination , is limited by poor sensitivity [7] , [8] , [9] . It is estimated that more than half of all infections with schistosomiasis are missed in cross sectional studies relying on the observation of only one fecal smear , necessitating the need to perform multiple smears [10] . Examining multiple fecal smears at different time points is logistically difficult , and time and labour intensive . There is , therefore , an urgent need to develop new diagnostic methodologies for intestinal schistosomiasis that are highly sensitive and applicable under field conditions [5] . There is also the need for a new gold standard method to which more sensitive , newly developed , simple and field applicable molecular and rapid diagnostic tests can be compared . Recently , a novel method for Schistosoma egg detection based on magnetic fractionation of parasite eggs from fecal matter was developed [11] . For this method , termed Helmintex , magnetic microspheres are added to larger volumes of fecal samples ( 30 g ) . Parasite eggs and magnetic microspheres can then be co-purified from other fecal components through the application of a magnetic field and field gradient . The purified egg concentrates are more readily detectable by light microscopy . Teixeira and colleagues reported that magnetic microspheres coated with a variety of adsorbed molecules could be used to purify eggs of Schistosoma mansoni from fecal matter in a magnetic field [11] . The nature of the adsorbed molecules had no influence on the efficiency of the purification and even the use of uncoated microspheres resulted in the purification of Schistosoma mansoni eggs from feces in a magnetic field . The mechanism of interaction between parasite eggs and microspheres is unclear , yet it is important to characterize it further in order to optimize specificity and efficiency of the Helmintex method [12] . There are two possible explanations for the seemingly specific interaction of magnetic microspheres and Schistosoma eggs . Firstly , it could be that biochemical , chemical or physical surface properties of the eggs mediate the interaction . Secondly , the eggs could themselves be magnetic , leading to a magnetically mediated adhesion of the microspheres to the eggs . Since the interaction seems independent of the surface characteristics of the magnetic microspheres , the latter of these two possibilities seemed the more probable at the beginning of this investigation . It has been shown that the eggshells of S . japonicum contain iron in concentrations detectable by energy dispersive X-ray spectroscopy in the transmission electron microscope [13] . The authors of that study suggested that iron assists in the formation of the biopolymer that makes up the eggshells . Eggshells are formed by polymerization of tyrosine-rich eggshell precursor proteins that are synthesised in the vitelline glands of the parasite . The tyrosine residues are oxidised by tyrosinases to o-quinones . Lysine and histidine residues on the same or adjacent eggshell precursors subject the o-quinones to nucleophilic attack , leading to a robust cross-linked polymer [14] , [15] , [16] . In other invertebrates , such as the bivalves Mytilus , DOPA-rich bonds in quinone-tanned protein polymers are stabilized by divalent metal-ions , including iron [17] . The vitelline glands of schistosomes are enriched with iron and the iron storage protein ferritin [18] . Thus , there is strong evidence for a role of iron in stabilizing the protein polymer that is the schistosome eggshell . Full verification of this hypothesis requires the refinement of methods for hydrolysis of the highly resistant shells [19] . Here we present results of experiments elucidating the elemental composition of the eggshell , with special focus on the organization of iron in the matrix and the resulting magnetic properties of the eggs . We performed a range of experiments to characterise the nature of magnetic microsphere interaction with parasite eggs and in order to investigate whether this interaction was a result of non-specific binding of the microspheres to the surface of the eggs , or whether it was the result of magnetically aided adhesion of eggs and microspheres .
Samples of S . mansoni and S . japonicum eggs were fixed in 2% ( v/v ) glutaraldehyde , 1% ( w/v ) paraformaldehyde in PBS for 60 min at 4°C and washed twice with PBS ( pH = 7 . 4 ) in 1 . 5 mL Eppendorf tubes . The samples were then serially dehydrated in ascending concentrations of ethanol ( 33% , 50% , 66% , and 100% ( dry ) ) followed by two further washes in dry ethanol using a PELCO Biowave microwave processor ( TedPella Inc . , Redding , CA , USA ) . Dehydrated samples were transferred onto circular polylysine-coated glass coverslips and critically point dried ( Emitech 850 Critical Point Drier , Quorum Technologies , Ashford , UK ) . The coverslips were then attached to aluminum sample holders and coated with a 5 nm thick platinum coating for morphological analyses . SEM was performed using the in-lens detector of a Zeiss 1555 VP field emission scanning electron microscope operating at 15 keV ( Carl Zeiss , Überkochen , Germany ) . Eggs of both parasite species were fixed in 3% ( v/v ) glutaraldehyde in 0 . 1 M phosphate buffer . Eggs were transferred to a solution of 20% ( w/v ) bovine serum albumin in PBS on a copper membrane and rapidly frozen in a Leica EM PACT2 High Pressure Freezer ( Leica , Vienna , Austria ) . Subsequently , the membranes and samples were transferred in cryo-tubes under liquid nitrogen to a Leica EM AFS freeze substitution apparatus for fixation and dehydration in 2% ( w/v ) osmium tetroxide and 0 . 5% uranyl acetate ( w/v ) in 100% anhydrous acetone . The tissues were cryo-substituted for 3 days , according to the following protocol: The temperature of the substitution chamber was increased from −160°C to −85°C over 2 h , and maintained at −85°C for 48 h , after which the samples were brought to room temperature . After further washes in anhydrous acetone , the samples were infiltrated and embedded in Epon resin . For HAADF-STEM , resin sections were cut from blocks at a thickness of 150 nm using an EM UC6 ultramicrotome ( Leica , Vienna , Austria ) and mounted onto 200 µm mesh carbon filmed copper grids for analysis at 300 kV using a JEOL JEM 3000F FEGTEM transmission electron microscope ( JEOL , Tokyo , Japan ) . A ∼1 nm probe size was used to image the mass variation within the sections , with areas of high mass appearing bright . Energy Dispersive X-ray Spectroscopy ( EDS ) data was combined with STEM imaging using an Oxford Instruments INCA detector ( Oxford Instruments NanoAnalysis , High Wycombe , UK ) to map the composition of features of interest . SquiD magnetic susceptometry is a technique to determine the magnetic properties of any given solid material . Samples are exposed to a desired sequence of magnetic fields at constant temperature or a sequence of temperatures at a constant magnetic field . The magnetization of the sample material resulting from this exposure is recorded for each point in a sequence . Using standard sequences , basic magnetic properties ( e . g . , whether a material is ferromagnetic , paramagnetic or diamagnetic ( non-magnetic ) ) can be determined . Lyophilized S . mansoni and S . japonicum eggs were placed in gel capsules for magnetic characterization in a 7 Tesla ( T ) magnetic property measurement system SQuID magnetic susceptometer ( Quantum Design , San Diego , CA , USA ) . Magnetic hysteresis loops were acquired between −7 T and 7 T at 5 K . Zero-field-cooled and field-cooled ( ZFC-FC ) magnetization versus temperature curves were obtained from 5 to 300 K , in a measurement field of 0 . 01 T . The concentration of iron , copper and silicon was determined for both types of eggs using ICP-AES . Inductively coupled plasma atomic emission spectroscopy ( ICP-AES ) is an analytical technique used to determine the elemental composition of a material . It uses inductively coupled plasma to produce excited atoms and ions that emit electromagnetic radiation at wavelengths characteristic of a particular element which are then detected by a detector . The same samples as in the SQuID measurements were used . For ICP-AES analysis , three replicate samples were digested in 70% HNO3 at 95°C . The analysis was performed at the Marine and Freshwater Research Laboratory at Murdoch University , Murdoch , WA , Australia . In order to assess the ability to manipulate parasite eggs using a magnetic field , approximately 100 glutaraldehyde-fixed eggs of S . japonicum were floated on a 100% Percoll/water interface in a 5 mL cell culture dish . No spontaneous hatching of eggs was observed in these fixed eggs . A cylindrical neodymium-iron-boron magnet was brought close to the eggs so that they were exposed to a magnetic field of approximately 0 . 1 T and a magnetic field gradient of approximately 35 T/m while being observed under an optical microscope . To assess the capability of the different egg species to bind magnetic microspheres , eggs of the two species were incubated with microspheres at egg/microsphere ratios of 1∶100 and 1∶500 . Unbound microspheres were washed out using custom made filters after an incubation time of 10 min with agitation . Images of the conjugated microsphere/egg suspension were taken at a 100-fold magnification and the distributions of the number of observed microspheres per egg were recorded and compared with the Poisson distribution . The SQuID magnetometry data was fitted to two functions , the Brillouin function and Curie's law . These functions are used to determine the atomic iron specific moment in the samples [21] . The Brillouin function is specifically used to describe the response of an ideal paramagnet to an applied magnetic field . The spin state and thus the oxidation state of the iron atoms in a material can be deduced from the Brillouin fit using Equation 1 . ( 1 ) The function describes the dependency of the magnetization ( M ) on the applied magnetic field ( B ) in an ideal paramagnet mixed with diamagnetic ( non-magnetic ) atoms and gives the total angular momentum quantum number J of the microscopic paramagnetic moments of the material . N is the number of paramagnetic atoms in the sample; g is the electron g-factor ( −2 . 0023 ) ; μB is the Bohr Magneton ( 9 . 274×10−24 J T−1 ) ; J is the total angular momentum quantum number for each paramagnetic atom; kB is the Boltzmann constant ( 1 . 380×10−23 m2 kg s−2 K−1 ) ; T is the temperature and B is the magnetic flux density . The factor A is the diamagnetic susceptibility of the sample holder and the diamagnetic components of the eggs . Curie's law is used to describe the temperature dependency of magnetic susceptibility ( χ ) . Data for this analysis are often plotted as 1/χ versus T . A linear relationship with line of best fit running through the coordinate origin indicates ideal paramagnetic behavior . In the present study , the magnetization versus temperature data were fitted with Curie's law shown in Equation 2 . ( 2 ) where χ is the magnetic susceptibility of the eggs and all other symbols correspond to those used in Equation 1 .
SEM images of S . mansoni and S . japonicum eggs are shown in Figure 1 . The eggs exhibit the typical features of S . mansoni ( large spine ) and S . japonicum ( oval shape , small spine ) [22] . The fibrous matrix surrounding the egg , also typical for S . japonicum , can clearly be seen in Figures 1C and 1D [22] . Figures 1B and 1D show examples of eggs of both species where the eggshell has broken open and the miracidium is still inside the egg . It can be seen that the eggshell curls outwards after initial rupture . Figure 2 shows details of the surfaces of the eggs of both species at a higher magnification . The surface of S . mansoni is covered with evenly spaced structures previously termed microspines with a length of about 200–300 nm and a diameter of 60 nm [22] , [23] . The surface of S . japonicum is also covered with microspines , however they are considerably shorter . In addition a structure , previously termed the fibrous matrix covers the S . japonicum surface [22] . The elemental composition and structure of the eggshells of both species was studied by HAADF-STEM and STEM-EDS ( Figure 3 ) . The TEM images show that the eggshells are about 700 nm thick . There are regions where the shell is interspersed with small holes of variable size ( 50–200 nm ) . These holes have been shown to be empty in other studies [23] . In the present study we show for the first time that these holes are partially filled with a material containing iron , phosphorous and oxygen and we hypothesise that this material is an iron-phosphate that is retained more readily by cryo-fixation and subsequent freeze-substitution processing compared to conventional TEM sample preparation methods ( Figure 3 ) . The results from the magnetometry measurements of the eggs are presented in Figure 4 . Both , S . mansoni and S . japonicum eggs exhibited paramagnetic behaviour with no hysteresis at 5K ( Figure 4A ) . The magnetic susceptibility versus temperature measurements were in nearly perfect agreement with Curie's Law ( Figure 4B ) . The magnetic moment per iron atom ( measured in Bohr magnetons - μB ) obtained from fitting the Brillouin function with an additional diamagnetic contribution ( Equation 1 ) to the magnetization versus magnetic field data ( Figure 4A ) at 5K was 4 . 8 and 4 . 1×μB for S . mansoni and S . japonicum respectively . It was assumed that iron was the only paramagnetic material in the eggs . Similar values were obtained from fits of Curies's law to the magnetization versus temperature data ( 4 . 76 and 3 . 7×μB for the S . mansoni and S . japonicum eggs respectively ) . These values agree but are slightly lower than what would be expected if all the iron in the sample were present as high spin Fe2+ ions ( typically 5 . 4×μB ) or high spin Fe3+ ( typically 5 . 9×μB ) . We can therefore conclude that there is mix of high spin and low spin iron configurations present in the eggs . Further , more sophisticated measurements using , for example , Mössbauer spectroscopy , that can be used to detect the chemical environment around each iron atom , would be necessary to resolve the exact distribution of these configurations . ICP-AES data are summarized in Table 1 . S . mansoni and S . japonicum eggs contained approximately 0 . 74 mg/g and 1 . 26 mg/g of iron respectively ( dry weight ) . By comparison , 1 g of blood contains 3 . 39 mg iron and 1 g of normal human feces approximately 0 . 3 mg iron ( dry weight ) [24] . The concentrations of copper and silicon were also determined . No movement of parasite eggs suspended at the Percoll/water interface was observed when the eggs were exposed to a strong magnetic field and a high magnetic field gradient , and imaged with light microscopy . However , when magnetic microspheres were incubated with parasite eggs they readily bound to a fraction of the eggs even without the presence of a magnetic field . The microsphere-egg conjugates were very susceptible to magnetic fields and field gradients as shown in Figure 5 and the video file provided as supporting information ( Video S1 ) . Figure 6 illustrates the binding characteristics of the microspheres to the parasite eggs . For both tested parasite egg to microsphere ratios ( 1∶100 and 1∶500 ) the fraction of S . japonicum eggs that bound microspheres was statistically significantly higher than the fraction of S . mansoni eggs that bound microspheres ( 54% versus 41% , p = 0 . 02 for the 1∶100 ratio and 76% versus 30% , p<0 . 001 , for the 1∶500 ratio , unpaired t-test ) . In addition the number of microspheres which bound to individual S . japonicum eggs was significantly higher than that for S . mansoni ( Figure 6 ) . The distribution of microspheres per egg was not well characterized by a single Poisson distribution , especially for the S . japonicum eggs but reasonable fits were obtained on the assumption that a fraction of the eggs had no binding capacity ( for more details on the analyses using Poisson statistics please refer to the supporting Figures S1 and S2 as well as Text S1 ) .
The present study investigated the magnetic properties as well as iron localization and content of S . mansoni and S . japonicum eggs and especially the eggshells . This investigation was conducted to elucidate the processes underlying the success of a previously developed magnetic fractionation approach for the detection of parasite eggs in fecal samples , namely the Helmintex method [11] . In recent years , this method has been used in a series of diagnostic studies and has consistently shown improved sensitivity when compared with the conventional Kato-Katz method of fecal evaluation and the saline gradient method [11] , [25] , [26] , [27] . The most important question for optimizing the existing Helmintex method was whether the magnetic properties of the Schistosoma eggs were the cause for the adhesion of the magnetic microspheres to the surface of the eggs or whether this binding was of another biochemical or physical nature . We show that the eggshells of S . mansoni and S . japonicum eggs contain iron in concentrations measurable by STEM-EDS and ICP-AES , and that the eggs are distinctly paramagnetic , meaning they magnetize in an applied magnetic field and demagnetize when the magnetic field is taken away . These are the principal characteristics required for magnetic fractionation . Interestingly , most of the iron seems to be accumulated as iron-phosphate in pores in the eggshell . However , the magnetization of the eggs was comparatively weak and the iron content was low . Furthermore we did not observe any movement of the eggs in magnetic fields and field gradients of a similar order of magnitude to those used in the Helmintex protocol . Therefore , the interaction between the eggs and the magnetic microspheres , which is the basis of the success of the Helmintex method , is unlikely to be magnetic in origin . The original Helmintex studies have shown that magnetic microspheres coated with a wide variety of different ligands could be used to purify parasite eggs [11] . Here , we show for the first time that microspheres physically bind to the eggs . The high surface area of the filamentous outer structure of the eggs may be part of the explanation as this large surface area may provide strong overall adhesion from relatively weak interactions . This hypothesis is further supported by the observation that S . japonicum eggs with their additional fibrous matrix bound significantly more microspheres than S . mansoni eggs , which do not have this matrix . However , the distributions of microspheres per egg observed in the binding studies suggest that a fraction of the eggs have very little , if any , binding capacity . Furthermore , it should be noted that the fixation using glutaraldehyde may lead to modified surface characteristics of the eggs . Further studies are required to investigate the impact of fixation on particle binding . The present study provides the first magnetic characterization of S . mansoni and S . japonicum eggs . We report the discovery of an iron-containing material , presumably iron phosphate located in pores within the eggshell . We provide evidence that Schistosoma eggs are not magnetic enough to move in an applied magnetic field of a similar order of magnitude as the one used in the Helmintex method . We show that magnetic microspheres spontaneously bind to eggs of S . mansoni and , to a greater degree , to S . japonicum . Based on these results we conclude that the conjugation of magnetic microspheres and parasite eggs is mediated not by magnetism but by the surface properties of eggs and microspheres . Systematic quantification of the binding of microspheres that have different surface functionalizations to parasite eggs is likely to represent an opportunity to optimize the Helmintex magnetic fractionation method . Previous field studies have indicated that such an optimized Helmintex method may be developed into a new gold standard to validate future rapid diagnostic and molecular methods for Schistosoma detection [25] , [26] , [27] . | In the present study , we investigated the mechanism underlying a novel diagnostic method for Schistosoma – one of the most widespread and frequently occurring parasites infecting humans in tropical countries . In recent years , the world has seen significant reduction in the burden of Schistosoma infections in many countries due to improved control and sanitation . However , it is becoming increasingly difficult to evaluate and monitor the progress of control towards elimination . At the moment it is extremely difficult to determine whether the parasite has been eliminated from a region . This is due to the absence of a sensitive and inexpensive method to detect the parasite . A series of recent studies describes a method with vastly improved diagnostic sensitivity based on the magnetic fractionation of parasite eggs from fecal samples . However , the mechanisms of action of this new diagnostic are not currently known . To further optimize and improve this method , we studied the magnetic properties of parasite eggshells and their binding characteristics to magnetic microspheres . | [
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] | 2013 | The Iron Distribution and Magnetic Properties of Schistosome Eggshells: Implications for Improved Diagnostics |
Virtually all of the elements of Mycobacterium tuberculosis ( Mtb ) pathogenesis , including pro-inflammatory cytokine production , granuloma formation , cachexia , and mortality , can be induced by its predominant cell wall glycolipid , trehalose 6 , 6′-dimycolate ( TDM/cord factor ) . TDM mediates these potent inflammatory responses via interactions with macrophages both in vitro and in vivo in a myeloid differentiation factor 88 ( MyD88 ) -dependent manner via phosphorylation of the mitogen activated protein kinases ( MAPKs ) , implying involvement of toll-like receptors ( TLRs ) . However , specific TLRs or binding receptors for TDM have yet to be identified . Herein , we demonstrate that the macrophage receptor with collagenous structure ( MARCO ) , a class A scavenger receptor , is utilized preferentially to “tether” TDM to the macrophage and to activate the TLR2 signaling pathway . TDM-induced signaling , as measured by a nuclear factor-kappa B ( NF-κB ) -luciferase reporter assay , required MARCO in addition to TLR2 and CD14 . MARCO was used preferentially over the highly homologous scavenger receptor class A ( SRA ) , which required TLR2 and TLR4 , as well as their respective accessory molecules , in order for a slight increase in NF-κB signaling to occur . Consistent with these observations , macrophages from MARCO−/− or MARCO−/−SRA−/− mice are defective in activation of extracellular signal-related kinase 1/2 ( ERK1/2 ) and subsequent pro-inflammatory cytokine production in response to TDM . These results show that MARCO-expressing macrophages secrete pro-inflammatory cytokines in response to TDM by cooperation between MARCO and TLR2/CD14 , whereas other macrophage subtypes ( e . g . bone marrow–derived ) may rely somewhat less effectively on SRA , TLR2/CD14 , and TLR4/MD2 . Macrophages from MARCO−/− mice also produce markedly lower levels of pro-inflammatory cytokines in response to infection with virulent Mtb . These observations identify the scavenger receptors as essential binding receptors for TDM , explain the differential response to TDM of various macrophage populations , which differ in their expression of the scavenger receptors , and identify MARCO as a novel component required for TLR signaling .
Mycobacterium tuberculosis ( Mtb ) , a causative agent of human tuberculosis , is responsible for 8 million new infections and 2 million deaths yearly . One third of the world population is currently estimated to be infected with M . tuberculosis , although less than 10% of those infected show clinical signs of infection [1] . This is mainly due to the robust granulomatous response that is initiated by the bacterium , which effectively contains the infection and allows the host to exist in equilibrium with a subclinical infection . The granulomatous response has been shown to be triggered by multiple components of the mycobacterial cell wall , such as phosphatidylinositol dimannoside , phosphatidylinositol hexamannoside , and trehalose 6 , 6′-dimycolate ( TDM ) [2]–[5] . The chemical structure of TDM ( also known as cord factor ) was solved in 1956 [6] and was identified as the predominant immunogenic mycobacterial cell wall glycolipid [7] . TDM can elicit pro-inflammatory cytokine production in vitro , and granulomatous responses in vivo , when administered as a monolayer or part of an oil-water emulsion [7]–[10] . Although it has been known for decades that this pro-inflammatory response is mediated primarily by macrophages , binding or signaling receptors for TDM on macrophages have yet to be identified . Biochemical approaches used in our laboratory to identify TDM receptors , such as affinity isolation using TDM columns , TDM bead phagosome isolation from labeled macrophages , and mass spectrometric analysis of phagocytosed TDM-coated beads after photoactivatable cross-linking , have been unsuccessful . These observations , as well as the finding that the stimulatory activity of TDM requires presentation over a larger surface area , such as emulsions , monolayers , or large diameter particles , suggests that the TDM-receptor interaction is of low avidity and requires the aid of co-receptors or other accessory molecules [9] , [10] . One class of receptors implicated in TDM recognition is the TLRs . TLRs have not been shown to bind to TDM directly , but bone marrow-derived macrophages ( BMMΦ ) from MyD88−/− mice do not produce pro-inflammatory cytokines in response to TDM-coated polystyrene microspheres [9] . Although TLRs have been shown to sense and signal from within the phagosome , they are not phagocytic receptors and usually require the presence of a co-receptor ( e . g . CD14 ) to present their ligands [11] . Class A scavenger receptors ( SR ) , SRA and MARCO , are a class of phagocytic receptors that we have demonstrated mediate recognition and presentation of TDM . SRs bind a range of ligands of endogenous and exogenous origin with relatively low affinity . Ligands for the SRs include proteins [12] and lipids . These lipids can be derived from either the host ( e . g . oxidized lipids ) [13] , or from exogenous sources ( e . g . lipopolysaccharide ) [14] . Of the two class A SRs , SRA has been clearly demonstrated to be involved in host defense by suppressing excessive pro-inflammatory cytokine production in mouse models of infection and septic shock [14] , [15] . MARCO has also been implicated in host defense against bacterial pathogens , but it is not clear whether it is a positive or negative regulator of pro-inflammatory cytokine production [16] , [17] . It has been proposed that the class A SRs may be involved in host defense against mycobacterial infection . SRA expression is increased after interferon-gamma ( IFN-γ ) treatment or exposure to M . tuberculosis , and is highly expressed on macrophages associated with M . bovis Bacille Calmette-Guérin ( BCG ) -induced granulomas [14] , [18] . There are conflicting reports as to whether expression of SRA increases uptake of M . tuberculosis or BCG; however , its presence does not appear to affect the rate of replication of BCG , despite being protective against BCG-primed endotoxic shock [14] , [18] . In mouse models , MARCO expression has been shown to be transiently up-regulated on macrophages in response to BCG infection and to be expressed on macrophages within , and adjacent to , BCG-containing granulomas [19] . MARCO-expressing macrophages in the splenic marginal zone appear to phagocytose more BCG than neighboring macrophages that do not express MARCO [19] . The mycobacterial ligands that mediate this recognition have not yet been identified . Herein , we identify that TDM recognition and signaling is mediated , at least in part , by MARCO , TLR2 , and CD14 . Although SRA and MARCO have many common ligands , our results show that MARCO binds more TDM-coated beads than either isoform of SRA . MARCO is required for TDM-induced signaling via TLR2 and CD14 in a transfection system , whereas SRAI and SRAII require co-transfection of TLRs 2 and 4 , and their accessory molecules , to permit even a minor response to TDM stimulation . Consistent with these data , both resident peritoneal macrophages ( RPMφ ) and BMMφ from TLR2/4 double-deficient mice ( but not the individual mutants ) have a markedly reduced response to TDM . This suggests that TDM engages TLR2 and TLR4 in a redundant fashion and that these predominantly MyD88-dependent pathways are required for the stimulatory effects of TDM [9] . When stimulated with TDM-coated microspheres , macrophages from MARCO−/− and MARCO−/− SRA−/− double-deficient ( DKO ) mice also show reduced activation of ERK1/2 compared to wildtype mice and are defective in subsequent pro-inflammatory cytokine production . These macrophages also produce fewer pro-inflammatory cytokines in response to infection with M . tuberculosis , indicating that SR-mediated detection of TDM may be an important component of the response to infection . On the basis of these data we propose a model in which MARCO , and to a lesser extent SRA , cooperate with TLR2 and CD14 for TDM recognition and signaling .
BMMφ secrete proinflammatory cytokines in response to TDM coated onto microspheres that are too large to be phagocytosed [9] , whereas RPMφ readily produce TNF-α in response to phagocytosable TDM-coated microspheres and secrete much higher amounts than BMMφ in response to larger microspheres ( Fig . 1A ) . RPMφ express high levels of MARCO and SRA , whereas BMMφ express high levels of SRA , but do not express MARCO at levels detectable by immunoblot ( Fig . 1B ) . RPMφ upregulate MARCO at the RNA and protein level in response to TDM-coated 90 µm microspheres , however we have not observed any significant increase in MARCO expression on BMMφ by immunoblot after as many as 72 hours of exposure to LPS or TDM-coated 90 µm microspheres ( data not shown ) . While there are most likely many differences between RPMφ and BMMφ , the absence of MARCO expression on BMMφ may account for the differences in the response to TDM between these two cell types . We have previously shown that TDM-induced cytokine production is MyD88-dependent but had not identified which TLRs are required ( 9 ) . Herein we demonstrate that both TLRs 2 and 4 are partially required for TDM-induced cytokine production . BMMφ from C3H/HeJ mice ( defective in TLR4 signaling ) and TLR2−/− mice showed no reduction in TNF-α production ( Fig . 2A&B ) , while macrophages from TLR2/4−/− mice had greatly reduced TNF-α responses to TDM ( Fig . 2C ) . This suggests that TLR2 and 4 can function at least in a partially redundant manner with respect to TDM responses . In order to determine whether TDM-coated beads were a ligand for MARCO , CHO-K1 cells , which do not express SRs , were transfected with plasmids encoding either MARCO or SRA , and non-opsonic bead binding was assessed . MARCO- and SRA-transfected cells bound significantly more beads than mock-transfected cells , and binding was inhibited by the SR inhibitor dextran sulfate ( DxSO4 ) , but not chondroitin sulfate ( ChSO4 ) , which does not inhibit the SRs ( Fig . 3A ) . Phosphatidylglyercol ( PG ) was used as a negative control lipid because we have previously shown that it can be coated onto microspheres in a similar manner and induces a minimal cytokine response from macrophages . PG-coated beads did not bind cells transfected with either MARCO or SRA to a greater extent over empty vector-transfected cells . Furthermore , PG bead binding to cells could not be inhibited by SR inhibitors , indicating that binding was not MARCO- or SRA-specific ( data not shown ) . SRA-transfected cells bound fewer TDM-coated beads despite having higher transfection efficiency ( Fig . 3B ) indicating that SRA may have a lower binding affinity for TDM-coated beads . HEK293 cells do not express most TLRs , except for TLRs 1 , 5 , and 6 ( C . Leifer , personal communication ) and are therefore often used for reconstitution assays . By co-transfecting these cells with a luciferase reporter driven by an NF-κB promoter and various combinations of TLRs and SRs , we determined which combinations of receptors allow TDM-induced NF-κB signaling . HEK293 cells transfected with only TLR2 or TLR4 , with accessory molecules CD14 or MD2 , respectively , did not respond to TDM-coated 90 µm microspheres more than PG-coated microspheres or medium alone ( Fig . 4A ) . The addition of human MARCO ( hMARCO ) to TLR2/CD14 or TLR2/CD14/TLR4/MD2 , however , allowed statistically significant ( p<0 . 05 ) stimulation of luciferase activity in response to TDM ( Fig . 4A ) . Interestingly , transfection of human SRAI ( Fig . 4B ) or SRAII ( data not shown ) required both TLRs 2 and 4 , with their respective accessory molecules , in order for TDM-induced stimulation to be observed . These results suggest that MARCO is required for TDM-induced signaling via TLR2/CD14 and that SRA is less efficient in mediating responses to TDM , requiring both TLRs 2 and 4 , as well as their accessory molecules . The scavenger receptors have been implicated in modulating TLR signaling by a variety of TLR agonists , but it is not clear if they alter these responses by direct involvement in the signaling pathway . In order to determine whether the cytoplasmic region of MARCO ( which would , in theory , transduce signaling events ) could contribute to TDM-induced NF-κB activation , two constructs of MARCO were made . The first ( Myr MARCO ) lacks amino acids 1–40 but contains a putative myristoylation site; the second ( N-tail ) lacks the entire cytoplasmic domain ( 1–49 ) ( Fig . 5A ) . After confirming that the constructs were expressed on the surface of transfected cells ( Fig . 5C ) , their ability to restore TDM-induced NF-κB activation was tested . Cells transfected with either the wild-type MARCO ( hMARCO ) , or the two deletion mutant constructs ( Myr MARCO and N-tail MARCO ) , in addition to TLR2 and CD14 , retained their ability to induce NF-κB activation ( Fig . 5B ) . These data suggest that the cytoplasmic region of MARCO is not required for binding or signaling responses to TDM . Confocal immunofluorescence microscopy was performed to determine whether MARCO or SRA localizes to the membrane of phagosomes containing TDM-coated or control beads . At early time points ( 5–10 minutes ) , MARCO recruitment to TDM bead phagosomes was visible in both transfected CHO-K1 cells ( Fig . 6C ) and RPMφ ( Fig . 6A ) . Recruitment was not clearly evident in MARCO-transfected CHO-K1 cells or in RPMφ that had phagocytosed bovine serum albumin ( BSA ) - or PG-coated microspheres ( Fig . 6A and C , and data not shown ) . SRA staining of RPMφ did not show strong co-localization with TDM- or BSA-bead phagosomes at this early time point ( Fig . 5B ) , and SRA-transfected CHO-K1 cells showed no appreciable accumulation of SRA at the phagocytic cup in response to either TDM or BSA-coated beads ( Fig . 6B and D ) . At later time points ( 15–30 minutes ) , both MARCO and SRA staining occurred at the site of binding to the TDM- , PG- , and BSA-coated microspheres ( data not shown ) , consistent with previous observations that the scavenger receptors are involved in the binding and uptake of polystyrene beads [20] . From these results , we conclude that MARCO is specifically recruited to the phagosomal membrane surrounding TDM-coated beads , whereas SRA recruitment is non-specific . RPMφ from wildtype , MARCO−/− , SRA−/− , MARCO−/−SRA−/− ( DKO ) , TLR2−/−/TLR4−/− and CD14−/− mice were stimulated with lipopolysaccharide ( LPS ) , Pam3Csk4 , microspheres coated with either TDM or PG , or medium only , and lysates were immunoblotted for phosphorylated ERK1/2 . ERK1/2 was phosphorylated in response to TDM-coated microspheres in wild-type macrophages ( Fig . 7 ) . There was no detectable decrease in the TDM response by SRA−/− macrophages ( data not shown ) ; however , MARCO−/− macrophages had a reduced response to TDM , and ERK1/2 phosphorylation was further reduced in macrophages from DKO mice ( Fig . 7 ) . The levels of ERK1/2 phosphorylation in response to LPS and Pam3Csk4 were similar between wildtype and SR-deficient macrophages indicating that the SR knockouts do not have a global defect in their ability to phosphorylate ERK1/2 in response to TLR agonists but are specifically defective in the TDM response ( Fig . 7 ) . Equivalent levels of ERK1/2 activation by LPS in CD14-deficient RPMφ may be due to soluble CD14 provided by fetal calf serum in the medium and the high dose of LPS used [21] . Because the SRs are required for TDM-induced ERK1/2 activation ( Fig . 6 ) , we hypothesized that SR-deficient macrophages might also be defective in downstream pro-inflammatory cytokine production . RPMφ from wildtype , MARCO−/− , SRA−/− or DKO mice were stimulated with 3 µm diameter TDM-coated beads for 24 hours . Both the MARCO−/− and SRA−/− macrophages had a statistically significant reduction in TNF-α production ( P<0 . 05 ) . TNF-α production from DKO macrophages was completely abrogated ( P<0 . 025 ) ( Fig . 8C ) . This is not due to decreased levels of TLR2 ( Fig . 8A ) or TLR4 ( Fig . 8B ) expression on the SR-deficient macrophages , as these are both expressed at equivalent levels between wildtype and SR−/− RPMΦ . The mouse macrophage cell line , RAW264 . 7 , is similar to BMMφ in expressing high levels of SRA ( data not shown ) but lacking MARCO at both the protein ( Fig . 9B ) and RNA levels ( data not shown ) , and in producing lower levels of cytokines in response to TDM as compared to RPMφ ( Fig . 9A ) . We predicted that transfection of RAW264 . 7 with MARCO could elevate responsiveness to TDM . Stable cell lines expressing either human MARCO ( hMARCO-RAW ) or an empty vector ( vector ) were created and stimulated with TDM- or PG-coated microspheres for 24 hours , after which the levels of TNF-α in the medium were assessed by ELISA . Only the hMARCO-expressing cells produced TNF-α in response to the TDM-coated microspheres ( P<0 . 05 ) ( Fig . 9A ) . This result further supports the important role of MARCO in TDM-induced cytokine production . TDM is an essential virulence factor for M . tuberculosis pathogenesis and thus we hypothesized that pro-inflammatory cytokine production resulting from M . tuberculosis infection would also be impaired in MARCO-deficient macrophages . RPMφ were infected with an MOI of 5 for 24 hours and cytokine production in the supernatants was assessed by ELISA . Consistent with our hypothesis , MARCO−/− and DKO macrophages produced significantly less TNF-α , IL-6 , and IL-1β than wildtype macrophages ( Fig . 10 ) .
The pathogenesis and establishment of M . tuberculosis infection requires phagocytosis of the bacterium by macrophages and initiation of the pro-inflammatory response . These two events are at least partially independent . Phagocytosis is mediated by a number of receptors including the mannose receptor and DC-SIGN which recognize mannose-capped lipoarabinomannan ( ManLAM ) [22] , [23] , and complement receptor which mediates the phagocytosis of both opsonized and non-opsonized bacteria [24] . The initiation of a pro-inflammatory response appears to be mediated primarily via TLRs [25] and possibly other signaling receptors such as dectin-1 [26] . Of the mycobacterial cell wall lipids that initiate a TLR-mediated inflammatory response , TDM appears to be one of the more potent [9] . Although we have demonstrated that the macrophage response to TDM is partially TLR2/4-dependent ( Fig . 2 ) , our initial attempts to reconstitute NF-κB signaling in a TLR2/4 stably-transfected cell line were not successful . It seemed likely that this was due to the requirement of an additional co-receptor , because many TLR ligands , and especially lipid-based ligands , require presentation via a co-receptor . The co-receptor CD14 has been implicated in facilitating TLR1/2-mediated responses to bacterial lipopeptides by enhancing the physical proximity of the ligand to the TLR1/2 heterodimers , without binding directly to the receptor complex [25] , [27] , [28] . However , CD14 expression in conjunction with TLR2 or TLR4 did not restore responsiveness to TDM ( Fig . 4 ) suggesting that CD14 was not the only co-receptor for TDM . Because MARCO and SRA bind to TDM-coated beads ( Fig . 3A ) , we hypothesized that they might be the additional co-receptors required for TLR2 signaling . Both MARCO and CD14 in conjunction with a TLR2 homo- or heterodimer appear to be required to initiate TDM signaling , and MARCO appears to be preferred over the closely related SRA ( Fig . 4A and B , Fig . 7 ) . We therefore propose that the scavenger receptors function as co-receptors that , in conjunction with CD14 , present TDM to TLR2 . Further work is warranted to determine whether TDM signaling requires an additional receptor such as TLR1 or TLR6 . The structure of TDM , however , does not include di-acylated lipids that signal via TLR2/6 heterodimers or tri-acylated lipids that signal via TLR1/2 heterodimers [29] . SRA has been demonstrated to modulate TLR signaling [30] , [31] and macrophages from both SRA- and MARCO-deficient mice have skewed cytokine responses in response to TLR agonists [16] . It is not entirely clear , however , if the SRs are able to signal directly or if they have an indirect function in the signaling pathway , for example , by phagocytosing and clearing TLR agonists . In order to test whether MARCO might be signaling directly , we created mutants that lacked the cytoplasmic domain , and thus any putative signaling motifs , of MARCO . The cytoplasmic region of MARCO has not been experimentally demonstrated to have any residues that are essential for cell signaling and indeed , apart from the putative myristoylation site at residues 41–46 , does not contain any potential signaling motifs identifiable by scanning various protein motif databases ( D . M . E . Bowdish , unpublished results ) . After confirming that these constructs were expressed on the surface of transfected CHO-K1 and HEK293 cells , we determined that they could indeed bind to TDM-coated beads and were in fact able to reconstitute TDM-induced NF-κB signaling ( Fig . 4 ) . Our receptor-ligand interaction assay results , however , show that MARCO alone is not sufficient for TDM binding , and that other receptors present on the surface of RPMφ must cooperate with MARCO for effective binding to occur ( Fig . S1 , Text S1 ) . Signaling induced by TDM may be independent of the phagocytic function of the scavenger receptors , as macrophages from the SRA−/− , MARCO−/− and DKO mice did not have any detectable defect in phagocytosis of TDM-coated beads ( data not shown ) . These data are consistent with a role for MARCO as a “tethering” receptor for TDM , perhaps extracting individual lipids and presenting them to the CD14/TLR2 complex ( Fig . 11 ) , but MARCO itself appears to lack a direct signaling function . Our observation that MARCO is the preferred receptor for TDM may explain why some macrophage populations respond robustly to TDM , while others do not . For example , RPMφ , which express high levels of MARCO , respond strongly to TDM , whereas BMMφ and RAW264 . 7 cells , which express SRA but not MARCO , produce a minimal amount of pro-inflammatory cytokines in response to TDM . We therefore propose that MARCO is the preferred receptor for TDM , although the less avid interaction between SRA and TDM can also facilitate signaling through TLRs . This hypothesis is consistent with the work of Ozeki et al . in which it was shown that SRA can bind to TDM in vitro and plays a role in suppressing TNF-α production by alveolar macrophages or Kupffer cells in response to TDM-coated wells [32] . Because TDM is highly immunogenic , it is being studied as an adjuvant that boosts both humoral and cellular immune responses [33] , [34] , is a novel candidate for vaccine development [35] , and is used to mimic the pathogenesis of M . tuberculosis infection [36] . Indeed , our observation that MARCO-deficient macrophages are defective in pro-inflammatory cytokine production in response to either TDM or virulent M . tuberculosis is consistent with our hypothesis that TDM is the major immunogenic lipid associated with pro-inflammatory responses . It is likely that TDM is not the only ligand for the scavenger receptors on M . tuberculosis and further study is warranted to elucidate what these interactions might be; however , our data suggests that this interaction is a major component of the macrophage response to infection . We propose that TDM is a novel SR ligand that binds to MARCO with a higher affinity than SRA and that the TDM-induced pro-inflammatory response is mediated in large part via SR/TLR2/CD14 receptors . . These observations may explain why some macrophage populations respond more strongly than others to TDM and thus will provide novel insight into the role of the scavenger receptors in the pathogenesis of tuberculosis .
C57BL/6 , SRA−/− , MARCO−/− , MARCO−/− SRA−/− ( DKO ) , CD14−/− , C3H/HeN , C3H/HeJ and 129sv/ev mice were bred and housed at the University of Oxford , Cornell University , or University of Georgia . All SR knockout mice were created on the C57BL/6 background strain [15] , [17] , [37] . The SRA−/− and DKO mice are deficient in SRAI and SRAII . Unless otherwise stated , C57BL/6 mice were used as wild-type and were purchased from either Taconic or Harlan . TLR2/4 double-deficient mice were created by Shizuo Akira ( Osaka University ) , generously supplied by Lynn Hajjar ( University of Washington ) , and were bred at the Cornell University Transgenic Mouse Core Facility . All mice were housed in specific pathogen-free conditions and experiments were designed to use age- and sex-matched mice , between 5 weeks and 3 months of age . All animal experiments were approved by the ethics board of the university at which the experiments were performed ( i . e . University of Oxford or Cornell University ) . CHO-K1 cell line ( ATCC#CCL-61 ) was maintained in Ham's F12K medium ( Gibco ) supplemented with 10% heat-inactivated fetal calf serum ( HI-FCS , Hyclone ) , 2 mM L-glutamine , 1 . 5 g/L sodium bicarbonate , 100 U/ml penicillin , and 100 µg/ml streptomycin ( Gibco ) . CHO-K1 cells were transfected using Lipofectamine as per the manufacturer's instructions ( Invitrogen ) . The HEK293 cell line ( ATCC# CRL-1573 ) was provided by Cynthia Leifer , cultured in Dulbecco's Modified Eagle Medium ( DMEM; Gibco ) supplemented with 10% HI-FCS , 2 mM L-glutamine , 1 mM sodium pyruvate , 10 mM HEPES ( Gibco ) , 100 U/ml penicillin , and 100 µg/ml streptomycin . Transfected RAW 264 . 7 cells were maintained in RPMI , 10% HI-FCS , 2 mM L-glutamine , and 100 µg/ml ascorbic acid ( Sigma ) . BMMφ were cultured as described previously [9] and maintained in DMEM supplemented with 20% L-929 cell-conditioned media , 10% HI-FCS , 2 mM L-glutamine , 1 mM sodium pyruvate , 100 U/ml penicillin , and 100 µg/ml streptomycin . RPMφ were obtained by lavaging the peritoneal cavity with 10 ml cold phosphate-buffered saline ( PBS ) , re-suspending the peritoneal cells in complete growth medium ( DMEM supplemented with 10% HI-FCS , 2 mM L-glutamine , 1 mM sodium pyruvate , 100 U/ml penicillin , and 100 µg/ml streptomycin ) and allowing the macrophages to adhere to Petri dishes overnight . Non-adherent cells were then rinsed off before RPMφ were used for experiments . All media components were routinely tested for endotoxin by Limulus Amoebocyte Assay ( Cambrex ) . All cells were maintained at 37°C and 5% CO2 . . The monoclonal anti-MARCO clones ED31 ( anti-mouse MARCO ) and PLK-1 ( anti-human MARCO ) , and anti-SRA clone 2F8 were grown and maintained at the Gordon laboratory . Polyclonal anti-hMARCO antibodies were a generous gift from Dr . Timo Pikkarainen . Anti- mouse TLR2 and TLR4 antibodies were purchased from eBioscience . Rabbit antibodies for phosphorylated and total ERK1/2 ( Cell Signaling Technologies ) were used according to manufacturer's protocols . Peroxidase-conjugated goat anti-mouse or anti-rabbit antibodies ( Jackson Labs ) were used at 1∶200 for immunoblotting . AlexaFluor 488 or 594 goat anti-mouse IgG antibodies ( Molecular Probes ) were used as secondary antibodies for immunofluorescence . TDM from M . tuberculosis H37Rv strain and bovine-derived PG ( Sigma-Aldrich ) were resuspended in chloroform/methanol ( 2∶1 v/v ) at 10 mg/ml and stored at −20°C under nitrogen . Sterile 3 µm and 90 µm diameter polystyrene microspheres or 2 . 5 µm fluorescent polystyrene microspheres ( Polysciences ) were coated with TDM or PG as described previously [9] . Coated microspheres were washed and re-suspended in endotoxin-free PBS ( Invitrogen ) at 2% solids . It should be noted that the manufacturing protocol for these microspheres was changed by Polysciences in 2007 , resulting in less efficient coating of the polystyrene particles by lipids and therefore reduced immunostimulatory ability . Original results could be reproduced using 80 µm diameter polystyrene microspheres from Duke Scientific . Human TLR4 , MD2 , TLR2 , and CD14 plasmids were generously provided by Dr . Cynthia Leifer ( Cornell University ) . All plasmids were amplified and purified using Endo-free Maxi Prep columns ( Qiagen ) . The plasmids containing human MARCO ( hMARCO ) , mouse MARCO ( mMARCO ) , human SRAI and SRAII have been previously described [38] , [39] . Constructs of human MARCO that were missing the cytoplasmic tail ( N-tail hMARCO or Myr hMARCO ) were created by designing primers that amplified the transmembrane region of hMARCO and contained restriction enzyme sites . After amplification and restriction digest , the amplified fragment was sub-cloned into pcDNA3 . 1 ( Invitrogen ) . Stable cell lines expressing either hMARCO or the empty vector ( pcDNA3 ) were created in RAW264 . 7 cells by transfecting the cells per the manufacturer's directions ( GeneJuice , EMDbiosciences ) and selecting under G418 . Surviving cells were tested for MARCO expression by flow cytometry and immunoblot , and were cultured in 1 mg/ml G418 until use . CHO-K1 cells were transfected with plasmids encoding either MARCO or SRA as described above . Medium was removed and replaced with Opti-MEM ( Invitrogen ) . Non-opsonic bead binding was assessed by incubating the cells on ice for 30 min with or without inhibitors , then adding TDM or PG-coated , fluorescent , 3 µm diameter microspheres . After 30 min on ice , the cells were washed , fixed , and bead binding was assessed by microscopy . Dextran sulfate ( DxSO4 ) or chondroitin sulfate ( ChSO4 ) were used at concentrations of 100 µg/ml . HEK293 cells were seeded at 5×105 cells per well in 2 ml of medium per well in a 6 well plate overnight . HEK293 cells were transfected according to manufacturer's protocol using TransIT transfection reagent ( Mirus ) with 144 ng each of NF-κB-luciferase and β-galactosidase reporter plasmids , 30 ng each of TLR2 , CD14 , or TLR4 , 90 ng MD2 , and 300 ng MARCO or SRAI/II per well depending on the experiment . Total DNA was brought to 2 µg using empty vector ( pcDNA3 . 1 ) . Transfected cells were incubated for 24 hours before trypsinization and reseeding in 96 well plates ( one row of a 96 well plate from each well of the 6 well plate ) in 100 µl media/well . After another 24 hours , cells were stimulated with either 1 . 25×103 TDM- or PG-coated 90 µm polystyrene microspheres per well , or positive control ligands 1 µg/ml Pam3Csk4 ( Calbiochem ) or 100 ng/ml lipopolysaccharide ( Sigma ) as positive controls for TLR2 and TLR4 , respectively . Only data from experiments , in which positive responses to LPS and Pam3Csk4 were consistent between relevant transfectants , were used in order to assure functional TLR4 and TLR2 complexes were expressed at equivalent levels . In addition , only experiments in which equivalent levels of MARCO and SRA were expressed , as determined by FACS , were used . After 18 hours , transfected cells were lysed using Reporter Lysis Buffer ( Promega ) and lysates were analyzed for luciferase ( Promega ) and β-galactosidase ( Tropix ) activity using a Veritas luminometer ( Turner Biosystems ) . NF-κB activity ( relative light units ) was measured by dividing luciferase activity by β-galactosidase activity , and then fold activity was calculated by dividing TDM and PG results by medium only results . Statistical significance was determined using Student's t test . RPMφ , or CHO-K1 cells transfected with hMARCO for 24 hours as described above , were seeded onto cover slips at 1×105 cells per well in 500 µl of media . Washed , 3 µm diameter , carboxylated silica beads ( Kisker Biotech ) were re-suspended in 25 mg/ml of the heterobifunctional crosslinker cyanimide and incubated with agitation for 15 minutes . Excess cyanimide was removed by washing twice in 0 . 1 M sodium borate pH 8 . 0 ( Sigma-Aldrich ) . The beads were then cross linked to defatted BSA in a 10 mg/ml solution for 2 hours with agitation , followed by labeling with 2 µg/ml carboxyfluoresceinthiosemicarbazide ( Molecular Probes ) for 30 minutes with agitation . After washing with PBS , the beads were then passively coated with TDM as described above . Beads were added to cells at a ratio of 5∶1 , maintained at room temperature for five minutes , then incubated at 37°C for five minutes , before rinsing with PBS and fixing in 4% paraformaldehyde in PBS . Cells were blocked overnight at 4°C in staining buffer ( SB; 1% BSA , 1% heat-inactivated goat serum , 0 . 25% saponin in PBS ) , then incubated with 10 µg/ml primary antibody in SB for 1 hour at room temperature , washed three times with PBS , and then incubated with secondary goat anti-mouse antibody also for 1 hour at room temperature . Cover slips were rinsed three times with PBS , then quickly in double-distilled water before mounting using Prolong Gold Antifade medium ( Molecular Probes ) . Confocal images were taken using an Olympus Fluoview 500 confocal laser scanning imaging system equipped with argon , krypton , and He-Ne lasers on an Olympus IX70 inverted microscope with a PLAPO 60× objective ( Olympus America , Inc . ) . Confocal images were processed using Adobe Photoshop 6 . 0 ( Adobe Systems , Inc . , ) . For FACS analysis , cells were transfected with plasmids as described above and stained with either the appropriate MARCO- or SRA-specific antibodies or corresponding isotype controls as per standard protocols . To assess SR expression , BMMφ , RPMφ , and RAW264 . 7 cells were seeded in 6 well plates overnight in the appropriate media ( discussed above ) . The cells were lysed in RIPA buffer [50 mM Tris-HCl ( pH 7 . 4 ) , NP-40 1% , sodium deoxycholate 0 . 25% , NaCl 150 mM , EDTA 1 mM , PMSF 1 mM , and protease inhibitors ( Roche ) ] , then assessed for protein concentration by Bradford Assay ( Bio-Rad ) . Equivalent amounts of protein per sample were boiled in 2× non-reducing sample buffer for five minutes , centrifuged , and separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) . Samples were transferred to nitrocellulose membranes , and then blocked in 5% nonfat dry milk in 0 . 1% Tween-20 in PBS ( PBST ) overnight with gentle rocking . Both primary and secondary antibodies were diluted in blocking buffer , and applied to membranes for one hour at room temperature with gentle rocking , and with three 5 minute washes with PBST between incubations . For MAPK immunoblots , RPMφ were seeded at approximately 5×105 cells per well in a 24 well plate in R10 medium and allowed to adhere overnight . After 16 hours , the cells were washed once with PBS and the medium was replaced with 450 µl of warm R10 . RPMφ were stimulated with either 100 ng/ml of LPS , 1 µg/ml Pam3Csk4 , or 1 . 9×103 TDM- or PG-coated 90 µm diameter beads , or PBS as a vehicle control . After stimulation , the medium was removed and the cells were lysed using 200 µl of hot 2× reducing sample buffer . Lysates were placed on ice , syringed to shred DNA , then boiled for five minutes at 100°C , centrifuged and stored at −20°C until use . Equal volumes of sample were loaded per lane for SDS-PAGE . Proteins were transferred to nitrocellulose membranes and blocked with 5% nonfat dry milk in TBST [10 mM Tris–HCl ( pH 8 ) , 150 mM NaCl , 0 . 1% Tween-20] . The filters were then incubated overnight at 4°C with anti-ERK1/2-P antibodies ( Cell Signaling Technology ) . Anti-total ERK1/2 antibody was used to show equivalent lane loading . Immunoreactive bands were detected using horseradish peroxidase-conjugated goat anti-rabbit IgG antibodies ( Jackson Immunoresearch ) and chemiluminescence ( Pierce ) . BMMφ or RPMφ were counted and seeded at approximately 1×105 cells/well in 100 µl media/well in 96 well plates , or 4×105 cells/well in 500 µl media/well in 24 well plates . RAW264 . 7 constructs were counted and seeded at approximately 1×104 cells/well in 96 well plates . RPMφ were washed once with PBS to remove non-adherent cells and the media replaced with Opti-MEM . Macrophages were stimulated with either TDM or PG-coated microspheres , with dose normalization for bead surface area ( total bead surface area of 5×107 µm2/well ) , 100 ng/ml of LPS ( E . coli , Sigma-Aldrich ) , or 100 ng/ml Pam3Csk4 ( Calbiochem ) ; PBS was added as a vehicle control . After 24 h , the media were collected and the concentration of TNF-α was determined by ELISA as per the manufacturer's directions ( BD Bioscience or eBioscience ) . In experiments comparing RPMφ from different mouse genotypes , cells were stained with crystal violet to normalize for cell numbers . The crystal violet was solubilized with 1% SDS and the resulting supernatant was read on a plate reader ( OD 550 nm ) . TNF-α release is expressed as pg/ml/OD550 to normalize for cell numbers . RPMφ were collected from MARCO−/− , SRA−/− , and DKO mice as described above and seeded in equivalent numbers in 24 well plates , depending on the genotype with the lowest yield ( approximately 1×105 cells per well ) . Mid-log phase cultures of Mtb H37Rv strain were washed then added to the cells at an MOI of 5 in 250 µl of medium . After 24 hours the medium was filtered and analyzed for cytokines by ELISA . Differences between the means of experimental groups were analyzed using Student's t test . Differences were considered significant when P≤0 . 05 . | The causative agent of tuberculosis , Mycobacterium tuberculosis , has a lipid-rich cell wall that contains a high percentage of mycolic acids . These mycolic acids contribute to both the impermeable nature of the cell wall and to the immunostimulatory properties of the bacterium . Indeed , it has been known for over 50 years that trehalose 6 , 6′-dimycolate ( TDM/cord factor ) is the major immunogenic lipid of M . tuberculosis , which induces potent pro-inflammatory responses from macrophages , although the receptor has not been identified . We have demonstrated that the toll-like receptor ( TLR ) pathway is required for pro-inflammatory cytokine production in response to TDM; however , the TLRs alone , or in conjunction with known co-receptors , are not sufficient to induce a response . We demonstrate that the macrophage receptor MARCO , a scavenger receptor , is utilized preferentially to “tether” TDM to the macrophage and activate the TLR2 signaling pathway , and is used preferentially over the related SRA . Macrophages from MARCO−/− mice are defective in activation of TDM-induced signaling and subsequent pro-inflammatory cytokine production in response to both TDM-coated beads and virulent M . tuberculosis . By identifying the macrophage receptors involved in initial recognition we can now explain variable responses to TDM between different macrophage populations ( which differ in scavenger receptor expression ) , and have identified a novel co-receptor that may be involved in lipid presentation to TLRs . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"immunology/cellular",
"microbiology",
"and",
"pathogenesis",
"immunology/immunity",
"to",
"infections",
"microbiology/immunity",
"to",
"infections",
"immunology/innate",
"immunity"
] | 2009 | MARCO, TLR2, and CD14 Are Required for Macrophage Cytokine Responses to Mycobacterial Trehalose Dimycolate and Mycobacterium tuberculosis |
Chagas disease , resulting from infection with the parasite Trypanosoma cruzi ( T . cruzi ) , is a major cause of cardiomyopathy in Latin America . Drug therapy for acute and chronic disease is limited . Stem cell therapy with bone marrow mesenchymal cells ( MSCs ) has emerged as a novel therapeutic option for cell death-related heart diseases , but efficacy of MSC has not been tested in Chagas disease . We now report the use of cell-tracking strategies with nanoparticle labeled MSC to investigate migration of transplanted MSC in a murine model of Chagas disease , and correlate MSC biodistribution with glucose metabolism and morphology of heart in chagasic mice by small animal positron emission tomography ( microPET ) . Mice were infected intraperitoneally with trypomastigotes of the Brazil strain of T . cruzi and treated by tail vein injection with MSC one month after infection . MSCs were labeled with near infrared fluorescent nanoparticles and tracked by an in vivo imaging system ( IVIS ) . Our IVIS results two days after transplant revealed that a small , but significant , number of cells migrated to chagasic hearts when compared with control animals , whereas the vast majority of labeled MSC migrated to liver , lungs and spleen . Additionally , the microPET technique demonstrated that therapy with MSC reduced right ventricular dilation , a phenotype of the chagasic mouse model . We conclude that the beneficial effects of MSC therapy in chagasic mice arise from an indirect action of the cells in the heart rather than a direct action due to incorporation of large numbers of transplanted MSC into working myocardium .
Chagas disease is a serious public health problem in all Latin American countries [1] , where it is estimated that 15–16 million people are infected with the its causative agent , the parasite Trypanosoma cruzi ( T . cruzi ) [2] . Although T . cruzi is endemic in Latin America , thousands of people are infected in Europe , United States , Canada , among other countries , due to migration of infected people [3] , [4] . Approximately one-third of individuals with Chagas disease develop a symptomatic chronic phase decades after the infection , of which 90% develop heart disease and the other 10% are affected by gastrointestinal diseases [5] . Chronic Chagas heart disease is a progressive , fibrotic inflammatory cardiomyopathy that results in permanent heart damage [6] . This heart damage leads to dilation and cardiac arrhythmia , and ultimately to congestive heart failure , which is the primary cause of death in chronic Chagas heart disease patients [7] , [8] . For more than 40 years , the only treatment option for Chagas disease in the acute phase has been the anti-parasitic drugs nifurtimox and benznidazole . However , these drugs have side effects and lead to parasite resistance [9] . In the chronic phase , when congestive heart failure ensues , heart transplantation is often the only therapeutic option , which is also fraught with many problems . In this complex scenario , where an estimated 20 , 000 people die of chronic Chagas heart disease each year [1] , cell therapies appear as an alternative solution . In a mouse model of chronic chagasic cardiomyopathy ( CCC ) we have previously shown that mononuclear cells from the bone marrow decrease inflammation and fibrosis , reduce or reverse right ventricular dilation and significantly restore gene expression pattern to that of control , non-infected hearts [10]–[12] . However , given the established role of the immune system in the physiopathology of Chagas disease [13] and the immune modulatory properties of bone marrow mesenchymal cells ( MSC ) [14] we hypothesized that MSC could be an optimal cell type for therapy in chagasic cardiomyopathy . In addition , preliminary studies with mononuclear cells from chronic chagasic patients have revealed a diminished colony forming capacity ( unpublished data ) , which can compromise autologous therapy . Due to the immune privileged characteristics of MSC , these cells can be used as an allogenic product [15] . Furthermore , previous studies with cellular therapy have focused primarily on the chronic phase of the disease and data about the effect of cellular therapy at early stages , such as 1 month after infection , was not previously evaluated . Thus , we wanted to examine the hypothesis that cell therapy is effective at earlier stage of the disease . Therefore , in this study we describe the use of cell tracking strategies following labeling of MSC with nanoparticles to investigate migration of intravenously transplanted cells in an acute murine model of T . cruzi infection . Furthermore , we correlated MSC migration with glucose metabolism and morphology of heart in chagasic mice by small animal positron emission tomography ( microPET ) .
All experiments were performed on adult male CD-1 mice in accordance with the U . S . National Institutes of Health Guide for the Care and Use of Laboratory Animals ( NIH Publication No . 80-23 ) , approved by the Institutional Animal Care and Use Committee of the Albert Einstein College of Medicine . To obtain bone marrow cells , tibias and femurs of approximately 8 week old mice were isolated , the epiphyses were removed , the bones were individually inserted in 1 mL automatic pipette polypropylene tips and then put in 15 mL tubes . The bones were centrifuged at 300× g for 1 min and the pellets suspended in Dulbecco's modified Eagle's high glucose medium ( DMEM; Invitrogen Inc . , Carlsbad , CA ) , supplemented with 10% fetal bovine serum ( FBS; Invitrogen Inc . ) , 2 mM l-glutamine ( Invitrogen Inc . ) , 100 U/mL penicillin ( Sigma-Aldrich Co . , St . Louis , MO ) , and 100 µg/mL streptomycin ( Sigma-Aldrich ) . The cells were plated in 100 mm culture dishes with supplemented DMEM and maintained in 5% CO2 atmosphere at 37°C . The medium was replaced 48–72 hrs after initial culture to remove non adherent cells and the adherent cells were grown to confluence before each passage . Medium was replaced three times a week . All experiments were performed on second or third passage cells . In the present study we used fluorescent nanoparticles called X-Sight nanospheres ( Carestream Health Inc . , Rochester , NY ) : X-Sight 761 ( 761 nm excitation and 789 nm emission ) and X-Sight 549 ( 549 nm excitation and 569 nm emission ) . We incubated MSC with a solution of 0 . 3 mg/mL X-Sight in supplemented DMEM in 5% CO2 atmosphere at 37°C for 4 hours . The labeled cells with X-Sight were then washed three times with phosphate-buffered saline ( PBS ) , trypsinized and centrifuged at 300× g for 5 min . Subsequently , the labeled cells were used for in vitro experiments or for tracking after transplant . The Brazil strain of T . cruzi was maintained by serial passage in C3H mice ( Jackson Laboratories , Bar harbor , ME ) . Eight to 10 week old male CD-1 mice ( Charles River ) were infected by intraperitoneal injection of 5×104 trypomastigotes in saline solution . One month after infection ( 1MAI ) these mice received a single dose of 3×106 MSC in 100 µL of PBS , or 100 µL of PBS via tail vein . For cell tracking , both control and chagasic mice received single doses of 3×106 labeled MSC via tail vein . The X-Sight 761-labeled MSC were visualized by the in vivo imaging system ( IVIS ) Kodak Image Station 4000MM PRO ( Carestream Health ) equipped with a CCD camera . For the fluorescence imaging , the machine was configured for 760 nm excitation , 830 nm emission , 3 min exposure , 2×2 binning and f-stop 2 . 5 . The acquired images were analyzed with the Carestream MI Application 5 . 0 . 2 . 30 software ( Carestream Health ) . For in vitro visualization of labeled cells , the MSC were grown on glass coverslips coated with 0 . 2% gelatin , incubated with X-Sight 549 for 4 hours , washed with PBS and fixed for 20 min in 4% paraformaldehyde . The cells were then observed by confocal microscopy to ascertain intracellular incorporation of the particles . Besides the IVIS technique , we tracked the labeled cells in the heart by microscopy . The same hearts used for IVIS tracking were fixed overnight in 4% paraformaldehyde , incubated in optical cutting temperature resin ( Sakura Finetek USA , Inc . , Torrance , CA ) and sliced in 5 µm frozen sections . The photomicrographs shown in this study were obtained using a Zeiss LSM 510 Duo confocal microscope . Mice were administered 300–400 µCi ( 12–15 MBq ) of [18F] fluoro-2-deoxyglucose ( 18F -FDG ) in 100 µL saline via tail vein and imaging was started 1 hour after injection . Imaging was performed on an Inveon Multimodality scanner ( Siemens Healthcare , Erlagen , Germany ) using its PET module . The mice were anesthetized with isoflurane inhalation anesthesia ( 2% in 100% oxygen ) administered via a nose cone . PET imaging was performed using the PET gantry which provides 12 . 7 cm axial and 10 cm transaxial active field of view . The PET scanner has no septa and acquisitions are performed in 3-D list mode . A reconstructed full-width-half-maximum ( FWHM ) resolution of <1 . 4 mm is achievable in the center of the axial field of view . After each acquisition ( approximately 3 minutes ) , data were sorted into 3D sinograms , and images were reconstructed using a 2D-Ordered Subset Expectation Maximization algorithm . Data were corrected for dead-time counting losses , scatter , random coincidences and the measured non-uniformity of detector response ( i . e . , normalized ) but not for attenuation . Analysis was performed using ASIPRO and IRW ( Siemens Healthcare ) dedicated software . The images were acquired from animals at 1 month after infection ( before treatment ) and 15 and 30 days after treatment with PBS or cells . Control group imaging was performed at every time point and for statistical analysis they were combined , thus , the technical number of control animals was 12 . In addition , as the collected data at 15 days after treatment was very similar to the collected data at 30 days , we combined these time points to increase the sample number in a group called 15–30d , thus , the technical number of animals were 8 and 9 for the PBS and MSC treated groups , respectively . Statistical significance was evaluated using one-way ANOVA with Newman-Keuls post-test for comparison among multiple groups . All calculations were done using GraphPad Prism 5 for Windows ( GraphPad Software , San Diego , CA ) and p<0 . 05 was considered as statistically significant . The data are presented as mean and the error bars represent the standard error of the mean .
By microscopy , we observed that all of the cells were labeled with X-Sight nanoparticles in vitro after 4 hours of incubation ( Figure 1A-A″ ) and because of that we considered that it was not necessary to quantify the number of labeled cells . By confocal microscopy , we confirmed that the nanoparticles were incorporated into the cell cytoplasm ( Figure 1B ) . We did not observe a cytotoxic effect of the X-Sight on cellular proliferation , evaluated by ki67 antibody , or on viability , evaluated by trypan blue staining ( data not shown ) . We analyzed the retention time of nanoparticles in vitro for up to 4 weeks using different cell plating densities , and we observed a substantial loss of fluorescence intensity over time ( Figure 1C ) , likely due to cellular proliferation as previously described by us [16] . The wells plated at 5×105 cells could not be monitored beyond 2 days because of high confluence and , consequently , cellular death . However lower plating densities allowed us to detect signals for up to 1–4 weeks . At 2 days after initial exposure to X-Sight for 4 hours a direct relation between cell number and fluorescence intensity was observed and a small number of cells , as low as 5×103 was detected ( Figure 1D and E ) . Control and chagasic mice at 1 month post infection received X-Sight 761-labeled MSC via tail vein . The images were acquired by IVIS , 2 or 15 days after cell transplantation . A weak signal from the labeled cells was observed in whole body images ( Figure 2A ) and a better signal was detected in ex vivo organs ( Figure 2B and H ) . Despite the filters being set to near infrared excitation and emission , a basal level of fluorescence was detected in control mice ( Figure 2A-CTRL ) , which did not receive cells , and similar fluorescence intensity was found in the infected mice that received unlabeled cells , indicating that neither the disease nor the unlabeled cells affected the basal fluorescence ( data not shown ) . From all analyzed organs , including heart , bladder , lung , liver , spleen and kidney we observed that approximately 70% of the fluorescence was localized in the lung , liver and spleen of control and chagasic mice ( Figure 2C–H ) . We also harvested some tissue samples , including leg muscle , white and brown adipose tissue but we did not observe cell migration to these tissues ( data not shown ) . When we compared the fluorescence intensity in the organs , 2 and 15 days after transplantation , a decrease of approximately 60% in total intensity was seen ( Figure 2C–H ) . Based on the possibility that nanoparticles might be released and secondarily label other cells , such as macrophages , we injected free X-Sight in the animals . In contrast to the distribution of labeled cells , free X-Sight was distributed more widely in whole body and about 60% of signal was found exclusively in the liver ( data not shown ) . It was interesting to note that despite the fluorescence signal in lung and spleen was stronger in control animals than in chagasic animal 2 days after therapy ( Figure 2E and H ) , in the heart we noticed the opposite . The quantification of fluorescence intensity showed that signal from chagasic hearts was statistically higher when compared to hearts obtained from control mice when ex vivo images of heart were compared 2 days after transplantation ( Figure 2C ) , suggesting the homing of cells to the , most affected tissue by the disease ( Figure 2C ) . In Figure 3A and B it is possible to observe with more detail the ex vivo images and graph of the hearts evaluated . In histological examination of the heart there were rare X-Sight-labeled cells in this tissue by confocal microscopy ( Figure 3C-C″ ) , which corroborates our data shown in Figure 2B–H where it is possible to note that only few cells migrate to this organ when compared to lung , liver and spleen . All analyzed organs in this experiment were weighed and wet weights were found not to be affected by the infection , except the spleen . The spleens of the chagasic animals were heavier than control , independent of PBS or MSC treatment for 1 month ( 84 . 35±2 . 5; 236 . 4±23 . 7 and 229 . 9±30 . 9 mg , for control or chagasic mice treated with PBS or MSC , respectively ) . MicroPET was performed with two main goals: to evaluate the glucose metabolism of the heart using the radioactive tracer 18F-FDG , and to measure the right ventricle ( RV ) dilation which is typical of the murine Chagas disease model [12] , [17] . Figure 4A represents a whole body image , in a horizontal plane , from an animal that received 18F-FDG . In high magnification , in a transversal plane , it was possible to visualize the heart of control ( Figure 4B ) , mice infected for 1 month and treated with PBS ( Figure 4C ) or MSC ( Figure 4D ) . Note the size of the RV in Figure 4C , from an untreated mouse . A high glucose activity was observed 1 month after infection ( without treatment ) in the LV ( Figure 4E ) as well as in the RV ( Figure 4F ) . However , 15–30 days after PBS treatment , a decrease in the glucose activity was observed in both ventricles what was not observed in the MSC treated groups . These data indicate that MSC can increase the glucose metabolism in infected hearts . Besides the heart , we observed a high glucose uptake in the brain , but we found no difference in uptake among the different experimental groups ( data not shown ) . When the RV area was measured , the dilation observed in the RV of mice 1 month post infection was significantly reduced by cell therapy 15–30 days after cell transplantation ( Figure 4G ) .
There are two types of cell therapy approaches that have been applied to animal models of Chagas disease: bone marrow mononuclear cells in mice [10]–[12] and co-cultured skeletal myoblasts with MSC in rats [18] . In human clinical studies of patients with end-stage heart failure due to Chagas disease the administration of autologous bone marrow mononuclear cells did not improve cardiac function [19] . The transplant of skeletal myoblast was associated with cardiac arrhythmias due to their inability to form electric coupling with cardiac myocytes [20] . Thus , these cells are not recommended for a disease with high incidence of arrhythmias , such as Chagas disease . On the other hand , MSC electrically couple to host cardiac myocytes and they have been suggested as a better cell type for cardiac therapy than other cell types , such as skeletal myoblasts [15] . MSC is an immune privileged cell type which can interact with cells of both the innate and adaptive immune systems and release trophic factors [14] , [21] . Hence , MSC might modulate the inflammation and reverse the tissue damage caused by T . cruzi infection . However there is no previous report of therapy for Chagas disease using only MSC . The present study thus pioneers the analysis of MSC therapy and biodistribution of these cells in infected mice . In contrast to myocardial infarction which causes a regional damage , Chagas disease affects the heart globally . Therefore , systemic delivery of MSC has been chosen for small animals infected with T . cruzi since multiple local injections into several heart areas would be expected to generate tissue damage [22] . Thus , it is very important to identify the preferential sites of MSC migration and correlate with their effects in cardiac function . In the present study an efficient visualization of X-Sight-labeled MSC was obtained and a small cell number as low as 5×103 could be detected in vitro by IVIS . However , there was a rapid decrease in fluorescence intensity over time . Based on our previous study with MSC labeled with superparamagnetic oxide iron nanoparticles [16] , cellular proliferation seems largely responsible for the signal decrease observed in vitro . Regarding cell homing to the site of infection , we observed that migration was significantly higher to the hearts of infected mice when compared to controls . MSC migration to the damaged tissue has been reported by several authors in different models such as tumors [23] , arthritic joints [24] , middle cerebral artery occlusion [25] and myocardial infarction [26] . Although MSCs reside in specialized niches their perivascular location allows global access to the tissues and when they migrate to an injured region they may secrete large amounts of immune regulatory and trophic bioactive factors [21] . The exact mechanisms and molecules involved in migration of MSCs to areas of inflammation are unknown . It is assumed that the process of MSC migration is similar to that of leukocytes [27] . This process comprises different types of molecules such as chemokines and their receptors , adhesion molecules and proteases [28] . The increase in chemokine concentrations at the site of inflammation is crucial for the MSC migration to injury site . The stromal cell-derived factor-1 ( SDF-1 ) is a member of the inflammatory chemokine family and stimulates the migration of various progenitor cells to injury site , including hematopoietic stem cells and MSCs , due to the CXC chemokine receptor type 4 ( CXCR4 ) [29] . Although some cells migrate to the heart , as visualized by IVIS and confocal microscopy , the quantity is negligible when compared to liver , lungs and spleen , where about 70% of the fluorescence intensity was found . Our findings are consistent with other studies where the majority of intravenous injected MSC were found in the liver , lung and spleen , including in dogs with myocardial infarction [26] and patients with cirrhosis [30] . The fluorescence intensity 15 days after transplantation was greatly reduced , which is likely due to nanoparticle exocytosis , cellular proliferation and/or death [31] . It has been shown that most MSC die within days or weeks of transplantation , yet their beneficial effects can be seen over a much longer term , suggesting a critical time window for MSC action [32] . The radioactive tracer 18F-FDG has been used to analyze the area of infarcted myocardium in mouse [33] and humans [34] and seems to be helpful in the diagnosis of infection and inflammation [35] . Here we used the 18F-FDG technique to evaluate glucose metabolism and morphology of the hearts by microPET . 18F-FDG uptake was increased in chagasic animals 15–30 days after infection and decreased to control levels 45–60 after infection ( 1MAI + PBS 15–30d group ) . Although these data differ from those of a previous study from our group , which showed the increase of 18F-FDG uptake in all time points studied [17] , in both studies there is a peak of uptake at 15–30 days after infection that corresponds to the peak of parasitemia , 25–30 days after infection in this model [36] . Although the incorporation of 18F-FDG is related to the general glucose metabolism , several authors suggest that the incorporation increase in tissues may also be related to inflammation and infection events [35] , [37] . It was interesting to note that MSC therapy increased 18F-FDG uptake in the heart , since the number of MSC present in this organ is very low , as revealed in the tracking experiment we suggest that the MSC induced increase in 18F-FDG uptake is due to the known effects of MSC in damaged tissues , such as enhanced angiogenesis , stimulation of mitosis in stem and progenitor cells , recruitment of circulating stem cells , inhibition of apoptosis and/or change in extracellular matrix composition [14] , [38] . We performed western blot analyses of heart tissue which reveled that MSC did not modify inflammatory proteins , such as interferon-γ ( INF- γ ) and interleukin 1β ( IL-1β ) and 10 ( IL-10 ) in chagasic animals at a time point 1 month after infection with 1 month of therapy ( total of 2 months of infection ) . Evaluating another time point of treatment ( at 2 months after infection ) we did not observe alterations in these proteins at 1 month after treatment ( total of 3 months of infection ) either; however , we did note differences in INF- γ and IL-10 due to MSC therapy after 2 months of treatment ( total of 4 months of disease ) . Thus , despite the fact that we did not observe an immunomodulation after 1 month of therapy , we have obtained evidence that MSCs are able to immunomodulate after a longer term ( data not shown ) . The remodeling of the right ventricle ( RV ) has been shown to be a characteristic phenotype of the chagasic mouse model used by our group [12] , [17] , [36] , [39] . RV dysfunction was described as a predictor of mortality in patients with chagasic cardiomyopathy [40] . Regarding the heart morphology evaluation by microPET , we observed that the RV dilation caused by T . cruzi infection was reduced after cellular therapy . This result demonstrates that MSC therapy is able to reduce the RV dilation in Chagas disease model and corroborates with another study from our group , in which magnetic nuclear resonance was used to show that RV dilation was reduced after bone marrow mononuclear cell transplantation [12] . To summarize , this study is the first to use MSC for therapy and cell tracking in chagasic mice . It was interesting to note that despite a very small number of cells migrating to the heart when compared to the other organs , a statistically significant preferential migration to the damage heart was observed . Since the vast majority of the intravenous injected cells migrated to lung , liver and spleen we suggest that the beneficial effect observed by MSC cell therapy in chagasic mice is due to an indirect action of the cells in the heart rather than a direct action by incorporation of large numbers of MSC into the working myocardium . | Chagas disease , resulting from infection with the parasite Trypanosoma cruzi , is a major cause of heart disease in Latin America . Treatment options are limited to a small number of drugs that were developed more than four decades ago and which have various drawbacks . Stem cell therapy with bone marrow mesenchymal cells ( MSCs ) has emerged as a novel therapeutic option for cell death-related heart diseases , but efficacy of MSCs has not been tested in Chagas disease therapy . Due to the established role of the immune system in the physiopathology of Chagas disease and the immune modulatory properties of MSC we hypothesized that MSC could be an optimal cell type for therapy in chagasic cardiomyopathy . Therefore , in this study we have used cell tracking strategies following labeling of MSCs with nanoparticles to investigate migration of transplanted MSCs in a murine model of Chagas disease , and have correlated MSCs migration with cardiac function in chagasic animals by small animal positron emission tomography imaging technique . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"biotechnology",
"medicine",
"diagnostic",
"medicine",
"physiology",
"biology",
"anatomy",
"and",
"physiology",
"cardiovascular"
] | 2012 | Mesenchymal Bone Marrow Cell Therapy in a Mouse Model of Chagas Disease. Where Do the Cells Go? |
When taking a blood meal on a person infected with malaria , female Anopheles gambiae mosquitoes , the major vector of human malaria , acquire nutrients that will activate egg development ( oogenesis ) in their ovaries . Simultaneously , they infect themselves with the malaria parasite . On traversing the mosquito midgut epithelium , invading Plasmodium ookinetes are met with a potent innate immune response predominantly controlled by mosquito blood cells . Whether the concomitant processes of mosquito reproduction and immunity affect each other remains controversial . Here , we show that proteins that deliver nutrients to maturing mosquito oocytes interfere with the antiparasitic response . Lipophorin ( Lp ) and vitellogenin ( Vg ) , two nutrient transport proteins , reduce the parasite-killing efficiency of the antiparasitic factor TEP1 . In the absence of either nutrient transport protein , TEP1 binding to the ookinete surface becomes more efficient . We also show that Lp is required for the normal expression of Vg , and for later Plasmodium development at the oocyst stage . Furthermore , our results uncover an inhibitory role of the Cactus/REL1/REL2 signaling cassette in the expression of Vg , but not of Lp . We reveal molecular links that connect reproduction and immunity at several levels and provide a molecular basis for a long-suspected trade-off between these two processes .
Malaria is a mosquito-borne parasitic disease affecting annually an estimated 250 million people , of which close to 1 million ( mostly children in sub-Saharan Africa ) succumb to the disease ( World Health Organization fact sheet #94 , April 2010; http://www . who . int/mediacentre/factsheets/fs094/en/index . html ) . Several Plasmodium species cause malaria , the most deadly being P . falciparum transmitted mainly by the Anopheles gambiae mosquito . As mosquito females require a blood meal to produce eggs , feeding on a malaria-infected host simultaneously activates oogenesis and triggers immune responses to malaria parasites . In the midgut , ingested Plasmodium gametocytes differentiate within minutes into gametes . After fertilization , zygotes rapidly transform into ookinetes , i . e . motile cells that traverse the midgut epithelium between 16 and 48 h post infection ( hpi ) . Once they reach the hemolymph-bathed basal side of the midgut , ookinetes round up and transform into oocysts , protected capsules within which asexual multiplication of the parasite takes place . Previous studies have established that the ookinete is the parasite stage most vulnerable to the mosquito immune response [1] , [2] . As a consequence of this response , most mosquito species efficiently eliminate all the invading ookinetes , thereby aborting the parasite cycle [3] . In a few parasite/mosquito combinations , up to 20% of ookinetes survive and the disease can be further transmitted . A number of mosquito humoral antiparasitic proteins have been characterized ( reviewed in [4] ) . The molecularly best characterized and phenotypically most prominent defense pathway mediating the killing of Plasmodium berghei in A . gambiae involves a thioester-containing protein ( TEP1 ) homologous to vertebrate complement factor C3 [2] , [5] , [6] . Depletion of TEP1 by RNA interference ( RNAi ) renders mosquitoes hypersusceptible to Plasmodium infections , resulting in abnormally high infection levels . Two leucine-rich repeat ( LRR ) proteins , LRIM1 and APL1C , act as TEP1 control proteins to stabilize the mature form of TEP1 in the hemolymph [7] , [8] and show the same RNAi phenotype as TEP1 in P . berghei infections [9]–[12] . The depletion of either protein results in precocious deposition of TEP1 on self tissues and completely aborts its binding to the ookinetes [7] . Therefore , it appears that LRR proteins regulate maintenance of mature TEP1 in circulation; however , the factors that control TEP1 targeting to the parasite surface remain unknown . Simultaneously to the midgut crossing by ookinetes , the physiology of the mosquito is profoundly modified by a blood meal in preparation for the laying of a clutch of eggs . Within 2 to 3 d after a blood meal , the massive ovary growth allows maturation of 50–150 oocytes , a process called vitellogenesis ( reviewed in [13] ) . The blood meal provides the mosquito with amino acids and lipids that are transferred through midgut cells to the hemolymph and signal via the Target of Rapamycin ( TOR ) pathway to initiate massive synthesis of nutrient transport proteins in the mosquito fat body [14] . These transport proteins include the lipid transporter lipophorin ( Lp , AGAP001826 ) ( also known as apolipoprotein II/I or retinoic and fatty acid binding protein , RFABG/P ) and vitellogenin ( Vg , AGAP004203 ) , a precursor of the yolk storage protein vitellin . Both proteins are secreted into the hemolymph and transported to the ovaries . Vg is a large phospholipoglycoprotein encoded in A . gambiae by a small family of nearly-identical genes . Insect Vg harbors potential sites for lipidation , glycosylation , and phosphorylation and is internalized by developing oocytes where it is proteolytically cleaved to generate vitellin , a nutrient source for the developing embryo ( reviewed in [15] , [16] ) . Lp , encoded by a single transcript and post-translationally cleaved , is composed of two subunits of 250 and 80 kDa that together scaffold a lipidic particle . Similar to vertebrate low- and high-density lipoproteins ( LDL and HDL , respectively ) , mosquito Lp particles contain a core of fatty acids and sterols , surrounded by an outer leaflet of phospholipids [17] , [18] . These particles function to deliver lipids and fatty acids to energy-consuming tissues , including rapidly growing imaginal discs in larvae , muscles , and the ovary in adult females [19] . In addition to lipids , Lp particles serve as a vehicle for morphogen proteins in the imaginal discs of Drosophila larvae [20] . Interestingly , human HDL has been shown to host a fraction of complement factor C3 [21] as well as trypanosome-killing protein complexes [22] . In mosquitoes , recent studies [23]–[25] have implicated Lp in both mosquito reproduction and Plasmodium survival . In particular , experimental depletion of Lp by RNAi inhibited oogenesis and also reduced the number of developing Plasmodium oocysts in the mosquito midgut [23] . This could point to a nutritional requirement for Lp in the early stages of parasite development . Indeed , Lp has recently been detected by in vitro approaches inside developing P . gallinaceum oocysts , suggesting that it provides parasites with a source of lipids [26] . An intriguing alternative explanation is that the increasing levels of Lp following a blood meal may negatively impact mosquito immunity against parasites . Artificially blocking the physiological rise in Lp levels would then allow the immune system to exert its full strength against the parasite . In the mosquito fat body , two distinct pathways are required for optimal expression of proteins involved in vitellogenesis: ( i ) the nutrient-sensing TOR pathway and ( ii ) a hormonal cascade that oversees production of 20-hydroxyecdysone [14] , [27] , [28] . Furthermore , in Ae . aegypti mosquitoes infected with microbes and Plasmodium , the NF-κB factor REL1 positively regulates expression of Lp and its receptor [24] , suggesting that the NF-κB pathway may also contribute to the regulation of oogenesis in addition to its known role in mosquito immunity [29]–[31] . However , our understanding of how oogenesis and immunity impact each other remains incomplete: on one hand depletion of Lp strongly inhibits development of P . gallinaceum; on the other hand over-expression of Lp resulting from the depletion of the REL1 inhibitor Cactus in Ae . aegypti is insufficient to rescue the complete block in parasite development [24] . Here , we investigated the role of the two major nutrient transport proteins Lp and Vg in mosquito antiparasitic responses using a common laboratory model of malaria transmission: A . gambiae mosquitoes infected with the GFP-expressing rodent parasite P . berghei [32] . We show that similarly to Lp , Vg depletion reduces parasite survival in mosquito tissues . Strikingly however , Lp and Vg are no longer required for parasite survival if TEP1 is depleted , suggesting that the low parasite survival phenotype associated with the Lp/Vg knockdowns requires TEP1 function . We propose that Lp and Vg exert distinct non-redundant roles in reproduction and immunity: Lp is crucial for oogenesis and is required for normal Vg expression after an infectious blood meal , whereas Vg contributes to oogenesis and negatively impacts TEP1 binding to the ookinetes . We suggest that the reported negative impact of Lp depletion on ookinete survival is indirect and is mediated by reduced levels of Vg . We further demonstrate that the NF-κB factors REL1 and REL2 limit the expression of Vg after an infectious blood meal . These results reveal an unexpected network of interactions whereby Plasmodium killing in mosquitoes is potentiated by NF-κB pathways at two levels: ( i ) activation of anti-Plasmodium genes and ( ii ) inhibition of the expression of the nutrient transport protein Vg .
Lp knockdown causes a decrease in parasite loads and simultaneously arrests oogenesis [23] . We examined whether the Lp knockdown phenotype requires the antiparasitic factor TEP1 . To this end , we compared the numbers of surviving parasites in single TEP1 or Lp knockdown mosquitoes and in double TEP1/Lp knockdowns by injecting double-stranded RNA ( dsRNA ) resulting in RNAi . Four days after dsRNA injection , mosquitoes were fed on a mouse infected with GFP-expressing parasites . Mosquitoes were dissected 8 to 10 d later to gauge prevalence of infection and mean oocyst numbers per midgut ( Figure 1A , Figure S3A ) . As reported earlier , Lp silencing strongly reduced the number of developing oocysts . Strikingly , silencing TEP1 at the same time as Lp annihilated the effect of Lp silencing , i . e . yielded the high oocyst numbers typically observed upon silencing of TEP1 alone . Therefore , the low oocyst counts observed in Lp-depleted mosquitoes are not due to a nutritional dependence of ookinetes on Lp-derived lipids but are a consequence of TEP1 activity . This result also suggests that the increased parasite killing in Lp-depleted mosquitoes takes place at the ookinete stage , since TEP1 binding does not kill oocysts . Further , these results imply that the loss of Lp renders ookinetes more vulnerable to TEP1-dependent killing . To explain these data , we initially hypothesized that Lp particles might physically sequester components of the TEP1 machinery in an inactive state , but a search for Lp-associated immune factors was unsuccessful ( with the notable exception of prophenoloxidase ) , suggesting that TEP1-containing complexes are not carried in the hemolymph by Lp particles ( see Text S1 and Figure S1 ) . To investigate whether the adverse effect on immunity is a specific property of Lp or may be manifested as well by other nutrient transport factors , we injected mosquitoes with dsVg and compared parasite development with dsLacZ and dsTEP1-injected mosquitoes . A 4-fold reduction in mean parasite numbers was observed in the dsVg group compared to dsLacZ controls ( p<0 . 001 , p<0 . 001 , p<0 . 05 , and p<0 . 05 depending on the replicate of this experiment; Figure 1B and Figure S3B ) . This effect was more profound than the effect of dsLp ( Figure 1A and 1E ) . We then examined whether depletion of the major yolk protein would compromise oogenesis . In contrast to Lp silencing , which resulted in total abortion of ovary development , roughly 50% of mosquito females still developed eggs after silencing of Vg compared to 80% in dsLacZ control mosquitoes ( Figure 1C ) , though ovaries that did mature usually contained only a few eggs bearing melanotic spots ( unpublished data ) . When given a chance to lay , Vg-silenced females did lay a few eggs , the majority of which never hatched ( unpublished data ) . The difference in strength between the Lp and Vg silencing phenotypes regarding egg development suggests either that Lp is more crucial than Vg for egg development or that the efficiency of Lp silencing is greater than the efficiency of Vg silencing . Residual Vg protein may allow the development of a few eggs in dsVg-treated mosquitoes . It is interesting to note that the strengths of the silencing phenotypes are reversed when considering parasite survival . To verify the efficiency of RNAi-mediated depletion of Lp and Vg , we used specific antibodies directed against the large and small subunits of Lp , and against Vg . RNAi silencing caused Lp and Vg protein amounts to drop below 10% of control levels ( Figure S2 ) . Subsequently , we systematically controlled for Lp and Vg silencing efficiency and noted that Vg depletion was somewhat more variable than Lp depletion , residual Vg protein sometimes approaching 20% of control levels ( unpublished data ) . Strikingly , this analysis revealed that the major protein bands detected in hemolymph samples by Coomassie staining of SDS-PAGE gels ( or of PVDF membranes after protein transfer ) correspond to the Vg and Lp signals detected by specific antibodies ( Figure S2 ) . We excised these easily visualized bands from Coomassie-stained protein gels and submitted them to MALDI mass spectrometry . The peptide mass spectra were searched against the NCBInr database . Each band from a triplet running between 160 and 200 kDa was unequivocally identified as Vg , and the bands running at ∼250 and 80 kDa were unequivocally identified as the large and small subunits of Lp , respectively . In addition , a protein running at ∼70 kD and showing an expression pattern identical to that of the ∼200 kD Vg band ( including after RNAi silencing ) was identified as the N-terminal fragment of the polypeptide encoded by Vg mRNA ( visible in Figures 3C , 4C , and S1 ) . This fragment was not recognized by our Vg antibody , raised against a C-terminal Vg fragment . Its existence is consistent with the cleavage of Ae . aegypti Vg prior to secretion [33]–[35] . No contaminating proteins were detected at these sizes in the mass spectrometry analysis . Therefore , Lp and Vg proteins can be readily visualized after hemolymph electrophoresis and Coomassie staining of SDS-PAGE gels even without immunoblotting . The efficiency of TEP1 silencing was also confirmed by immunoblotting ( Figure 1D ) . We next investigated whether Vg and Lp cooperate to sustain oogenesis and parasite development or are involved in independent processes . We performed double-knockdown experiments by simultaneously injecting dsVg-dsLp to compare to single injections of dsVg and dsLp as controls . As expected , dsLp completely blocked oogenesis and the same was observed in concomitant dsLp-dsVg knockdowns ( Figure 1F ) . Moreover , single dsVg ( p = 0 . 0001 ) and double dsLp-dsVg ( p<0 . 0001 ) knockdowns caused comparable reductions in oocyst counts; these reductions in oocyst numbers were stronger than in the single dsLp knockdown ( p = 0 . 024 ) ( Figure 1E ) . These results suggest that the influences of Lp and Vg on reproduction and immunity are balanced differently . Lp may be more crucial for oogenesis than Vg , whereas Vg influences Plasmodium survival more strongly than does Lp . In most experiments , the effect of Vg and Lp knockdowns on parasite counts did not appear to be additive ( Figures 1E , 2A , and unpublished data ) . Although this observation is not supported by strong statistical significance , it raises the possibility that the two proteins may be involved in a single process benefiting ookinete survival in the physiological situation . To determine whether similarly to Lp , the effect of Vg on parasite development required TEP1 function , we performed triple knockdown experiments by injecting combinations of dsTEP1 , dsVg , dsLp , or control dsLacZ . Again , total inhibition of oogenesis was observed in all dsRNA combinations that included dsLp , suggesting that oogenesis is not influenced by TEP1 function but absolutely requires Lp ( Figure 2B ) . In striking contrast , high parasite loads similar to that detected in the dsTEP1 single knockdown were obtained when TEP1 was depleted simultaneously to Vg ( unpublished data ) or to both Vg and Lp ( Figure 2A , Figure S3C ) . These findings imply that blocking the transport of lipids and vitellogenin-derived nutrients does not limit parasite survival when the immune defense is suppressed; instead , the observed reduction in parasite numbers in dsLp and dsVg knockdowns is dependent on TEP1 . We conclude that TEP1-dependent parasite killing is more efficient when Lp and/or Vg levels are low and that the TEP1-mediated immune pressure exerted by the vector is a bigger impediment to the establishment of a Plasmodium infection than nutrient availability . If this constraint is removed via TEP1 depletion , Plasmodium parasites can effectively exploit even reduced vector resources and proceed with the formation of viable oocysts . We next examined at which level Vg and Lp genetically interact with TEP1 . Binding of mature TEP1 to the parasite surface is one of the first steps leading to parasite killing; either increasing or reducing this event greatly influences the outcome of infection [31] , [36] . Therefore , we gauged the efficiency of TEP1 binding to ookinetes in dsLp- and dsLp-Vg-injected mosquitoes . At early time points ( 24 hpi ) TEP1 binding to ookinetes did not differ in the Lp or Lp-Vg -depleted versus control mosquitoes; but at 48 hpi 70% to 86% of ookinetes were TEP1 positive ( i . e . , either dead or moribund ) in dsLp- or dsVg-Lp-injected mosquitoes versus only 41% to 68% in dsLacZ controls ( Figure 2C and Table S1 , p = 0 . 005 or less by chi-square analysis ) . Thus , TEP1 binding to parasites is more efficient in the absence of Lp/Vg . This strongly suggests that physiological levels of Vg and Lp interfere with the efficient binding of TEP1 to ookinetes once the invasion phase is completed . To see if we could also detect an effect of Lp and Vg depletion at a later stage of parasite development , we examined oocyst growth . Strikingly , oocyst size 9 d after infection was markedly reduced when Lp , but not Vg , was depleted ( Figure 2D ) . In contrast to oocyst numbers , silencing TEP1 at the same time as Lp did not rescue oocyst growth ( unpublished data ) , indicating that the small oocyst size does not result from TEP1 activity in Lp-deficient mosquitoes . This supports the hypothesis that Lp contributes nutrients to oocyst development [26] . Therefore , Lp benefits Plasmodium development at two independent levels: an early effect favoring ookinete survival by protecting against TEP1-dependent killing , and a later effect favoring normal oocyst growth . The latter effect does not require Vg or TEP1 function . Previous work [7] , [31] has demonstrated that boosting mosquito basal immunity via depletion of the inhibitory IκB protein Cactus up-regulates components of the TEP1 pathway ( including TEP1 , LRIM1 , and APL1C ) and completely blocks parasite development . Therefore , we asked whether the knockdown of Vg and Lp could mimic the effect of Cactus depletion and elevate TEP1 expression levels , providing an explanation to the above observations . We silenced Lp and/or Vg and examined the transcript levels of TEP1 before and after blood feeding using quantitative real-time polymerase chain reaction ( qRT-PCR ) . Silencing of the two nutrient transport genes did not alter TEP1 expression ( Figure 3A ) . We then evaluated the effect of Lp and Vg silencing on TEP1 protein amounts and TEP1 cleavage in the hemolymph by immunoblotting using polyclonal anti-TEP1 antibodies . This analysis did not reveal any marked increase in the amounts of full-length or mature TEP1 protein ( Figures 3C and 1D ) . Surprisingly , silencing of Lp reproducibly lowered the expression of Vg mRNA ( Figure 3B and unpublished data ) . At the protein level , Lp depletion strongly reduced Vg levels at 47 h ( but not 24 h ) post-infectious feeding compared with the controls ( Figure 3C ) , confirming that Lp is indeed required for full Vg expression between day 1 and day 2 post-infectious blood-feeding . In contrast , the depletion of Vg had no effect on Lp expression ( Figure 3B ) or protein levels ( Figure 3C ) . The unexpected observation that Lp and Vg knockdown simultaneously arrests oogenesis and facilitates TEP1 binding to ookinetes led us to re-examine the previously observed striking phenotype of dsCactus , which boosts basal immunity while arresting oogenesis ( [31] and unpublished data ) . Depleting the IκB-like repressor protein Cactus increases the activity of NF-κB factors REL1 and REL2 , leading to elevated expression of TEP1 and other immune factors . Therefore , we investigated whether REL1 , REL2 , and Cactus influence the expression of Vg and/or Lp . To this end , mosquitoes were injected with either dsRel1 , dsRel2 , dsCactus , or co-injected with dsRel1-dsRel2 , dsRel1-dsCactus , dsRel2-dsCactus , and dsLacZ control . Mosquitoes were fed on an infected mouse , and subsequently , the expression of Vg and Lp was monitored by qRT-PCR . Strikingly , Vg expression was almost abolished in dsCactus mosquitoes at 24 hpi; conversely , the depletion of REL1 or REL2 at this time point elevated Vg expression above the levels in the dsLacZ control ( Figure 4A ) . Interestingly , concomitant silencing of Cactus/Rel1 and Cactus/Rel2 restored Vg expression to physiological levels ( Figure 4B ) , indicating that REL1 and REL2 contribute to the regulation of Vg expression . At the protein level , Vg amounts were unchanged at 24 h but strongly reduced 43 h after infectious blood feeding specifically in dsCactus-injected-mosquitoes ( Figure 4C ) , confirming the qPCR data and revealing a clear delay between mRNA and protein fluctuations . Thus , in the dsCactus background , while TEP1 expression is upregulated , Vg expression is directly or indirectly repressed by REL1/2 . Therefore , the Cactus protein affects TEP1 and Vg levels in opposite directions . We extended our analysis to Lp , but in contrast to the situation reported for Ae . aegypti [24] , its expression was unaffected by the knockdown of the NF-κB-like factors ( Figure 4A ) . Since Vg silencing alone , unlike Cactus silencing , is not sufficient to completely block oogenesis , other molecules required by developing mosquito oocytes may be regulated by Cactus in the same manner as Vg . Taken together , our findings uncover the complex phenotype of Cactus depletion . It leads to a lower level of Vg expression after a blood meal , thereby contributing to the arrest in oogenesis seen in Cactus knockdown mosquitoes . On the other hand , it stimulates the mosquito antiparasitic defense at least at two different levels: ( i ) by lowering the level of Vg , rendering TEP1-mediated killing more efficient , and ( ii ) by elevating the levels of TEP1 pathway proteins .
The first indication that nutrient transport after a blood meal influences mosquito susceptibility to P . berghei was provided by Vlachou et al . [23] , who demonstrated that experimental depletion of the lipid carrier protein Lp by RNAi reduces the number of developing oocysts in the mosquito midgut . Recently , these results were extended to P . falciparum [25] . However , how and at which stage of development the parasites were eliminated in Lp-deficient mosquitoes remained to be determined . We show here that the major yolk protein Vg shows a similar but more drastic knockdown phenotype than Lp on Plasmodium survival and that the Lp and Vg depletion phenotypes require the function of the immune factor TEP1 , which targets ookinetes for killing . Further , high numbers of parasites actually survive and turn into oocysts even in the context of Lp and/or Vg depletion , as long as TEP1 is also experimentally depleted . From these observations , we infer that physiological levels of both nutrient transport proteins following a blood meal somehow dampen the strength of the immune defense and protect ookinetes against destruction by the TEP1 pathway . The effects of Lp and Vg depletion on TEP1-mediated parasite killing are similar , and we find that Lp is required for the full induction of Vg expression on day 2 following an infectious blood meal . We therefore propose that Lp may indirectly affect ookinete survival by influencing Vg expression , while Vg impinges either directly or more closely than Lp on the TEP1-killing mechanism . The induction of Vg expression after a blood meal requires both the TOR pathway and ecdysone signaling [14] . It is unclear why Lp depletion reduces the expression of Vg after an infectious blood meal . One possible explanation is that an Lp shortage precludes ovarian follicle development , preventing the normal secretion of ecdysone by follicle cells; thus leading to the reduction in Vg expression . However , attempts to rescue the Lp silencing effect on Vg expression with exogenously provided 20-hydroxyecdysone were unsuccessful . As the lower level of Vg expression in Lp-deficient A . gambiae is reminiscent of the situation observed in adult Ae . aegypti mosquitoes malnourished during larval life [37] , it would be interesting to determine if Plasmodium survival is compromised in such malnourished mosquitoes in laboratory and field settings . The GFP-tagged P . berghei strain used in this study provides a good model and enables analyses of vectorial capacity that are much more demanding with wild malaria parasites . However , recent studies indicate that the mosquito response to P . berghei and to P . falciparum differ in important ways [10] , [38] . In addition , the P . berghei–A . gambiae model is an unnatural host-parasite association . Therefore , it will be important to see whether our observations hold true in the A . gambiae–P . falciparum relationship . Importantly though , the TEP1 pathway does limit P . falciparum survival in A . gambiae natural infections ( [39] and Levashina et al . , unpublished results ) and the Lp knockdown was shown to have similar effects in both systems [25] . What is the molecular basis of the negative effect of the two nutrient transport proteins on the TEP1 pathway ? We initially hypothesized that Lp-scaffolded lipidic particles could sequester components of the TEP1 pathway in an inactive state . However , TEP1 and its interacting partners LRIM1 and APL1C were not detectable in Lp extracts , suggesting that the Plasmodium-killing machinery is not carried by Lp particles . Instead , RNAi-mediated depletion of Lp and , more strikingly , of Vg resulted in more efficient TEP1 binding to the surface of ookinetes at 48 hpi , promoting their killing . One explanation could be that Vg ( and perhaps Lp , to a lesser extent ) are recruited to the parasite surface , where they might mask TEP1 binding sites . Consistent with this idea , fish vitellogenin has recently been found to bind microorganisms and to opsonize them for phagocytosis [40] . Mosquito Vg may behave non-productively in a similar manner and outcompete TEP1 from the ookinete surface . Alternatively , a physical interaction between TEP1 and Vg could inhibit TEP1 activity , a hypothesis that should be further investigated . Yet another possible explanation is that transient interactions of ookinetes with Vg might alter the lipid composition in the ookinetes' membrane , rendering them less visible to the TEP1 machinery . The parasite molecules to which TEP1 covalently attaches are currently unknown , but hydroxyl residues on surface lipids could be good targets for thioester-dependent TEP1 covalent binding . We further observed a retarded oocyst growth in Lp-deficient mosquitoes 9 d post infection . This phenotype was specific to Lp , as parasites developed normally in Vg-deficient mosquitoes . Therefore , Lp is a probable lipid source for developing oocysts . Indeed , Lp was detected inside P . gallinaceum oocysts in vitro , suggesting that oocysts tap some of the host's Lp for their development [26] . Taken together , Lp appears to regulate parasite development at two distinct stages by two independent mechanisms: ( i ) providing an indirect protection to ookinetes via regulation of Vg levels after a blood meal and thereby dampening TEP1 binding to ookinetes , and ( ii ) exerting a direct nutritional role by supplying lipids to growing oocysts . The quantitative RT-PCR and protein expression results reported here added the IκB/NF-κB-like factors Cactus/REL1 and REL2 , previously known to control immunity [29]–[31] , to the list of factors that influence Vg expression . We propose that Cactus depletion boosts TEP1 parasite killing by simultaneously increasing TEP1 expression [31] and decreasing the expression of Vg , in the absence of which TEP1-mediated killing is more efficient . Previously , the reason why Cactus depletion blocked oogenesis while boosting anti-Plasmodium immunity was unknown . Our results shed new light on this phenomenon by suggesting that Cactus activity is necessary for the expression of Vg , and probably of additional factors involved in vitellogenesis . Although many mosquito genes showing antiparasitic activity are induced by the NF-κB-like factors REL1 and REL2 [12] , [29]–[31] , [41] , it is currently unclear whether parasite invasion of mosquito tissues actually activates the NF-κB pathways . However , the expression of nutrient transport molecules is affected by signals arising from the parasite's invasion , in addition to being influenced by hormone signaling , the TOR pathway , and NF-κB factors . Indeed , ookinete invasion of the midgut induces Lp mRNA expression further than does an uninfected blood meal in A . gambiae and Ae . aegypti [23] , [24] . At the protein level , we did not observe a corresponding increase in Lp amounts using specific antibodies ( unpublished data ) , which may reflect consumption of the additionally produced Lp by parasites and/or by the midgut wound healing response to parasite invasion . This implies that Lp protein homeostasis is under tight physiological regulation . Conversely , Ahmed et al . [42] reported that parasite invasion reduces the abundance of the Vg transcript in A . gambiae , while Vg protein levels were only transiently reduced before accumulating in the hemolymph . Therefore , the production of both proteins is subjected to multiple physiological switches . The reported changes in Vg levels correlated with apoptosis of patches of ovarian follicular cells , which was prominent following infections and immune stimulation . Dying ovarian follicles stop secreting ecdysteroids and taking up Vg protein , which may explain both the drop in Vg transcription and the accumulation of Vg protein in the hemolymph [43] , [44] . It would be interesting to identify infection-dependent signals arising at the midgut and triggering ovarian follicle apoptosis . In Drosophila , pathogenesis is also reported to trigger cell death in ovaries [45] . In the presence or absence of an infection , activation of the Immune deficiency ( Imd ) pathway ( e . g . , by injection of dead bacteria ) negatively impacted oogenesis . This effect depended on the immune status , as oogenesis remained normal in Imd pathway mutants injected with dead bacteria [46] . The mosquito Cactus/REL1/REL2 NF-κB pathway is related to the Drosophila Toll and Imd immune pathways; its targets would therefore represent attractive candidates as modulators of mosquito reproduction . A full understanding of the interactions between reproductive and immune functions in mosquitoes will require a thorough study of the molecular pathways influencing the transcription of immune and vitellogenic factors , and how these pathways are affected by blood meals , immune defense , and parasite invasion . To our knowledge , Vg and Cactus are the first molecules reported to occupy a central position at the interface between reproduction and immunity , providing a molecular handle to further explore the long-suspected trade-off between these two processes .
Approximately 0 . 5 g of mosquito adults ( ca . 330 mosquitoes ) were roughly ground with a Polytron electric homogenizer in 2 ml ice-cold TNE buffer ( 100 mM Tris-HCl pH 7 . 5 , 0 . 2 mM EGTA , 150 mM NaCl ) + Complete protease inhibitors ( Roche ) . Debris were centrifuged at 4°C in a tabletop centrifuge . The supernatant was transferred to 2 . 2 ml ultracentrifuge tubes and spun for 3 h at 120 , 000 g at 4°C in a Sorvall ultracentrifuge equipped with an S55-S rotor . The cleared supernatant was recovered , completed with solid potassium bromide to a final concentration of 0 . 34 g/ml , overlayed with 0 . 5 ml TNE buffer+0 . 33 g/ml KBr , and centrifuged in 2 . 2 ml PET ultracentrifuge tubes ( Hitachi Koki ) at 250 , 000 g , 10°C , for at least 36 h . The top layer of fat was discarded and 5 or 6 fractions of 0 . 5 ml were carefully collected starting from the top . Lipophorin particles were present in the top fraction , while the majority of other proteins fractionated into the fourth . The top fraction of a potassium bromide gradient prepared using a scale-up of the above method was desalted on a Pharmacia PD-10 column according to the manufacturer's instructions . The two subunits of Lp were the predominant proteins in the extract according to Coomassie staining of an SDS-PAGE gel . Protein amount was quantified with a Bradford assay . Six-week-old female BALB/c mice were injected intraperitoneally with 40 µg of these lipophorin particles and 100 µg of poly I/C as adjuvant . Three injections were performed at 2-wk intervals . Four days prior to hybridoma fusion , mice with positively reacting sera were reinjected . Spleen cells were fused with Sp2/0 . Agl4 myeloma cells as described [47] . Hybridoma culture supernatants were tested at day 10 by ELISA for cross-reaction with purified Lp particles . Positive supernatants were then tested by Western blot on mosquito extracts . All ELISA-positive supernatants recognized peptides corresponding in size to either the large ( 250 kDa ) or the small ( 80 kDa ) Lp subunit . Specific cultures were cloned twice on soft agar . A hybridoma clone ( 2H5 , immunoglobulin subclass IgG2aκ ) recognizing the 80 kDa Lp subunit was selected and ascites fluid was prepared by injection of 2×106 hybridoma cells into pristane-primed BALB/c mice . The resulting antibody efficiently immuno-precipitated the 80 kDa Lp subunit and co-immunoprecipated the 250 kDa subunit . The identity of both immuno-precipitated subunits , excised from Coomassie-stained protein gels , was confirmed by mass spectrometry . Similarly , we prepared a monoclonal antibody ( 2C6 ) recognizing the large Lp subunit . Rabbit polyclonal antibodies specific to Vg were obtained by immunizing rabbits with a purified recombinant Vg fragment fused to GST . The Vg gene fragment used for protein production was amplified from mosquito cDNA using attB-site ( capital letters ) -containing primers GGGGACAAGTTTGTACAAAAAAGCAGGCTtcaagtttgtgctgcagcacaagcag and GGGGACCACTTTGTACAAGAAAGCTGGGTCCTAagcgcaagatggatggtagtttc . The PCR product was cloned into pDEST15 ( Invitrogen ) using the Gateway technology . Protein was produced in E . coli BL21-AI . 120 adult mosquitoes were severed by opening the thorax and abdomen cuticles with fine forceps and bled on ice in 1 ml IP buffer ( TRIS pH 7 . 9 50mM , NaCl 100 mM , EDTA 2 mM , BSA 0 . 1 µg/ml ) + Complete protease inhibitors ( Roche ) . Carcasses and cellular debris were removed by two successive 2 , 500 g centrifugation steps ( for 2 min at 4°C ) ; the extract was further cleared by three 16 , 500 g centrifugations ( 2 min each ) . The sample was pre-cleared for 1 h at 4°C under gentle rocking with 2 µg of an irrelevant mouse IgG2aκ antibody that was removed by incubation at 4°C with 35 µl protein A-sepharose slurry ( Pharmacia ) for 1 h followed by centrifugation . Supernatant was split in two aliquots , one subjected to a 1 h incubation with specific antibody and the other with a non-specific antibody of the same immunoglobulin class . 35 µl of protein A-Sepharose were added to each sample , further rocked at 4°C for 1 h , centrifuged . The supernatant was saved ( post-IP supernatant sample ) . Sepharose beads were washed 5×10 min in TE buffer with or without 500 mM KCl , successively . Lipophorin and associated proteins were eluted from the beads using SDS-PAGE sample buffer and submitted to Western blotting . At least 8 anesthetized mosquitoes were aligned on ice under the binocular microscope . Their proboscis was clipped with dissection scissors . Each mosquito was gently pressed on the thorax with forceps and the hemolymph droplet forming at the tip of the cut proboscis was collected into 1× sample ( Laemmli ) buffer . An hemolymph amount equivalent to that collected from 4 mosquitoes was loaded in each lane of SDS-PAGE gels . The 741 bp long HincII fragment of Vg1 ( AGAP004203 ) and the 431 bp long BspHI/BsgI fragment of Lp ( AGAP001826 ) were cloned from cDNA library clones into the pLL10 vector . RNAi constructs for TEP1 and NF-κB factors have been described ( Frolet et al . 2006 ) [31] . Potential cross-silencing effects of the chosen sequences were analyzed using the Deqor software ( [48]; http://deqor . mpi-cbg . de/ ) with the predicted A . gambiae transcriptome ENSEMBL database . DsRNA was synthesized as previously described [36] . A . gambiae susceptible G3 strain were maintained at 28°C , 75%–80% humidity , and a 12/12 h light/dark cycle . Two-day-emerged adult female mosquitoes from the same cohort were injected with 0 . 2 µg of dsRNA using a Nanoject II injector ( Drummond , http://www . drummondsci . com ) . Co-injection experiments were performed by injecting a double volume of 1∶1 mixtures of 3 µg/µl solutions of dsRNAs . Four days after dsRNA injection mosquitoes were fed on a mouse carrying P . berghei GFP-con 259cl2 as previously described [36] , [37] . Statistical significance was determined with a Kruskall-Wallis test for non-parametric data followed by Dunn's post-test . The indicated p values are those obtained with Dunn's test . The ovaries of dissected females were observed under the binocular microscope . Ovaries containing 3 fully grown eggs or more were scored as positive . Ovaries with only undeveloped oocytes or less than 3 fully grown eggs were scored negative . Total RNA from 10 mosquitoes was extracted with Trizol reagent ( Invitrogen ) before and after dsRNA injection or after blood feeding . 2–8 µg of RNA was reverse transcribed using M-MLV enzyme and random primers ( Invitrogen ) . Specific primers ( Table 1 ) were used at 300 nM for qRT-PCR reactions . Ribosomal protein L19 ( RPL19 ) served as an internal control to normalize gene expression . The reactions were run on an Applied Biosystems 7500 Fast Real-Time PCR System using Power SYBR Green Mastermix ( http://www . appliedbiosystems . com ) . In order to count the surviving GFP-expressing parasites , mosquito midguts were dissected between 7 and 10 dpi and prepared as previously described [36] , [37] and observed under a fluorescence microscope . To assess TEP1 binding to ookinetes , mosquito midguts were dissected at 18 , 24 , and 48 hpi , fixed in 4% formaldehyde at room temperature for 45 min , then washed with phosphate buffered saline , and stained with anti-TEP1 antibodies as previously described [31] , [36] . Parasite numbers and TEP1 labeling were scored using a Zeiss fluorescence microscope ( Axiovert 200M ) equipped with a Zeiss Apotome module ( http://www . zeiss . com ) . GFP-expressing parasites were considered live while dead parasites were GFP negative . Differential TEP1 staining on ookinete were gauged at 18 , 24 , and 48 hpi . At least three independent experiments were conducted per treatment group with a minimum of five mosquito midguts per treatment . For each midgut , all ookinetes visible in 4 fields covering most of the midgut were scored . Table S1 summarizes the ookinete counts from three independent experiments . Coomassie-stained protein bands excised from SDS-PAGE gels were digested with trypsin . Tryptic peptides eluted from the gel slices were subjected to MALDI mass measurement on an Autoflex III Smartbeam ( Bruker-Daltonik GmbH , Bremen , Germany ) matrix-assisted laser desorption/ionization time-of-flight mass spectrometer ( MALDI-TOF TOF ) used in reflector positive mode . The resulting peptide mass fingerprinting data and peptide fragment fingerprinting data were combined by Biotools 3 software ( Bruker Daltonik ) and transferred to the search engine MASCOT ( Matrix Science , London , UK ) . Peptide mass error was limited to 50 ppm . Proteins were identified by searching data against NCBI non-redundant protein sequence database . | Malaria annually claims the lives of almost 1 million infants and imposes a major socio-economic burden on Africa and other tropical regions . Meanwhile , the detailed biological interactions between the malaria parasite and its Anopheles mosquito vector remain largely enigmatic . What we do know is that the majority of malaria parasites are normally eliminated by the mosquito's immune response . Mosquitoes accidentally acquire an infection by sucking parasite-laden blood , but this belies the primary function of the blood in the provisioning of nutrients for egg development in the insect's ovaries . We have found that the molecular processes involved in delivering blood-acquired nutrients to maturing eggs diminish the efficiency of parasite killing by the mosquito immune system . Conversely , molecular pathways that set the immune system on its maximal capacity for parasite killing preclude the efficient development of the mosquito's eggs . Our results reveal some of the molecules that underpin this example of the trade-offs between reproduction and immunity , a concept that has long intrigued biologists . | [
"Abstract",
"Introduction",
"Results",
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"Material",
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"Methods"
] | [
"infectious",
"diseases",
"immunology/innate",
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] | 2010 | The Major Yolk Protein Vitellogenin Interferes with the Anti-Plasmodium Response in the Malaria Mosquito Anopheles gambiae |
Centromere sequences are not conserved between species , and there is compelling evidence for epigenetic regulation of centromere identity , with location being dictated by the presence of chromatin containing the histone H3 variant CENP-A . Paradoxically , in most organisms CENP-A chromatin generally occurs on particular sequences . To investigate the contribution of primary DNA sequence to establishment of CENP-A chromatin in vivo , we utilised the fission yeast Schizosaccharomyces pombe . CENP-ACnp1 chromatin is normally assembled on ∼10 kb of central domain DNA within these regional centromeres . We demonstrate that overproduction of S . pombe CENP-ACnp1 bypasses the usual requirement for adjacent heterochromatin in establishing CENP-ACnp1 chromatin , and show that central domain DNA is a preferred substrate for de novo establishment of CENP-ACnp1 chromatin . When multimerised , a 2 kb sub-region can establish CENP-ACnp1 chromatin and form functional centromeres . Randomization of the 2 kb sequence to generate a sequence that maintains AT content and predicted nucleosome positioning is unable to establish CENP-ACnp1 chromatin . These analyses indicate that central domain DNA from fission yeast centromeres contains specific information that promotes CENP-ACnp1 incorporation into chromatin . Numerous transcriptional start sites were detected on the forward and reverse strands within the functional 2 kb sub-region and active promoters were identified . RNAPII is enriched on central domain DNA in wild-type cells , but only low levels of transcripts are detected , consistent with RNAPII stalling during transcription of centromeric DNA . Cells lacking factors involved in restarting transcription—TFIIS and Ubp3—assemble CENP-ACnp1 on central domain DNA when CENP-ACnp1 is at wild-type levels , suggesting that persistent stalling of RNAPII on centromere DNA triggers chromatin remodelling events that deposit CENP-ACnp1 . Thus , sequence-encoded features of centromeric DNA create an environment of pervasive low quality RNAPII transcription that is an important determinant of CENP-ACnp1 assembly . These observations emphasise roles for both genetic and epigenetic processes in centromere establishment .
Centromeres are the chromosomal sites upon which kinetochores are assembled to ensure accurate segregation of sister chromatids into daughter cells . Most kinetochores are built upon a specialized type of chromatin in which canonical histone H3 is replaced by the histone variant CENP-A . Although the centromere-kinetochore complex performs conserved essential functions , and kinetochore proteins are generally conserved [1] , centromeric DNA is not conserved , even between related species , and a huge variety of centromere sequences and structures exist [2–5] . The point centromeres of budding yeast consist of 125 bp of DNA and utilize an essential centromere-specific DNA binding protein [6] . At the other extreme , the nematode , Caenorhabditis elegans , has holocentric centromeres , in which kinetochore proteins assemble at multiple loci along each chromosome arm [7 , 8] . The majority of centromeres studied to date are regional . Centromeres in various plant and animal species are composed of arrays of different types of satellite , repetitive sequences and transposable elements , for instance , human centromeres encompass several megabases of tandem repetitive arrays of alpha-satellite sequence [9–11] . Fission yeast centromeres represent another type of regional centromere , in which a unique central core of 4–7 kb is flanked by inverted repeat elements and blocks of relatively large repeat units , resulting in centromeres of 40–120 kb [12] . Even the centromeres of different chromosomes in individual species are not necessarily homologous; each Candida albicans centromere has a unique central core , whilst chicken and potato each utilize both repeat-rich and unique sequence centromeres [13–15] . Thus , functional centromeres are assembled on diverse types of sequences in different organisms and it remains unknown if there is a universal fundamental property that defines centromeric sequences . Abundant evidence indicates that centromeres are epigenetically regulated [16] . Although rare , neocentromeres have been observed in many species , forming on DNA sequences that do not normally possess centromere function and share no sequence homology with normal centromeres [17] . The best-characterized example in human is the neocentromere in 10q25 on the long arm of chromosome 10 that arose upon deletion of the centromere and loss of the entire alpha satellite array [18] . In S . pombe , neocentromeres form in close proximity to telomeres following the engineered deletion of a centromere [19] . Conversely , centromeres can be inactivated on dicentric human chromosomes despite the continued presence of alpha-satellite sequence at both centromeric loci [20] . In S . pombe one centromere on a dicentric chromosome can be inactivated by mechanisms such as heterochromatinisation or formation of a domain of histone hypoacetylation [21] . These and numerous other examples demonstrate that centromeric sequences are neither necessary nor sufficient for kinetochore assembly . The histone H3 variant , CENP-A acts as the epigenetic mark that specifies centromere identity [22–24] . CENP-A is found only at active centromeres , including neocentromeres , and is absent at inactivated centromeres . The forced recruitment of CENP-A either by directly tethering CENP-A or its chaperone ( HJURP ) to a non-centromeric locus leads to the accumulation of CENP-A and kinetochore proteins at that location [24 , 25] . It is thought that continued deposition of CENP-A at centromere regions through cell and organism generations involves a self-propagation mechanism in which CENP-A chromatin , or features of the kinetochore which is assembled upon it , are recognized and attract additional CENP-A [26 , 27] . In most organisms there is no obligate coupling of sequence and CENP-A assembly , yet kinetochores are normally assembled upon particular centromeric sequences in any given species [4] . This suggests that centromeric sequences possess underlying properties that promote CENP-A incorporation . Alternatively , the preponderance of particular sequences at centromeres could be driven by properties of CENP-A chromatin or kinetochores themselves [28] . However , centromeric DNA allows the de novo assembly of functional centromeres following its introduction into cells in many organisms . Alpha satellite arrays are able to direct the de novo assembly of centromeres when introduced into certain cell lines as naked DNA [29 , 30] . De novo assembly of centromeres also occurs when centromeric DNA from S . pombe is introduced into cells . However , de novo establishment does not seem to be a universal property: despite promiscuous neocentromere formation in C . albicans , transformation with bone fide centromeric sequences does not result in kinetochore assembly[13] . At the other extreme , many sequences introduced into the holocentric organism C . elegans appear able to assemble CENP-A chromatin [31 , 32] . Thus , the relationship between centromeric sequence and the establishment and maintenance of CENP-A chromatin is enigmatic . Transcription has received a lot of attention as a possible contributor to assembly of CENP-A chromatin . Transcripts emanating from centromeric regions have been detected in many organisms , including maize , human , rice , budding yeast , fission yeast and tammar wallaby [33–38] . Interfering with the chromatin status or transcriptional properties of centromeric repeats affects maintenance of CENP-A chromatin and segregation function on human artificial chromosomes ( HACs ) [39 , 40] . RNA Polymerase II ( RNAPII ) has been detected at mitotic mammalian centromeres where it may influence centromere function [38] . In fission yeast , transient H2B ubiquitylation may loosen centromeric chromatin to promote transcription and CENP-ACnp1 incorporation and defective reassembly of H3 chromatin behind elongating RNAPII aids CENP-ACnp1 incorporation [41 , 42] . However , although there are numerous tantalising hints that transcriptional activity contributes to centromere function or identity , much remains to be understood [33 , 38 , 43 , 44] . Here we investigate the contribution of DNA sequence to the establishment of CENP-A chromatin in fission yeast , an organism in which epigenetic mechanisms clearly influence centromere identity . Normally proximal heterochromatin is required to facilitate establishment of CENP-ACnp1 chromatin on centromere central domain sequences [45 , 46] . We show that this requirement can be bypassed by overexpression of CENP-ACnp1 and that central domain DNA is a preferred substrate for establishment of CENP-ACnp1 chromatin . We find that there is functional redundancy within the central domain but that sub-regions are non-equivalent in their ability to establish CENP-ACnp1 chromatin . Analysis of a 2 kb region capable of directing CENP-ACnp1 assembly indicates that it contains numerous transcriptional start sites , along with promoter elements , and that relatively high levels of RNAPII are recruited , despite low levels of transcripts produced , consistent with the presence of stalled RNAPII . Our observations suggest that redundant sequence features in the centromere central domain create a unique transcriptional environment that is permissive for CENP-ACnp1 establishment . Consistent with this , defective transcriptional elongation where stalled RNAPII is increased promotes the establishment of CENP-ACnp1 chromatin .
In wild-type fission yeast cells , de novo CENP-ACnp1 chromatin establishment on circular plasmid-based minichromosomes requires an outer repeat or tethered Clr4 histone H3K9 methyltransferase to form a block of heterochromatin in close proximity to central domain DNA from centromeres [45 , 46] . CENP-ACnp1 can also be deposited at other non-centromeric locations in the genome when it is overexpressed , however the level incorporated at these ectopic sites is much lower than that detected at natural centromeres [41 , 47] . To determine whether central domain DNA is a preferential substrate for the establishment of CENP-ACnp1 chromatin , plasmid pMcc2 bearing 8 . 5 kb of central domain from cen2 ( imr2-cc2-imr2 ) sequence , but no heterochromatic outer repeat sequences , was transformed into cells expressing additional GFP-CENP-ACnp1 ( Fig . 1A ) . All strains used have 6 kb of cen2 central domain DNA replaced with 5 . 5 kb of cen1 central domain DNA ( cc2Δ::cc1—Fig . 1A , S1 Fig . ) so that only 2 . 5 kb of normal cen2 central domain DNA remains at this modified cen2 ( imr2L , regions J , K , R; Fig . 1A ) . The resulting deletion of fragments L-Q from the cen2 central domain allows detailed and specific analysis of 6 kb of central domain DNA when borne by plasmid-based minichromosomes . Quantitative chromatin immunoprecipitation assays ( qChIP ) shows that CENP-ACnp1 chromatin does not assemble on regions L , M N , O or P when a plasmid ( pMcc2 ) containing the 8 . 5 kb cc2 sequence , but lacking heterochromatin , was transformed into wild-type cells [45] . However , when pMcc2 was transformed into cells over-expressing CENP-ACnp1 ( hi-CENP-ACnp1; ∼15 fold more than wild-type cells [41] ) , CENP-ACnp1 and the kinetochore proteins CENP-CCnp3 and CENP-KSim4 were easily detected over the central domain of pMcc2 by qChIP ( Fig . 1B-E ) . Importantly , these centromeric proteins were enriched on centromeric DNA but not on the plasmid backbone , indicating that CENP-ACnp1 chromatin assembles specifically on central domain DNA from centromeres ( Fig . 1 ) . The relative level of enrichment of CENP-ACnp1 and the other kinetochore proteins on different parts of pMcc2 suggests all proteins are distributed uniformly across this plasmid-borne central domain ( Fig . 1B-D ) . Furthermore , the levels of histone H3 associated with the L-P regions of pMcc2 were reduced in cells expressing additional CENP-ACnp1 compared to control cells ( Fig . 1E ) . We conclude that H3 chromatin is normally assembled on central domain DNA on pMcc2 in wild-type cells but CENP-ACnp1 chromatin assembles instead when pMcc2 is placed in hi-CENP-ACnp1 cells . To determine whether CENP-ACnp1 can become established on plasmids that are already assembled in chromatin , the pMcc2 plasmid was transformed into cells expressing wt-CENP-ACnp1 levels and subsequently crossed with hi-CENP-ACnp1 cells . qChIP analyses indicate that CENP-ACnp1 is initially absent from pMcc2 in the wt-CENP-ACnp1 parental strain and then becomes assembled in CENP-ACnp1 chromatin when transferred into the hi-CENP-ACnp1 environment , indicating that plasmid-borne cc2 initially assembled in normal ( H3 ) chromatin can be converted to CENP-ACnp1 chromatin ( Fig . 2A ) . In addition , a copy of cc2 ( 8 . 5 kb ) was inserted on the arm of the 530 kb Ch16 linear minichromosome which carries a complete cen3 [48] ( Ch16-cc2; Fig . 2B ) . When the expression of additional GFP-CENP-ACnp1 was repressed ( 0h+T ) , no CENP-ACnp1 was detected on cc2 . However , when GFP-CENP-ACnp1 was induced ( 48h-T ) both CENP-ACnp1 and CENP-CCnp3 were detected on cc2 ( Fig . 2B ) . Thus , cc2 borne on a linear minichromosome can be converted from a pre-chromatinised state to a CENP-ACnp1 state . Moreover , colony colour assays indicate that hi-CENP-ACnp1 expression induces increased loss of Ch16-cc2 , which is consistent with a second functional kinetochore being formed at cc2 on Ch16 ( Fig . 2C ) . Thus , Ch16-cc2 behaves as an inducible dicentric chromosome controlled by CENP-ACnp1 levels . It is possible that high levels of CENP-ACnp1 are continuously required to maintain CENP-ACnp1 on pMcc2 , or alternatively , once established , CENP-ACnp1 and kinetochore proteins may persist even when CENP-ACnp1 is returned to wild-type levels ( wt-CENP-ACnp1 ) . To investigate the maintenance of CENP-ACnp1 chromatin , pMcc2 was first transformed into hi-CENP-ACnp1 cells to allow the assembly of centromeric chromatin and subsequently these pMcc2-containing cells were crossed with wt-CENP-ACnp1 cells to transfer the pMcc2 plasmid into cells expressing wild-type CENP-ACnp1 levels . ChIP analyses show that CENP-ACnp1 persisted on the pMcc2 in this wild-type background ( Fig . 2D ) . Western analysis of extracts from Parental , F1 and F2 cells confirmed that GFP-CENP-ACnp1 was lost in F1 and F2 cells ( Fig . 2D ) . Thus , CENP-ACnp1 chromatin behaves as a true epigenetic entity in that once established it carries its own efficient propagation mechanism , persisting even though the original stimulus has been removed . More remarkably , this CENP-ACnp1 chromatin is maintained through 2 rounds of meiosis and at least 50 mitotic divisions . Thus central domain sequences are particularly receptive to the establishment and maintenance of CENP-ACnp1 chromatin . To determine if specific regions from the central domain of cen2 are required to establish CENP-ACnp1 chromatin , plasmids bearing different sub-fragments from cc2 were transformed into wt-CENP-ACnp1 or hi-CENP-ACnp1 cells ( Fig . 3 ) . We used an unbiased approach to divide the 8 . 5 kb cc2 into 1 kb regions ( J-R ) . Deletion of 1 kb from the centre of cc2 ( N ) does not affect CENP-ACnp1 establishment ( pΔN; Fig . 3A , compare with pMcc2 , Fig . 1B ) . Notably , CENP-ACnp1 incorporation on a plasmid carrying identical centromeric DNA as pΔN but with the right half ( O-R ) inverted relative to the native sequence , was less efficient ( pΔN-rev; Fig . 3B ) . Thus , the relative orientation of central domain sequences within cc2 influences the degree of CENP-ACnp1 deposition; directionality or the juxtaposition of certain sequences may be important for promoting CENP-ACnp1 incorporation . The central CENP-ACnp1 domain at endogenous fission yeast centromeres is composed of inverted imr repeats that flank the central core . ChIP analyses demonstrated that , in a plasmid-based establishment assay , the imr repeats are dispensable for de novo CENP-ACnp1 incorporation on the remaining central domain sequences ( pΔimr; Fig . 3C ) . Deletion of additional regions ( LMN ) of cc2 markedly decreased the efficiency of CENP-ACnp1 incorporation relative to pMcc2 and pΔN ( Fig . 3D; compare Fig . 1B , Fig . 3A ) , suggesting that either the LM region is critical for promoting CENP-ACnp1 incorporation or that the overall reduced centromeric DNA length diminishes CENP-ACnp1 deposition ( Fig . 3D ) . However , further investigation using plasmids bearing smaller cc2 fragments suggests that the specific sequences present have a more significant influence on CENP-ACnp1 deposition than the overall length of cc2 DNA present ( Fig . 3E , F ) . For example , pΔJM and pΔNR differ by only 500 bp , however , pΔJM incorporated substantially more CENP-ACnp1 than pΔNR ( Fig . 3F ) . We conclude that specific sequences from the central domain of fission yeast centromeres , combined with their overall length , promote the efficient de novo assembly of CENP-ACnp1 chromatin . It is possible that shorter fragments of centromere DNA from within the central domain can actively promote CENP-ACnp1 assembly but that because longer total lengths are required to stabilise incorporated CENP-ACnp1 the activity of shorter fragments cannot be detected . To address this possibility we selected two distinct sequences from the central domain of cen2 for analyses . The 2 kb OP region was present on all the pMcc2 derivatives with which we detected significant CENP-ACnp1 incorporation following transformation into hi-CENP-ACnp1 cells ( Fig . 3 ) . In addition , ChIP-seq analysis indicates particularly high CENP-ACnp1 nucleosome occupancy within OP at endogenous cen2 [49 and Fig . 4A] . In contrast , the 2 kb LM region appears to be dispensable for de novo CENP-ACnp1 assembly on pMcc2 derived plasmids and exhibits low CENP-ACnp1 nucleosome occupancy ( Fig . 3 , Fig . 4A ) . Initial tests showed that neither OP ( p1xOP ) nor LM ( p1xLM ) sequences alone were capable of inducing significant de novo CENP-ACnp1 incorporation when introduced into hi-CENP-ACnp1 cells ( Fig . 4B ) . This finding is consistent with a minimal length of central domain DNA being required for stable CENP-ACnp1 chromatin assembly and retention . To satisfy this apparent length requirement , the OP and the LM fragments were multimerised as tandem repeats to create 3xOP and 3xLM ( p3xOP , p3xLM; Fig . 4C ) . Remarkably , when transformed into hi-CENP-ACnp1 cells no CENP-ACnp1 was detectable on p3xOP whereas p3xLM allowed a reasonable level of CENP-ACnp1 incorporation ( Fig . 4C ) . This suggests that in isolation the OP region is unable to promote CENP-ACnp1 deposition even though in the context of an entire central domain it normally accepts CENP-ACnp1 and ends up with high CENP-ACnp1 nucleosome occupancy ( Fig . 4A ) . We note that the removal of the LM region from the central domain of pMcc2 derived plasmids greatly reduced the level of CENP-ACnp1 incorporated on OP ( compare pΔN Fig . 3A with pΔLMN Fig . 3D ) . Thus , in contrast to OP , the LM region appears to have the ability to induce CENP-ACnp1 deposition . To directly test this possibility , a single copy of LM was placed adjacent to two tandem copies of OP ( pLM-2xOP ) and transformed into hi-CENP-ACnp1 cells . High levels of CENP-ACnp1 were detected on both the LM and OP regions of pLM-2xOP ( Fig . 4D ) , thus the LM region has an innate ability to stimulate CENP-ACnp1 deposition on the OP region . A different arrangement of the same sequences ( pOPLMOP ) also attracted CENP-ACnp1 in hi-CENP-ACnp1 cells ( S2 Fig . ) . These analyses indicate that the 2 kb LM sequence contains all the features that are required to promote and accept CENP-ACnp1 assembly , and thus LM defines a 2 kb region of S . pombe centromeric sequence that allows the de novo assembly of CENP-ACnp1 chromatin . Plasmids bearing an entire central core domain flanked by outer heterochromatin repeats assemble functional centromeres when transformed into wild-type cells [45 , 50] . To determine if the 2 kb LM region imparts centromere function , a plasmid carrying the 3xLM tandem repeat adjacent to a 5 kb outer repeat heterochromatin forming element ( pH-3xLM ) was transformed into wild-type cells expressing CENP-ACnp1 at normal levels ( Fig . 5A ) . The establishment of functional centromeres in the resulting transformants was monitored by an ade6-based colony colour sectoring assay [51] . Minichromosomes carrying full-length cc2 and 5 kb of outer repeat heterochromatin were able to establish functional centromeres upon transformation ( Fig . 5B and S2 Fig . ) . pH-3xLM and pH-LM-2xOP transformants also established functional centromeres , but at lower frequency than pH-cc2 ( Fig . 5B and S2 Fig . ) . Differences in the ability of various constructs to form functional centromeres may reflect the particular configuration of sequences in individual minichromosomes . In contrast , pH-3xOP ( 3xOP flanked by heterochromatin ) was unable to establish functional centromeres . Thus the LM sequence in a 3x tandem array , flanked by heterochromatin , is sufficient to form functional centromeres . ChIP analyses confirmed that kinetochores were assembled on pH-3xLM since CENP-ACnp1 and the kinetochore proteins CENP-CCnp3 and CENP-KSim4 were enriched over the LM sequences at levels comparable to endogenous centromeres ( Fig . 5C ) . We conclude that the LM sequence within pH-3xLM not only promotes incorporation of CENP-ACnp1 into chromatin but also supports the assembly of a functional centromere . Nucleosome occupancy is known to be influenced by a combination of DNA sequence and the action of chromatin remodelers [52] . Primary DNA sequence itself influences nucleosome occupancy since DNA sequences with a high GC content and periodic dinucleotide patterns , that are devoid of poly ( dA:dT ) sequences , are strongly favored for nucleosome occupancy because of biophysical constraints that allow such sequences to wrap more easily around nucleosomes . These constraints have led to the development of algorithms that predict the probability of nucleosome occupancy [53 , 54] . In common with centromeres of many organisms , fission yeast centromeric DNA is AT-rich with a higher frequency of poly ( dA:dT ) tracts . It is therefore possible that H3 nucleosomes have a lower affinity for such sequences whereas CENP-A nucleosomes may be unperturbed by such AT rich DNA . To examine the underlying sequence specificity within centromeric DNA that favours the deposition of CENP-ACnp1 nucleosomes , the sequence of LM DNA was altered by randomisation using a 5 bp sliding window throughout the entire 2 kb element . This generated a synthetic LM sequence ( SynR-LM ) that is 62 . 6% identical to the wild-type LM sequence , retaining the same AT content and dinucleotide periodicity , and thus the same predicted nucleosome occupancy as the wild-type LM element ( Fig . 6A ) [55] . Synthesised SynR-LM assembled as a 3xSynR-LM tandem array was placed in the same plasmid backbone as p3xLM to generate p3xSynR-LM . p3xSynR-LM was transformed into wt-CENP-ACnp1 and hi-CENP-ACnp1 cells . In contrast to p3xLM , CENP-ACnp1 was not detectable on the shuffled LM sequence of pSynR-LM ( Fig . 6B , compare with Fig . 4B ) . These analyses demonstrate that preservation of nucleotide composition ( AT-content , dinucleotide periodicity ) and predicted nucleosome occupancy within an altered centromeric DNA is not sufficient to allow CENP-ACnp1 deposition . The fact that the natural 2 kb LM sequence is active whereas the artificial SynR-LM is inactive reveals that the primary sequence of wild-type centromeric LM DNA encodes properties that somehow allow its recognition in vivo and consequent de novo assembly of CENP-ACnp1 chromatin . Upon transformation into cells innate features within 3xLM sequence must allow it to be either immediately assembled in CENP-ACnp1 chromatin , or , initially assembled in H3 chromatin with subsequent remodelling that exchanges canonical H3 for CENP-ACnp1 . The process of transcription is obviously accompanied by chromatin remodelling and non-coding transcripts synthesised from within the central CENP-ACnp1 domains of fission yeast centromeres have been detected [37 , 42] . The transcription of central domain DNA might influence the assembly of CENP-ACnp1 chromatin . In cells expressing CENP-ACnp1 at wild-type levels , plasmid-borne central domain sequences are assembled in H3 rather than CENP-ACnp1 chromatin ( Fig . 1E ) . Higher levels of RNAPII are detected on plasmid-borne central domain sequences ( pMcc2 ) introduced into wild-type cells than when cc2 is assembled in CENP-ACnp1 chromatin on pMcc2 or at endogenous centromeres ( Fig . 7A , S3 Fig . ) . Although relatively high levels of RNAPII associate with the pMcc2 central domain when assembled as H3 chromatin in wild-type cells ( 10–30% of levels at act1+ ) ( Fig . 1E , Fig . 7A ) , the level of transcripts emanating from the central domain is very low ( <0 . 1% of act1+ ) , even when analysed in exosome defective cells ( dis3–54; Fig . 7B ) . Thus , although ample RNAPII is recruited to the central domain of pMcc2 few transcripts are generated , suggesting that transcriptional stalling occurs . To map transcriptional start sites ( TSSs ) within the LM and OP regions , 5’ RACE was performed on RNA extracted from dis3–54 exosome mutant cells harbouring p3xLM or p3xOP ( Fig . 7C , S4 Fig . ) . Many TSSs were identified within LM and OP , suggesting that these regions contain several promoters ( Fig . 7C ) . 200 bp regions from both LM and OP were tested for their ability to drive production of β-galactosidase when placed upstream of a lacZ reporter in fission yeast and as shown in Fig . 7D , the regions displayed promoter activity . Mutated or inverted versions of promoter region M2 did not promote transcription of LacZ ( S4 Fig . ) . Whilst most regions of LM and OP exhibit promoter activity that is lower than that of nmt81 control promoter , it is notable that region-O1 and region-P2 from OP have equivalent and 10-fold higher activity , respectively ( Fig . 7D ) . It is possible that the higher promoter activity possessed by some regions of OP may affect its ability to establish CENP-ACnp1 . We surmise that the central domain from cen2 is peppered with promoters that can drive the production of transcripts on both strands . Their relative arrangement along with the strength and pattern of transcription may affect CENP-ACnp1 incorporation . The progression of RNAPII is impeded by obstacles such as nucleosomes , DNA damage , bound proteins and by sequences that are intrinsically difficult to transcribe , causing transcriptional pausing , stalling or arrest [56] . RNAPII-associated proteins ease the passage of RNAPII through such impediments , contributing to the processivity of the polymerase [57] . TFIIS facilitates transcriptional elongation of stalled/backtracked RNAPII by stimulating cleavage of nascent transcripts [58–60] . Upon stalling an elongating RNAPII becomes mono- then poly-ubiquitylated on the largest Rpb1 subunit . A rescue pathway involving de-ubiquitylation by the ubiquitin hydrolase Ubp3 is deployed to restart stalled RNAPII [56 , 61] . Our analyses suggest that the central domain chromatin landscape contains numerous promoters on both strands and multiple TSSs . In addition , long poly ( dA:dT ) tracts are likely to be an intrinsically problematic sequence for RNAPII transcription and present a barrier to RNAPII elongation [62 , 63] . We reasoned that mutants that are defective in the response to transcriptional stalling might influence the ability of the central domain to become assembled in CENP-ACnp1 chromatin . To test this possibility , wild-type and TFIIS ( tfs1Δ ) mutant cells expressing hi-CENP-ACnp1 were transformed with pMcc2 . Surprisingly , slightly increased levels of CENP-ACnp1 were detected on pMcc2 in the tfs1Δ mutant compared to wild-type cells , suggesting that loss of TFSII promotes CENP-ACnp1 deposition ( S5 Fig . ) . Consistent with this , even when pMcc2 was transformed into tfs1Δ cells expressing wt-CENP-ACnp1 levels , CENP-ACnp1 was detected on the pMcc2 central domain ( Fig . 8A ) . In order to determine whether the effect on CENP-ACnp1 establishment was specific to tfs1Δ or a general consequence of increased RNAPII stalling , we also investigated if loss of the ubiquitin hydrolase Ubp3 , which normally rescues arrested RNAPII , affects CENP-ACnp1 deposition . Strikingly , CENP-ACnp1 was detected at high levels on central domain sequences in ubp3Δ cells transformed with pMcc2 . CENP-ACnp1 was also detected on p3xLM , but not p3xOP in ubp3Δ ( Fig . 8B , S6 Fig . ) . CENP-CCnp3 and CENP-KSim4 centromere proteins were also significantly enriched on pMcc2 in ubp3Δ cells ( S8 Fig . ) . These effects were not due to increased abundance of CENP-ACnp1 in tfs1Δ or ubp3Δ cells as protein levels were similar to wild-type cells ( S7 Fig . ) . In fact , a reduction in CENP-ACnp1 and CENP-CCnp3 levels was detected at endogenous centromeres in ubp3Δ , but not tfs1Δ cells ( S8 Fig . ) . Tfs1 and Ubp3 were previously reported to modulate RNAi-independent heterochromatin assembly [64] . To test whether the effect on CENP-ACnp1 establishment in tfs1Δ or ubp3Δ cells could be due to spurious assembly of heterochromatin on pMcc2 , H3K9me2 ChIP was performed . The level of H3K9me2 on pMcc2 in tfs1Δand ubp3Δ was similar to that on a negative control locus , act1+ , and assembly of CENP-ACnp1 on pMcc2 in these mutants was not dependent on the H3K9-methyltransferase Clr4 ( S9 Fig . ) . Thus , CENP-ACnp1 assembly on pMcc2 in the absence of TFIIS or Ubp3 does not result from induction by ectopic heterochromatin . If lack of TFIIS or Ubp3 hinders transcriptional elongation , an increased level of RNAPII would be expected on affected chromatin templates . Elevated levels of Rpb1/RNAPII were detected on the central domain of pMcc2 in tfs1Δ ( TFIIS ) and ubp3Δ cells ( Fig . 8C ) . In addition , increased levels of the elongation-specific Phospho-Ser2 form of RNAPII were observed on the central domain of pMcc2 in ubp3Δ cells , suggestive of failure to efficiently clear stalled RNAPII ( Fig . 8D ) . Thus , two mutants , which perturb the progress of RNAPII elongation complexes in different ways , lead to deposition of CENP-ACnp1 . These observations suggest that altering the transcriptional properties of the central domain chromatin through increased RNAPII stalling creates an environment that is permissive for establishment of CENP-ACnp1 chromatin in place of H3 chromatin .
It is thought that once established , CENP-A chromatin has the ability to be ‘self-propagating’ , and through the recruitment of factors that are themselves involved in deposition of CENP-A , it ensures its own maintenance [16 , 17 , 23 , 24 , 26 , 65] . Epigenetic inheritance can be defined as the propagation of a state in the absence of the initial inducer of that state . In this study , the inducer—overexpression of CENP-ACnp1—causes an event that would not normally occur , the assembly of CENP-ACnp1 chromatin on episomal centromeric DNA ( pMcc2 ) . When CENP-ACnp1-assembled pMcc2 is crossed from hi-CENP-ACnp1 cells into wt-CENP-ACnp1 cells , CENP-ACnp1 is propagated in the absence of the initial inducer through many generations and through meiosis . These observations further strengthen the evidence that CENP-A behaves as a bona fide epigenetic entity [24] . It is clear that both epigenetic and genetic factors influence CENP-A assembly . We have investigated the role of DNA sequence in establishment of CENP-A chromatin in fission yeast , an organism where analysis is not confounded by repetitive arrays of short satellite sequences . CENP-ACnp1 is normally restricted to the central domain of centromeres where it forms the basis for the kinetochore . Central domain DNA is a preferred substrate for establishment of CENP-ACnp1 chromatin upon overexpression , whilst other genomic loci do not support accumulation of high levels of CENP-ACnp1 [47] , and even vector DNA adjacent to the central domain is not a good substrate . Conditions and mechanisms that influence assembly of CENP-ACnp1 on naïve plasmid DNA are also able to convert pre-chromatinised cc2 present on episomal plasmids or linear minichromosomes . What makes central domain DNA a preferred site for CENP-ACnp1 assembly ? The lack of homology between cc2 and cc1/cc3 sequences suggests that it is not a simple case of specific sequence that is critical [66–68] . Our analyses indicate that there is functional redundancy within the central domain and no one particular sequence is either necessary or sufficient for CENP-ACnp1 establishment , consistent with previous findings [50] . Despite this redundancy it appears that there are inherent distinctions between different regions of cc2 . The 2 kb sub-regions , LM and OP , are functionally non-equivalent and consistently behaved differently when challenged to assemble CENP-ACnp1 chromatin . LM is competent to establish centromeric chromatin upon CENP-ACnp1 overexpression , contains sufficient information to make a functional centromere when placed next to heterochromatin ( pH-3xLM ) , and assembles CENP-ACnp1 chromatin in cells lacking Ubp3 . On the other hand , the OP region fails to become assembled in CENP-ACnp1 chromatin in all these situations , yet can accept CENP-ACnp1 when adjacent to one copy of LM , which apparently acts as an initiator . The ability of LM , but not OP , to substitute for full-length cc2 sequence indicates that not all sequences are equivalent and LM must contain all information necessary to make this region permissive for CENP-ACnp1 establishment . It is possible that the observed higher promoter activity observed in the OP region ( Fig . 7D ) prevents stabilisation of CENP-ACnp1 nucleosomes on this sequence . In common with many organisms , the central domain of S . pombe centromeres is AT rich and this property might contribute to the propensity of centromeric DNA to attract CENP-A [5 , 68] . S . pombe central domain DNA has an AT content of 72% ( genome average of 64% ) , as does the establishment competent LM sequence . However , other regions that alone fail to support CENP-ACnp1 establishment have a similar AT content , such as OP ( 71% AT ) and intergenic regions ( 72% AT ) . Moreover , randomisation of the LM sequence resulted in SynR-LM that , even with identical nucleotide composition ( 72% AT ) , was incompetent for CENP-ACnp1 establishment . Thus , high AT content alone , even when it mimics natural nucleosome positioning predictions , is not a defining factor in CENP-ACnp1 assembly . Together our observations indicate that rather than there being a specific critical sequence , central domain sequences encode unique properties capable of triggering or promoting the establishment of CENP-ACnp1 chromatin . Transcription-coupled remodelling is associated with the deposition of histone variants and could potentially contribute to the assembly of CENP-A chromatin [69 , 70] . However , the simple act of transcription cannot be sufficient to provide specificity to the deposition of CENP-A . Our observations suggest that the transcriptional landscape of the centromeric central domain is unusual: scattered promoters of various strengths resulting in pervasive low quality transcription and numerous TSSs on both strands , in conjunction with poly ( dA:dT ) tracts that are inherently difficult to transcribe are likely to cause collision between convergently transcribing RNAPIIs and pile-ups at difficult sequences [63 , 71] . The relatively high density of RNAPII on pMcc2 contrasts with very low levels of transcripts ( Fig . 7 ) , consistent with inefficient progress of transcription by RNAPII on cc2 , and many stalled elongation complexes . In addition , long tracts of poly ( dA:dT ) are known to disfavour nucleosome assembly , consistent with the apparently wide spacing of nucleosomes at endogenous centromeres [49 , 72] . These regions may be de facto nucleosome free regions , similar to those at promoters , allowing cryptic initiation of transcription to occur [72 , 73] . The randomized synthetic sequence SynR-LM that is a poor substrate for CENP-ACnp1 deposition has similar long A tracts , but transcription-related sequence-sensitive elements—such as promoters and transcription factor binding sites—would be destroyed . Thus , the central domain , due to its sequence-encoded properties , may produce a distinctive chromatin and transcriptional environment . CENP-ACnp1 chromatin does not assemble de novo on cc2 sequence alone in wild-type cells expressing normal CENP-ACnp1 levels [45] . Instead , we envisage that the unique transcriptional chromatin environment created by the cc2 sequence renders it permissive for CENP-ACnp1 establishment , but that establishment occurs only if other favourable conditions exist . CENP-ACnp1 is preferentially incorporated on these central domain sequences upon overexpression , when adjacent to heterochromatin , and in the absence of factors that usually enhance transcriptional elongation . Any explanation of CENP-ACnp1 chromatin establishment on central domain DNA must also account for how CENP-ACnp1 is incorporated instead of H3 . Serine 2 in the CTD heptad repeat of Rpb1 is phosphorylated in elongating RNAPII , and this Ser2P-Rbp1/RNAPII becomes ubiquitylated upon stalling [74–76] . The ubiquitin hydrolase Ubp3 normally acts as a proof-reading activity to prevent degradation of stalled but rescuable RNAPII [56 , 61] . Absence of Ubp3 compromises the processing of stalled RNAPII , resulting in the accumulation of ubiquitylated Ser2P-Rbp1/RNAPII complexes . We propose that such modifications contribute to the distinctive status of central domain chromatin , leading to recruitment of factors that promote CENP-ACnp1 deposition ( Fig . 8E ) . Alternatively , it may create an environment in which H3 nucleosomes are efficiently turned over/evicted , whereas CENP-ACnp1 nucleosomes are poorly evicted specifically in the context of stalled RNAPII . In cells lacking Ubp3 , severe or prolonged stalling , even with normal levels of CENP-ACnp1 , would provide extended opportunities for CENP-ACnp1 recruitment , or poor eviction of CENP-ACnp1 during prolonged stalling . TFIIS promotes transcriptional elongation by cleaving nascent transcripts in the context of stalled/backtracked RNAPII [57 , 58 , 77] . Although the effects of TFIIS deletion are more subtle than lack of Ubp3 , the accumulation of RNAPII correlates with assembly CENP-ACnp1 chromatin , supporting a mechanism where persistent RNAPII stalling within central domain triggers remodelling that results in CENP-ACnp1 deposition . In this model , when naïve central domain DNA ( pMcc2 ) is introduced into wild-type cells , transient stalling occurs but it is efficiently cleared with the aid of factors such as TFIIS and Ubp3 ( Fig . 8E ) . Because in wild-type cells CENP-ACnp1 levels are extremely low compared to histone H3 there would be little opportunity for CENP-ACnp1 to gain access to cc2 , and with efficient clearing of stalled RNAPII , CENP-ACnp1 would fail to accumulate in cc2 [78] . CENP-ACnp1 overexpression would increase the probability of interaction with the transiently stalled RNAPII in central domain chromatin , increasing the likelihood of recruitment . Alternatively , increased access coupled with poor eviction would lead to CENP-ACnp1 accumulation . In addition , CENP-ACnp1 nucleosomes themselves , which have distinct N-terminal tails that lack the conserved lysine residues of H3 whose modification aids transcription , are likely to present a greater barrier to transcription than H3 nucleosomes [79] . Thus , once incorporated , CENP-ACnp1 nucleosomes might exacerbate the poor transcriptional elongation , creating conditions permissive for recruitment of more CENP-ACnp1 in a self-perpetuating system . Longer regions of central domain DNA would have greater probability of triggering stalling events and thus be more likely to initiate the incorporation of CENP-ACnp1 . In the context of this model , heterochromatin could promote establishment of CENP-ACnp1 chromatin on adjacent cc2 sequence by drawing plasmids to sites of endogenous heterochromatin such as the spindle pole body where they would encounter a higher concentration of CENP-ACnp1 than non-heterochromatinized plasmids located in the nuclear interior [80] . Alternatively , heterochromatin-associated chromatin modifying activities may influence transcriptional elongation by RNAPII within cc2 , causing enhanced stalling and deposition of CENP-ACnp1[41] . Following establishment of CENP-A chromatin and kinetochore assembly , transcription could play a proof-reading role that evicts H3 deposited at centromeres during S phase [81] . Indeed , transcription and RNAPII have been detected at centromeres in mammalian cells and transcription/RNAPII may play a role in centromere integrity [33 , 34 , 38] . Transcription of human α-satellite arrays introduced as HACs is known to occur . Although CENP-A assembly is compatible with targeting of mild transcriptional activators , targeting of a strong transcriptional activator is deleterious [30 , 38 , 82] . Thus transcription and/or the transcription-coupled histone modifications detected at centromeres may promote CENP-A deposition at mammalian centromeres . In conclusion , we show that the sequence of fission yeast centromere central domain DNA is important only in so far as it encodes for certain properties that contribute to the region’s unusual chromatin and transcriptional landscape . Establishment of CENP-ACnp1 chromatin is driven by these sequence-encoded properties that when combined with the presence of nearby heterochromatin , overexpressed CENP-ACnp1 or increased RNAPII stalling , tips the balance in favour CENP-ACnp1 chromatin assembly . It seems likely that a similar combination of factors , which together favour CENP-A incorporation , must also contribute to the formation of neocentromeres at novel chromosomal locations .
Standard genetic and molecular techniques were followed . Fission yeast methods were as described [83] . Fission yeast strains are listed in Table 1 . Minichromosomes used in this study were transformed by electroporation . Transformants were selected by growth on PMG—ura—ade at 32°C . As circular minichromosomes lack heterochromatin and therefore centromeric cohesion , plasmids were maintained in cells by selection in medium lacking adenine and uracil . 3 independent colonies from each transformation were analysed for the presence of kinetochore proteins by chromatin immunoprecipitation ( ChIP ) . Plasmids bearing centromere fragments contained a minimal ars1 element to ensure efficient replication in S . pombe , in addition to selectable markers sup3-5 ( complements ade6-704 ) , ura4+ and KANR . 8 . 5 kb of central domain DNA ( cc2 plus inner part of imr2L and imr2R ) was cloned into the multiple cloning site as a SalI-NcoI fragment to create pMcc2 . Various sub-fragments of cc2 ( J-Q ) were amplified by PCR and cloned into the multiple cloning site as BamHI/BglII fragments . 5 . 6 kb of heterochromatin-forming outer repeat sequence was inserted adjacent to central domain sequences to test ability to form functional centromeres . A plasmid , pMC28 , bearing cc2 , a KAN resistance marker and an inverted ura4 sequence was constructed from pMcc2 . Linearisation of the plasmid at NotI within the inverted ura4 sequence allowed integration at ura4+ located on the arm of Ch16-m23:ura4+ . Ch16-m23: ura4+ is a derivative of Ch16 , a 530 kb minichromosome , itself derived from Chromosome III [48] . It also bears the ade6-216 allele which complements the ade6-210 allele present on endogenous Chromosome III by interallelic complementation . Integration of linearised pMC28 on Ch16-m23:ura4 allowed selection on the counter-selective drug 5-fluoro-orotic acid and G418 ( KAN ) . Cells that lost the Ch16-m23:ura4::cc2-KAN ( abbreviated as Ch16-cc2 ) became red on limiting adenine and were sensitive to G418 . For growth in liquid , cells containing Ch16-cc2 were grown in media lacking adenine . ChIP was performed as previously described [84] using anti-CENP-Acnp1 antibody , anti CENP-CCnp3 antibody , anti-CENP-KSim4 antibody , anti-H3 antibody ( ab1791; Abcam , ) , anti-H3K9me2 antibody ( T . Urano ) and anti-total RNA polymerase II ( 4F8; 61081 , Active Motif ) , anti-Rpb1-Ser2P ( 3E10; 61083 , Active Motif ) and analysed by qPCR . Primers are listed in Table 2 . P-values were calculated by standard t-test on 3 replicates between wild-type and mutant; p<0 . 05 was considered significant . For the establishment assay , cells were transformed with minichromosomes ( containing 5 . 6 kb of outer repeat sequence in addition to cc2 sequences ) , by electroporation with ∼200 ng of DNA and plated on selective medium . Resultant colonies were replicated onto rich medium containing limiting adenine . The presence of pale pink/white colonies indicates establishment of a functional centromere on the minichromosome . Establishment efficiency is calculated as percentage of these colonies divided by the total number of transformants . Colonies were streaked on limiting adenine plates to confirm the presence of sectoring that is indicative of centromere function . Quantitative PCR reactions were carried out in 10 μl volume , with 5μl Light Cycler 480 SybrGreen Master Mix ( Roche ) , 0 . 5μl each primer ( 10 μM ) and 3μl ChIP or total template . The data were analysed using Light Cycler 480 Software 1 . 5 ( Roche ) . 5’RACE-PCR was performed as previously described [37] . In brief , RNA was isolated with RNeasy mini/midi kit ( Qiagen ) according to the manufacturer’s protocol . Poly ( A ) containing RNA was purified from 500 μg of total RNA by affinity purification with biotinylated oligo-dT using PolyATtract mRNA Isolation Systems ( Promega ) . 5’RACE PCR was performed using SMARTer 5’/3’ RACE ( Clontech ) according to the manufacturer’s protocol . PCR products were then run on 1% agarose gel , purified and cloned into pGEM-T Easy vector ( Promega ) and subsequently sequenced . Reverse transcription reaction for 5’RACE and qRT-PCR was performed using Superscript III Reverse Transcriptase ( Invitrogen ) using RNA extracted from 3 independent colonies . For qRT-PCR , transcript levels were normalized over gDNA to take into account differences in copy number between plasmids and normalized relative to act1+ . LacZ assay was performed as described [85] . pREP81X-LacZ was digested with XhoI and PstI and the nmt81 promoter upstream of LacZ replaced with sequences from centromere 2 . Plasmids were transformed into wild-type and grown on minimal medium ( n = 3 ) . DNA was extracted as previously described [83] . The DNA was digested with BglII/SpeI or SphI/SpeI , run on a 1% agarose gel , blotted on nylon membrane ( Hybond N , Amersham ) and UV-crosslinked . The membrane was hybridized with DNA probes specific for central domain 1 or central domain 2 . To make the probes , PCR products were used as template in the labelling reaction using High Prime ( Roche ) . Primers sequences are listed in Table 2 . Western analysis was performed as described previously using anti-GFP antibody ( Roche ) and anti-H3 antibody ( ab1794-abcam ) [86] . The intensities of GFP and H3 signals were acquired using LICOR Odyssey Infrared Imaging System software ( Li-COR Bioscience ) . | The kinetochore directs the separation of chromosomes and is assembled at a special region of the chromosome—the centromere . DNA is wrapped around particles called nucleosomes , which contain histone proteins . The nucleosomes at centromeres are specialized , and contain the centromere-specific histone CENP-A . CENP-A nucleosomes form the platform upon which the kinetochore is built . Thus , CENP-A and centromere function go hand-in-hand . How the cell ensures that CENP-A is deposited at centromeres and not elsewhere is not well understood . We investigated the role that DNA sequence plays in defining centromere function in fission yeast . Our observations suggest that it is not the DNA sequence per se that is important for attracting CENP-A , but rather , the particular environment that the sequence creates . During transcription of centromeric DNA , RNA polymerase ( RNAPII ) appears to get stuck or stalled . Particular proteins—such as TFIIS and Ubp3—are known to help restart RNAPII so it can continue transcribing . We found that when cells lack Ubp3 or TFIIS , CENP-A becomes deposited on centromere sequences . We propose that persistent stalling of RNAPII on centromere DNA attracts factors that help deposit CENP-A . This study highlights the influence of DNA sequence in creating an attractive environment for CENP-A assembly . | [
"Abstract",
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] | [] | 2015 | Sequence Features and Transcriptional Stalling within Centromere DNA Promote Establishment of CENP-A Chromatin |
Access to health care is a major requirement in improving health and fostering socioeconomic development . In the People's Republic of China ( P . R . China ) , considerable changes have occurred in the social , economic , and health systems with a shift from a centrally planned to a socialist market economy . This brought about great benefits and new challenges , particularly for vertical disease control programs , including schistosomiasis . We explored systemic barriers in access to equitable and effective control of schistosomiasis . Between August 2002 and February 2003 , 66 interviews with staff from anti-schistosomiasis control stations and six focus group discussions with health personnel were conducted in the Dongting Lake area , Hunan Province . Additionally , 79 patients with advanced schistosomiasis japonica were interviewed . The health access livelihood framework was utilized to examine availability , accessibility , affordability , adequacy , and acceptability of schistosomiasis-related health care . We found sufficient availability of infrastructure and human resources at most control stations . Many patients with advanced schistosomiasis resided in non-endemic or moderately endemic areas , however , with poor accessibility to disease-specific knowledge and specialized health services . Moreover , none of the patients interviewed had any form of health insurance , resulting in high out-of-pocket expenditure or unaffordable care . Reports on the adequacy and acceptability of care were mixed . There is a need to strengthen health awareness and schistosomiasis surveillance in post-transmission control settings , as well as to reduce diagnostic and treatment costs . Further studies are needed to gain a multi-layered , in-depth understanding of remaining barriers , so that the ultimate goal of schistosomiasis elimination in P . R . China can be reached .
Health disparities exist in all societies and recent data suggest that they might be widening [1]–[4] . Yet , many have argued that the existence of disparities in health status is unfair , should reasonable actions exist for their avoidance or mitigation [1] , [4]–[10] . The concept of ‘fairness’ in health is related to ( i ) equality ( the state of being equal ) in health status; ( ii ) equality in health services; and ( iii ) equity ( the quality of being fair and impartial ) in health services [7] . Equality in health status may be impossible to achieve , given the vast array of behavioral , cultural , environmental , and genetic factors involved in generating each individuals' health outcome . Equality in health services is theoretically possible but , given inequalities in health status , this would strain finite resources and , hence , it is not desirable . Equity in health services , on the other hand , is characterized by universal access to essential health care and is key to achieving health for all [9] , [11] , [12] . Five inter-related dimensions of access to health care have been identified; availability , accessibility , affordability , adequacy , and acceptability of health-related goods , such as equipment and pharmaceuticals , and services , including prevention , diagnosis , and treatment [13] . Achieving health for all depends upon the context , policies , processes , institutions , and organizations that govern health-related goods and services , as well as the capacity among individuals , families , and communities to accrue assets and make use of them in times of need [10] , [13]–[15] . The People's Republic of China ( P . R . China ) has undergone profound social , economic , political , and institutional transitions in the past decades as the centrally planned economy shifted to a socialist market economy . With the Chinese economy growing at an annual rate of 9–13% for the past 30 years [16] , more than 210 million people were lifted out of poverty , benefiting from improvements in nutrition , drinking water , housing conditions , and health care . Within the health care system , major reforms included de-professionalization , de-centralization , de-regulation , commoditization , and economic restructuring [17] , [18] , creating both opportunities and challenges [19] , [20] . Vertical disease control programs are considered particularly vulnerable to such changes and , indeed , for schistosomiasis control in P . R . China , substantial disparities may have emerged among the population [21] , [22] . The Chinese government has been increasingly concerned with inequities and inequalities and , while the goals of generating economic growth and revenue remain , the creation of a peaceful and harmonious society is gaining prominence [23] . While the effects of the market-oriented reforms have frequently been analyzed with respect to rural primary health care in general , little is known regarding the specific case of schistosomiasis control [24]–[27] . The national schistosomiasis control program in P . R . China , launched in the mid-1950s , has reduced the prevalence of infection in humans by more than 90% through a multi-faceted integrated control approach , consisting of health education , environmental management , snail control , population screening , and large-scale treatment campaigns targeting humans and animal reservoir hosts , and the removal of bovine reservoirs and their replacement by mechanized agriculture [23] , [27]–[29] . Much of this accomplishment can be attributed to strong and sustained political commitment and economic investment , including a 10-year World Bank loan project ( WBLP ) implemented between 1992 and 2001 [28] , [30]–[33] . After termination of the WBLP , there were signs of re-emergence in some endemic areas , including an increase of advanced schistosomiasis japonica cases [28] , [33] , [34]–[37] . Among several reasons for the potential re-emergence of schistosomiasis , health systems reform has been identified as an important one [17] , [37] , [38] . Exploring systemic barriers to equitable and effective schistosomiasis control in P . R . China is of high importance . Here , we briefly review the history of the national schistosomiasis control program in Hunan Province , P . R . China . We then examine the five core dimensions of access to schistosomiasis-related health care in Hunan , using a framework proposed by Obrist and colleagues [13] . Emphasis is placed on perspectives from staff working at local anti-schistosomiasis control stations and designated schistosomiasis hospitals , and patients with advanced schistosomiasis japonica .
Ethical clearance for this study was obtained from the Medical Ethics Committee of Hunan Province and the Chinese Ministry of Health ( MoH ) . The aims and objectives of the study were explained to each participant and written informed consent was obtained before beginning each interview and focus group discussion ( FGD ) . All personal identifiers of the study notes and tapes were kept confidential and destroyed once the study was completed . Documents regarding policies and regulations associated with the prevention , diagnosis , and treatment of schistosomiasis were accessed from the following sources: ( i ) history of Hunan Province; ( ii ) history of health care in P . R . China; ( iii ) research on schistosomiasis control in Hunan Province; ( iv ) compilation of statistical data on schistosomiasis control in Hunan Province; ( v ) statistical data on schistosomiasis control in Hunan Province ( 1983–2001 ) ; ( vi ) atlas of schistosomiasis in Hunan Province; ( vii ) history of Hunan Institute of Parasitic Diseases; ( viii ) history of schistosomiasis control in Huarong County; and ( ix ) selected compilation of regulations on schistosomiasis control in Hunan Province . Government documents were accessed from ( i ) the Foreign Loan Office , Beijing; ( ii ) the Chinese MoH , Beijing; and ( iii ) the Hunan Provincial Office for Schistosomiasis Control . All unpublished data ( ‘grey literature’ ) were checked and validated through year-books of the Hunan Institute of Parasitic Diseases . A total of 145 key informant interviews and six FGDs were conducted in six schistosome-endemic counties in the Dongting Lake area , Hunan Province , between August 2002 and February 2003 . These were Lixian and Hanshou in Changde City , Nanxian and Yuanjaing in Yiyang City , and Huarong and Yueyang in Yueyang City . A mix of staff were selected through a purposeful sampling technique based on the value of their knowledge to the research to be undertaken; these included: ( i ) physicians or surgeons working in anti-schistosomiasis control stations who are acquainted with the diagnosis of advanced schistosomiasis; ( ii ) local staff involved in the administration and management of schistosomiasis control measures; and ( iii ) other schistosomiasis experts working at the Changde City Anti-Schistosomiasis Office and Institute , the Yiyang City Anti-Schistosomiasis Office and Institute , and Hunan Institute of Parasitic Diseases . Patients with advanced schistosomiasis japonica were selected by convenience sampling according to registration lists and medical history records at the local anti-schistosomiasis control stations and designated hospitals taking care of patients with advanced schistosomiasis . Hospital records were used to trace the location of non-hospitalized individuals with advanced schistosomiasis . Interviewing of staff and patients was undertaken within the anti-schistosomiasis control stations or hospitals . Non-hospitalized individuals with advanced schistosomiasis were interviewed at home or in their village of residence . Interviews were undertaken face-to-face with each participant by researchers and interviewees and lasted between 30 and 90 min each . Demographic , occupational , and socioeconomic variables were filled in on the interview form according to the answers of each individual interviewee , while other information was tape-recorded and notes were taken , with the consent of the participant . Interview guides were prepared collectively by the co-authors and according to themes emerging from the published literature and prior experience and expertise . Staff interviews consisted of 16 closed and three open-ended questions , including views of the current approach to schistosomiasis control , opinions regarding the organization , management , and financing of schistosomiasis control , perceived technical quality , and responsiveness as well as potential barriers to sustainable control . Patient interviews included questions on schistosomiasis-related knowledge , attitudes , and practice ( KAP ) , health-seeking behavior associated costs , experience of hospitalization , and patient satisfaction . The FGDs guide was prepared collectively by the co-authors , the contents of which included the evolution of schistosomiasis control , the dynamics of the endemic status of advanced schistosomiasis , and future recommendations for sustained control of schistosomiasis . With the consent of the participants , the FGDs were tape-recorded , transcribed into Chinese , and later translated into English . Transcripts were complemented with notes taken during the FGDs . For consistency , all interviews and FGDs were undertaken by the same interviewer , who had received training in sociology , anthropology , and communication . In addition , the interviewer was native to the region and was familiar with local dialects , customs , and values . Data were double entered , cross-checked , and validated . Quantitative data analyses were performed using SPSS version 16 . 0 ( Illinois , USA ) . A temporal trend analysis was carried out to examine changes in the endemic status of acute and advanced schistosomiasis in Hunan Province between 1956 and 2001 . Case detection , treatment , and cause-specific mortality rates were analyzed separately . Work space infrastructure , such as the number of anti-schistosomiasis control stations and institutes , and the number of beds within them , human resources , including full-time and temporary staff , and medical equipment were examined by a temporal trend analysis , as proxy variables for the availability of services . Interviews and FGDs were examined by content analysis . We created a code list , categorized all interviews and FGDs according to the code list , and entered the data into a MaxQDA 2007 database ( Marburg , Germany ) . Using the health access livelihood framework , we identified key concepts based upon the frequency with which they occurred within the database , and explored the relationships between them . Conceptual and relational analyses were conducted for explicit terms only; implicit terms were not examined . Demographic and socioeconomic variables were examined using χ2 test statistic .
Historical data suggested that the total area of land in the Dongting Lake area inhabited by Oncomelania hupensis hupensis ( the intermediate host snail of Schistosoma japonicum in P . R . China ) , peaked at 2 , 159 million m2 in 1958 . It gradually decreased to 1 , 756 million m2 in 1970 and to 1 , 121 million m2 in 1979 . Thereafter , the snail-habitat increased . A similar trajectory was observed for the snail-populated area outside of lake embankments that were constructed around the Dongting Lake to provide additional farmland and protection from Yangtze River floods ( Figure 1 ) . The estimated number of schistosomiasis cases in Hunan Province gradually decreased and stabilized at approximately 225 , 000 cumulative cases in 1993 . The number of patients with acute schistosomiasis japonica peaked in 1964 ( 7 , 572 cases ) . Twenty-five years later , in 1989 , the number of acute cases showed more than a two-third reduction ( 2 , 259 cases ) ( Figure 2 ) . With regard to the number of patients with advanced schistosomiasis japonica , no data were available prior to 1982 . A first peak appeared in 1989 with 13 , 000 cases . Between the late 1990s and 2001 , approximately 7 , 000 cases in total were recorded ( Figure 2 ) . The proportion of individuals with stunting and splenomegaly among the schistosome-infected population decreased over time . On the other hand , there was an apparent increase in liver cirrhosis and ascites , particularly among patients who concurrently had hepatitis . Splenectomy operations for advanced schistosomiasis patients were most common in the mid-1980s , with a subsequent declining trend ( Figure 3 ) . Mortality due to advanced schistosomiasis peaked in the early 1990s , decreasing and stabilizing thereafter ( Figure 3 ) . A total of 66 ( 62 or 95% male ) staff from anti-schistosomiasis control stations and 79 ( 58 or 73% male ) patients with advanced schistosomiasis were interviewed . Staff were mostly based at county level institutes ( n = 35 , 51% ) with more than a third of them being senior staff members ( n = 24 , 37% ) . There were 26 physicians ( 40% ) , 21 surgeons ( 32% ) , eight directors or managers ( 12% ) , six policy makers ( 9% ) , and five consultants ( 8% ) . The age of the staff ranged from 30 to 72 years ( mean = 49 years; standard deviation ( SD ) = 9 years ) . The interviewed staff had been employed for as long as 48 years ( mean = 26 years; SD = 9 years ) ( Table 1 ) . The age of patients ranged from 27 to 76 years ( mean = 57 years; SD = 10 years ) . While 22 ( 28% ) patients had no formal education , the remaining patients had between 1 and 15 years of schooling , often 6 years in total ( n = 16 , 20% ) . Most of the advanced schistosomiasis patients were farmers and/or fishermen ( n = 64 , 81% ) . Other professions included teachers ( n = 6 , 8% ) and civil servants ( n = 4 , 5% ) , whereas five individuals ( 6% ) did not specify their occupation . Most of the advanced schistosomiasis patients lived with their nuclear family ( n = 52 , 66% ) , while 22 ( 28% ) lived with their extended family , and 5 ( 6% ) lived alone ( Table 1 ) . The number of professional anti-schistosomiasis institutes in Hunan almost halved in the first 5 years of operation; from 140 in 1956 , to 76 in 1961 . A slight increase to 106 institutes had occurred by 2001 . The number of administrative institutes decreased from 20 in 1956 , to 16 in 1961 , but increased rapidly to a maximum of 49 in 1999 ( Figure 4 ) . The number of professional employees more than quadrupled from 959 in 1962 , to 3 , 918 in 2001 , and the number of administrative employees rose from a minimum of 65 in 1962 , to a maximum of 347 in 1999 . The number of employees per institute ranged from 12 to 37 ( mean = 22; SD = 9 ) for the professional institutes and from 3 to 8 ( mean = 6; SD = 1 ) for the administrative institutes . The number of beds available for schistosomiasis patients increased from 2 , 219 in 1956 , to 3 , 346 in 1958 , and then halved to 1 , 589 by 1962 . A second peak of 4 , 256 beds occurred in 1977 , which then decreased once more to 3 , 781 in 1984 and increased again steadily to the overall maximum of 4 , 697 beds in 2001 ( Figure 5 ) . The number of beds was highly significantly associated with the number of professional employees ( r = 0 . 88 , p<0 . 001 ) and the number of administrative employees ( r = 0 . 90 , p<0 . 001 ) . Specific expertise of staff at anti-schistosomiasis control stations included the use of preventive measures such as snail control , surveillance methodology , health information , education , and communication ( IEC ) skills , as well as experience in clinical examination and chemotherapy procedures . According to 30% of staff at anti-schistosomiasis control stations , most advanced schistosomiasis patients originated from non-endemic or only mildly endemic areas . A senior physician and schistosomiasis expert working at one anti-schistosomiasis control station explained it as follows: Risk awareness and chemotherapy were reported to be high among residents in high-endemic areas , blocking progression of schistosomiasis from acute and chronic stages to the advanced stage . On the contrary , individuals , including health workers , from non-endemic or mildly endemic areas were said to often lack disease-specific knowledge vital for diagnosis and treatment . Sixty-five percent of anti-schistosomiasis control station staff mentioned this issue , which apparently was most critical for women and children . As a director for a county anti-schistosomiasis office explained: Over 80% of patients reported that if their symptoms were only mild or moderate , they would go to a nearby clinic or to the township level anti-schistosomiasis control stations to seek treatment . When their symptoms were very serious , however , they would go directly to the county level professional anti-schistosomiasis institutes to seek health care . Furthermore , it was reported that splenectomy was only available at specialized anti-schistosomiasis institutes at the county level , or above , since lower level institutes lacked the equipment , facilities , and expertise to perform such operations . However , more than 90% of advanced schistosomiasis cases lived in the countryside . The mean annual per capita income in rural areas of Hunan Province in 2001 was CNY 2 , 299 ( SD = 1 , 980 ) , whilst in the families of the 79 patients suffering from advanced schistosomiasis , it was CNY 1 , 381 ( SD = 1 , 060 ) . The latter figure translates to approximately 60% of the rural average . The average medical cost of the 79 patients with advanced schistosomiasis was CNY 1 , 135 , equating to 82% of the average annual income . Most of the medical expense ( CNY 905 , 80% ) was directly due to schistosomiasis treatment . Medical spending was 12-fold higher among the advanced schistosomiasis patients interviewed than the per capita average ( CNY 96 ) in the rural areas of Hunan Province in 2001 . None of the patients had any form of health insurance and all expenses were out-of-pocket payments . Over half of the patients reported being unable to partake in strenuous farming activities due to their disease , but that they were also unable to cover the high costs of treatment and hospitalization: Expenditures were highest for inpatient treatment . Seventy percent of patients reported preferring outpatient treatment and buying medicine at the local chemist to inpatient systematic hospitalization and treatment . Similarly , 65% of the patients reported that they would ask to be discharged from the hospital or schistosomiasis station once symptoms improved only slightly , due to a lack of financial resources . Furthermore , 30% of patients delayed seeking professional medical care , turning instead to cheaper forms of traditional medicine . In 1994 , the local government of Hunan Province established the Hunan Provincial Foundation for Treatment of Advanced Schistosomiasis . The aim of the foundation is to provide subsidies during hospitalization; however , this was only applicable to patients who live in the countryside or those who are extremely poor with severe signs and symptoms of advanced schistosomiasis . The amount of money provided depended on the severity of disease and on the availability of resources within the foundation , but usually varied between CNY 200 and CNY 500 per person per year . One out of five patients had been eligible to apply for the subsidy , which covered part of their total medical costs . Ten percent of staff occasionally referred to gaps in the organization and management of human resources , with a viewpoint that a lack of thorough training , together with limited biomedical knowledge , resulted in poor quality of service provision . This was expressed by a director of a local anti-schistosomiasis control station and a surgeon , respectively , as follows: A county level deputy-director expressed regret that , whereas in the past the central government allocated significant financial resources to subsidize the schistosomiasis control program , this was no longer the case and counties had to provide the largest financial share for control . The same key informant further emphasized that schistosomiasis-endemic areas were mostly located in economically underdeveloped areas and that the local governments of these regions had limited financial capacities: Seventy-five percent of patients interviewed had known for a decade about their disease . They had acquired much knowledge in diagnosis and treatment through their personal experience and were often able to correctly identify modes of infection , prevention , and treatment . Fourty-five percent indicated that advanced schistosomiasis severely affected their health and that once they were diagnosed , they actively sought treatment . They believed that it was possible to cure the disease using modern scientific means and technology . However , the other 55% were more pessimistic about the disease . One 35-year-old male patient said: Patients who had considerable experiences of hospitalization in the local anti-schistosomiasis control stations and in general hospitals had a favorable impression of the services provided . For example , most felt that the doctors and staff were friendly and treated them fairly . However , 30% of the interviewees also said that they paid the bill without being able to assess the fairness of the charges . Mostly , they reported that when compared with the general hospital , the professional competence of the anti-schistosomiasis doctors and nurses was poorer , as were ward conditions and the equipment used . A 69-year-old male patient stated: However , being hospitalized in an anti-schistosomiasis control station was cheaper and it provided the possibility of being subsidized .
This study provides a comprehensive analysis of the performance of a disease control program at the sub-national level . Within P . R . China , in particular , this is an important unit of analysis due to a highly decentralized fiscal system . We applied the health access livelihood framework [13] to the Chinese schistosomiasis control setting , using a mixed-methods approach with both quantitative and qualitative data , including historical records , key-informant semi-structured interviews , and FGDs . The results indicate that unaffordable diagnostic and treatment costs , relative to household spending capacity , posed a major barrier in access to schistosomiasis-related health care in Hunan Province . Although sufficient infrastructure and human resources were available at local control stations , relevant health information , diagnostics , and specialized surgical services were poorly accessible to patients residing in non-endemic or low-risk areas . Moreover , staff implied that resource organization and management were somewhat inadequate , although patients felt that care was acceptable and that doctors and other health personnel were friendly and treated them well . The lessons learned from the current research have several important policy implications , particularly in view of P . R . China's goal to eliminate schistosomiasis . Over the past 60 years , P . R . China has demonstrated major achievements in schistosomiasis control [27]–[29] , [39]–[44] and the provincial level historical trends in our dataset indicate that this is also the case for Hunan Province . Nonetheless , the data in Figure 1 indicate that in 2001 there was still an increased schistosomiasis risk as a result of marginal temporal changes in the size of the snail habitat area inside the embankments and their effect on local schistosomiasis endemicity [45] . The nationwide control program was successful from its inception , although less so during the economic , political , and social upheaval of the 1960s [19] , [46] , [47] . Between 1983 and 1988 schistosomiasis re-emerged considerably , possibly as a result of changes in the rural economy and a severe reduction in finances directed toward schistosomiasis control [48] . The Chinese Government continued to pay attention to the control of acute schistosome infections , this being a sensitive issue . Thus , acute cases were kept at a relatively low level . Ultrasonography was introduced to measure morbidity caused by schistosomiasis during the middle of the 1980s . The resulting improvements in diagnosis led to more advanced cases being identified and reported , indicated by the sharp peak shown in Figure 2 . Following an outbreak of acute schistosomiasis in 1989 in Wuhan , Hubei Province [49] , [50] , a major metropolitan area neighboring Hunan Province , a decade-long robust targeted control ensued . This was supported by an overall investment of US$ 82 million from the Chinese government , complemented by a further US$ 71 million through the WBLP [29] , [32] , [51] . More recently , the national control program has been further strengthened and prioritized , as P . R . China aims to move toward schistosomiasis elimination [23] , [43] , [52] , [53] . Despite clear accomplishments , the data in Figure 2 suggest that , while acute and advanced cases of schistosomiasis declined considerably over time , both disease types remained of public health importance . This may be a result of substantial changes in human and animal population density connected to large water management projects in the Dongting Lake area [45] , as well as daily water contact patterns of migrant farmers and fishermen . Whilst snail and animal reservoir host infection rates may have decreased as a result of large-scale chemotherapy , they remained important contributors towards the schistosomiasis transmission cycle . In accordance with other studies [54] , [55] , we found that patients with advanced schistosomiasis japonica were , on average , above 40 years , with lower levels of general , and health-specific , education . We also found a growing trend of advanced schistosomiasis japonica among the young and/or female populations , especially those originating from low-endemic areas . This is likely to be due to a lack of awareness of key risk factors , such as contact with infested water , as well as the presence of non-specific symptoms which do not trigger the appropriate treatment-seeking behavior [56]–[59] . Moreover , populations from non- or low-endemic areas who relocate to endemic areas are at an increased risk of infection because of a lack of acquired immunity ( i . e . , persons never exposed to schistosomiasis ) or diminished immunity ( i . e . , persons formerly exposed ) [60] . In the Dongting Lake area , several thousands of people have migrated from non-endemic areas upstream of the Three Gorges Dam , and the system currently in place may fall short on meeting the distinct needs of such a mobile population , posing heightened risks of re-emergence [43] . We advocate enhancing disease control and surveillance in less endemic areas , and strengthening health education and awareness among residents in all areas . Poverty is a key social determinant of schistosomiasis [24]–[26] , [38] , [41] , [42] , [61] . We found that patients with advanced schistosomiasis were among the poorest individuals in the community . High medical expenditures accounted for a considerable proportion of their annual income and a majority of the patients interviewed reported having no form of social security . In the mid-1950s , a collectivism-based rural health insurance system known as ‘co-operative medical system’ ( CMS ) was established in P . R . China [62] , covering the cost of medical care , including the management of advanced schistosomiasis cases [63] . However , as the CMS collapsed at the end of the 1970s , the provision of schistosomiasis-related health care altered from free services to fee-for-service ( i . e . , mostly out-of-pocket ) for all except government employees and state-run factory workers , and around 90% of the rural population were left uninsured . The Chinese government has recently recognized the need to increase health spending and promote new health insurance schemes such as the ‘new co-operative medical system’ ( NCMS ) , which focuses on reducing the risk of catastrophic costs of healthcare [64] , [65] . There are concerns , however , that this may be less effective in rural areas , where a majority of the population resides [66] . Furthermore , a series of regulations were implemented by the Chinese government to alleviate the financial burden of advanced schistosomiasis in endemic areas , but these regulations do not take into account cases in non-endemic areas [67] . High treatment costs prevented 35% of patients interviewed from seeking professional medical care , whilst others may have been forced to sell productive assets or may have become indebted and subjected to high short-term interest rates , in order to finance their healthcare . These strategies for coping impact people's livelihoods , increase their vulnerability , and reduce their capacity to negotiate their way out of poverty . If many households experience similar problems , this may have a negative effect on a community's capacity to overcome such shocks . In order to establish a comprehensive social security system applicable to the entire population , efforts should be made to develop feasible , practical , and sustainable health insurance and social support systems with special attention to the most vulnerable and marginalized groups , such as remote and impoverished patients suffering from advanced schistosomiasis . We found that the availability of physical resources ( e . g . , number of hospital beds ) , and human resources ( e . g . , number of trained health personnel ) increased over time although the prevalence of S . japonicum decreased . Broadening control efforts to include other parasitic diseases may maximize the benefits of available resources , improve efficiency and resource allocation , and increase cost-effectiveness , particularly in light of the incorporation of novel technologies such as geographical information systems ( GIS ) and remote sensing for disease surveillance in remote areas [41] , [43] , [52] , [53] , [66] , [68] , [69] . This expanded profile of parasites may include soil-transmitted helminthiasis and intestinal protozoa infections , which are highly prevalent in many areas of P . R . China [25] , [70] . Historical observations suggest that the availability of schistosomiasis-related health care is largely due to the Chinese governments' perceived public health importance of schistosomiasis , which was placed high in the political agenda and treated as a public or semi-public good . In view of recent market-oriented health-care reforms , the relative roles of the government and market in health services for P . R . China should be re-assessed [17] , [21] , [22] . According to a majority of interviewees' perceptions , there were no major barriers in terms of adequacy and acceptability of schistosomiasis control , although several patients and staff expressed a need for improved technical expertise and efficiency at the grass-roots level . Studies in various country settings have found positive associations between health service provider-user relationships and user satisfaction , with a lack of clear and effective communication being a key undermining factor in provider-user interactions , negatively affecting health seeking behavior through a lack of trust [71] , [72] . In P . R . China , a review of general health services delivery reported patient complaints about unclear information and communication regarding the services received [73] . In our study , a small number of patients showed signs of distrust of doctors with regard to payment and reimbursement policies . In addition , some staff claimed that providers may recommend more expensive tests than necessary , suggesting that considerations other than clinical needs are influencing their provision . These results highlight the need for increased staff training , improved communication , and an appropriate regulatory framework which redefines more clearly and transparently the responsibilities of schistosomiasis control staff particularly in light of comprehensive integrated control that includes cross-sectoral collaboration and co-operation [23] , [52] , [74]–[75] . The study was subject to several limitations . First , its small-scale and exploratory design precludes generalization at the provincial or national level for P . R . China . The study area is , however , a major endemic focus , epidemiologically representative of schistosomiasis control in Hunan Province and other marsh and lake regions in southern P . R . China such as the Poyang Lake area in Jiangxi Province . Second , the qualitative data are subject to a number of biases , including recall bias , partial inclinations according to the interviewees' judgment of what the interviewers wished to hear , or by reluctance to talk about sensitive issues . There is also a possibility of gender bias due to the under-representation of female key informants . Nevertheless , the study aimed to highlight key issues and interests among providers and recipients of schistosomiasis control activities , and rigorous measures were taken throughout to reduce such biases . Third , the data collection was undertaken more than 10 years ago , and hence it is conceivable that people's perceptions have changed and schistosomiasis control services have further adapted to the changing epidemiology and control emphasis of the national program . However , advanced schistosomiasis is a chronic condition with people suffering from it many years after schistosome transmission has been interrupted [76] . We have now planned a similar study to determine whether disease-specific knowledge , specialized health care services , and rural health insurance have indeed improved . In this study we illustrate the importance of taking a systemic approach to evaluation that links utilization and provision of health services with the wider context of policy making . According to Tang et al . [20] three major inter-related processes have emerged concurrently to create “a perfect storm” for health care in P . R . China . These are: ( i ) market failures and insufficient government stewardship; ( ii ) inequities in the social determinants of health; and ( iii ) the erosion of public perceptions of fairness and trust of the health-care system [20] . Our study has identified areas that require action within Hunan Province , and the results gained from applying the health-access livelihood framework indicated that , from the above three points , the second may be the most vital component for the control of schistosomiasis , followed closely by the first and the third . The findings are of particular relevance now that P . R . China's goal is to eliminate schistosomiasis and , even if elimination has been reached , they are of importance for post-transmission schistosomiasis [76] . This study also highlights future research needs in order to generate evidence for design and development of disease control programs . | China has made great strides toward reducing the burden of schistosomiasis , facilitated by sustained political commitment and a multi-faceted , integrated control strategy . The ultimate goal is disease elimination , which might be challenging due to high rates of re-infection , clusters of re-emergence , and growing health disparities . Market-oriented reforms and system-wide policies within the health care system offer new opportunities , but also entail challenges for the national schistosomiasis control program . Few studies have examined systemic barriers to equitable and effective schistosomiasis control in China . We explored the five core dimensions of access to health care , placing emphasis on schistosomiasis in the Dongting Lake area of Hunan Province . We collected and analyzed perspectives from staff working at local anti-schistosomiasis control stations and designated schistosomiasis hospitals , and from patients with advanced schistosomiasis . Our data suggest that a lack of affordability and high out-of-pocket expenditure posed a major barrier to the health care users , as did a lack of relevant health-information , and poorly accessible diagnostic and specialized surgical services . The lessons learned from this work are important in the design and development of disease control programs and entail key policy implications for schistosomiasis elimination . | [
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] | 2013 | Health Access Livelihood Framework Reveals Potential Barriers in the Control of Schistosomiasis in the Dongting Lake Area of Hunan Province, China |
Visual pattern detection and discrimination are essential first steps for scene analysis . Numerous human psychophysical studies have modeled visual pattern detection and discrimination by estimating linear templates for classifying noisy stimuli defined by spatial variations in pixel intensities . However , such methods are poorly suited to understanding sensory processing mechanisms for complex visual stimuli such as second-order boundaries defined by spatial differences in contrast or texture . We introduce a novel machine learning framework for modeling human perception of second-order visual stimuli , using image-computable hierarchical neural network models fit directly to psychophysical trial data . This framework is applied to modeling visual processing of boundaries defined by differences in the contrast of a carrier texture pattern , in two different psychophysical tasks: ( 1 ) boundary orientation identification , and ( 2 ) fine orientation discrimination . Cross-validation analysis is employed to optimize model hyper-parameters , and demonstrate that these models are able to accurately predict human performance on novel stimulus sets not used for fitting model parameters . We find that , like the ideal observer , human observers take a region-based approach to the orientation identification task , while taking an edge-based approach to the fine orientation discrimination task . How observers integrate contrast modulation across orientation channels is investigated by fitting psychophysical data with two models representing competing hypotheses , revealing a preference for a model which combines multiple orientations at the earliest possible stage . Our results suggest that this machine learning approach has much potential to advance the study of second-order visual processing , and we outline future steps towards generalizing the method to modeling visual segmentation of natural texture boundaries . This study demonstrates how machine learning methodology can be fruitfully applied to psychophysical studies of second-order visual processing .
Many of the most common functions of sensory systems involve detection , identification or discrimination of particular stimuli . For example , a critically important ability of early visual processing is to segment images into distinct regions , which may correspond to cohesive surfaces or objects [1] . To better understand the underlying mechanisms , it would be useful to fit biologically plausible models of surface segmentation to psychophysical data , and assess how well such models can account for independent datasets . A widely used approach to modeling human psychophysics has been to assume that performance can be understood in terms of how well a visual stimulus matches an internal “template” , typically modeled as a linear spatial filter . This approach uses high-dimensional regression models fit to data from tasks in which a subject detects or classifies a sensory stimulus presented with superimposed white noise [2–5] , to recover an estimate of the linear filter that best accounts for the data . This system-identification ( SI ) approach to psychophysics , often called “psychophysical reverse correlation” or “classification image analysis” , parallels neurophysiology studies that fit linear filter models to neural responses elicited by white noise stimuli [6 , 7] . Psychophysical SI methods have been applied in varied problem domains and yielded valuable insight into perceptual mechanisms [4 , 8–13] . One limitation of most previous psychophysical system identification studies has been the use of stimuli that are typically defined by spatial or temporal variations in first-order statistics like luminance or color . However , many natural visual stimuli such as occlusion boundaries ( e . g . , the top image patch in Fig 1a , showing animal fur occluding a forest floor ) are defined by spatial variations in higher-order image statistics , e . g . texture or contrast , as well as simple luminance differences [14–18] . Such “second-order” boundaries ( Fig 1a , bottom ) have been of particular interest because their detection cannot be explained by simple linear filters [19 , 20] , and consequently human performance cannot be readily modeled using conventional psychophysical SI methods . More generally , there is a need for a psychophysical modeling approach that can handle a broader range of stimuli and tasks . Here we present a novel machine learning method to extend psychophysical SI to more complex naturalistic stimuli such as second-order boundaries . Our general approach is to fit image-computable hierarchical neural network models of second-order vision ( Fig 2 ) directly to psychophysical trial data , similar to recent studies using multi-stage models to characterize nonlinear responses in sensory neurons [21–24] . Our application of this framework generalizes a class of biologically plausible “Filter-Rectify-Filter” ( FRF ) models that have often been invoked to explain second-order processing [20] . However , in contrast to previous general schemes for second-order vision [25–27] and quantitative models [28 , 29] , we take a machine learning approach in which we estimate the defining model parameters by direct fitting to psychophysical trial data . This permits more incisive tests of predictions on novel datasets in order to validate our models , and to evaluate competing models . We focus on contrast-defined second-order boundaries ( Fig 1a and 1b ) since they are well studied psychophysically [30–34] , and there are neurons in the early mammalian visual cortex that give selective responses to contrast modulation stimuli [35–38] . Performance on two tasks making use of contrast-defined boundaries is examined: ( 1 ) identification of boundary orientation , and ( 2 ) discrimination between two slightly off-vertical boundaries ( Fig 1c ) . The results demonstrate that subjects utilize spatial information in markedly different ways for these two tasks , employing “region-based” processing for the orientation identification task and “edge-based” processing for the orientation discrimination task , in both cases consistent with an ideal observer . We further demonstrate the power of this methodology for evaluating competing hypotheses regarding the nature of channel summation within the FRF scheme [29 , 39–41] by comparing the ability of two different model architectures to fit human performance on a contrast edge detection task in which the carrier pattern contains elements with two orientations . Post-hoc Bayesian model comparison [42–44] demonstrates a preference for a model in which carrier pattern orientation is integrated across channels by the second-stage filters , consistent with previous psychophysical studies [40] . Finally , we discuss potential applications of this methodology to future quantitative studies of first- and second-order cue integration [45–47] and natural boundary detection [18 , 48] . We suggest that the application of machine learning methodology similar to that presented here can potentially help to better quantify visual processing of natural occlusion boundaries and complex natural visual stimuli more generally .
The model used to fit psychophysical trial data ( Fig 2 ) employed a first stage comprised of a bank of fine-scale Gabor filters that compute local oriented energy in a manner similar to V1 complex cells [49] . The second stage consisted of a pair of filters , representing the two task alternatives ( L/R ) , each instantiated by a weighted sum of the normalized outputs of the first-stage filters . In contrast to the first-stage filters , these second-stage filters analyze the image at a more coarse spatial scale , integrating variations in contrast across the boundary . Finally , the outputs of these two second-stage filters are passed through a static non-linearity h ( u ) = |u|α and then compared to decide on a trial-by-trial basis if a given stimulus boundary appeared more likely to be tilted left or right ( L/R ) , i . e . the probability 0 < P ( R ) < 1 of the stimulus being classified by the observer as R . The main goal of Experiments 1 and 2 was to estimate the shapes of the second-stage filters . The filter shapes are not specified beforehand , but rather are learned by fitting actual psychophysical data , with the goal of revealing how the observer spatially integrates texture information . To reduce model parameter space dimensionality , we performed downsampling between the first- and second-stage filters , i . e . pooling of spatially adjacent first-stage filter responses . Here we compared three ways of implementing downsampling within each locality: ( 1 ) taking the simple average ( AVG ) , ( 2 ) taking the largest value ( MAX ) [50] , and ( 3 ) taking just one of the values , e . g . the center one ( sub-sampling , SUB ) [26] . In Experiment 1 we considered all three pooling rules; most analyses utilized 16x16 downsampling , but 3 downsampling sizes ( 22x22 , 16x16 and 8x8 ) were compared . In Experiments 2 and 3 we considered only AVG pooling and a single downsampling size ( Experiment 2: 22x22 , Experiment 3: 12x12 ) . We implement the model’s final ( L/R ) classification in two alternative ways: In the deterministic model ( DET ) , if the output of the R filter is larger than that of the L filter ( plus the bias term ) so that P ( R ) > 0 . 5 , the stimulus will be classified as R , otherwise it is classified as L . In the stochastic model ( STO ) , the output P ( R ) specifies only the probability of classifying the stimulus as R or L , thus introducing randomness to the model without requiring an additional free parameter . A common approach to make the estimation problem more tractable is to utilize reasonable prior constraints on the model parameter space [51–53] . Here we use two kinds of Bayesian priors: ( 1 ) ridge-regression [44 , 54] to penalize large weights ( ridge ) , and ( 2 ) ridge-regression combined with an additional smoothness prior [51] to penalize excessively “rough” second-stage filter weight profiles ( ridge + smooth ) . Model performance was evaluated by using different randomly selected partitions ( called folds ) of the data for training and testing , a standard practice in machine learning ( i . e . , k-fold cross-validation [51] ) . Additional details of the model are given in Methods . To better assess the meaningfulness of the above model-fitting results , it is important to make quantitative measures of the fitted models’ accuracy in predicting observers’ responses to novel stimuli . Such measures can also address the relative merit of different variants of the estimation procedure ( ridge vs ridge + smooth prior ) and of the model details , such as downsampling method ( AVG , MAX , SUB ) and classification rule ( DET , STO ) . In this section we describe some of our analyses of model performance , and refer to other assessments in the Supplementary Material . The perceptual filters obtained in Experiment 1 ( Fig 3 ) seem consistent with the idea that observers are monitoring the entire stimulus region for each of the two possible boundary orientations . However , to rule out the possibility that such filters could also be observed if observers were utilizing some different kind of spatial processing , we implemented four alternative varieties of spatial summation as ideal observer models fit to category labels . We then fit the model architecture in Fig 2 to data from these ground truth ideal observers in order to assess whether our methodology is consistent in the sense that it recovers the actual set of filters generating the data . One possible alternative spatial summation that could mediate our task would be to simply monitor two adjacent “pizza slice” shaped quadrants defined by the four compass directions ( 2-slice ) . For instance , if one is monitoring the North and East slices , a difference in contrast between these slices is diagnostic for a right-oblique boundary . Implementing an ideal observer that utilizes such a subset of the spatial information ( Fig 10a , top ) and fitting our model architecture shown in Fig 2 to this data reveals consistent results: A single filter which monitors two slices only . Implementing an observer who monitors three adjacent slices ( "3-slice" ) , for instance N+E and N+W ( Fig 10a , second row ) also reveals consistent filters . Another possible spatial processing approach would be to observe all slices but to only monitor for one of the two potential boundary orientations ( "1-filter" ) —simulating this model yields a consistent estimate of a single filter tuned to the monitored boundary ( Fig 10a , third row ) . One can in principle observe filters somewhat similar to those we recover in Fig 3 by using a sub-optimal spatial processing model which only monitors a single pair of potentially informative slices , chosen at random on each trial . However , such a model leads to perceptual filters which are extremely “noisy” compared to those we find ( Fig 10a , bottom row ) , so it is quite unlikely that our observers are actually using such spatial processing . The simulated ideal observer that produces filters providing the best match to those we obtain from human observers ( Fig 3 ) makes use of two filters , each of which monitors the entire stimulus region for each of the two stimulus possibilities ( Fig 10b ) . The first experiment demonstrated that human observers employ region-based processing for the orientation identification task . To see if our modeling method could also reveal situations where observers employ edge-based processing , we utilized a fine orientation discrimination task ( Fig 1c , right ) . The orientation of a second-order boundary was perturbed slightly ( JND at 32% contrast modulation—see Methods ) from vertical , and observers judged the direction of perturbation ( i . e . slightly left- vs right-oblique ) . In one variant of this task ( Experiment 2-VAR ) , contrast modulation was varied in 9 logarithmic steps from 16% to 64% centered at 32% ( plus zero modulation ) to vary the task difficulty . In a second variant ( Experiment 2-FIX ) , contrast modulation was fixed at 32% . In both variants of Experiment 2 , the perturbation of orientation was fixed near its JND for each observer at 32% contrast modulation ( CJD: 7 deg . , JJF: 6 deg . , VHB: 7 deg . ) . In the data analysis , a higher downsampling factor ( 22 x 22 ) was used to better reveal fine spatial details , and only AVG pooling was considered , since Experiment 1 demonstrated substantial robustness of spatial summation to pooling rules ( S8 and S9 Figs ) . Fig 11 shows the second-stage filter weights from an observer ( JJF ) tested in both Experiment 2-VAR ( Fig 11a ) and Experiment 2-FIX ( Fig 11b ) . Unlike in Experiment 1 , here observers employ an “edge-based” summation , assigning substantially more weight to texture regions near the boundary . Bootstrapped significance plots for individual weights are shown in S15 Fig , and consistent results from other observers are shown in S16 Fig . The edge-based processing employed by these observers is very similar to that obtained from an ideal observer ( S17 Fig ) . As with Experiment 1 , we find a reasonably strong agreement between model predictions and observed performance on sets of novel data not used for model parameter estimation ( S18 Fig ) , and find an expansive second-stage filter nonlinearity for Experiment 2 ( S7 Table ) . Taken together , Experiments 1 and 2 show that our modeling methodology can reveal different kinds of spatial processing that observers may employ for different kinds of psychophysical tasks . We also considered the possibility that our results in Experiment 2 ( Fig 11 ) could also be explained by two off-orientation filters defined on the spatial scale of the whole stimulus [61] . We defined an ideal observer with two off-orientation filters ( +/- 20 deg . ) defined on the scale of the stimulus and simulated responses of this observer to the stimuli used in Experiment 2 . This analysis yielded very different filters from those we observe in Fig 11 , as we simply recovered the original off-orientation filters ( S22 Fig ) , again demonstrating the consistency of our method ( as in Fig 10 ) . Based on these results , we suggest that it is more likely that the relevant neural mechanisms employed in Experiment 2 are contrast-modulation tuned neurons with smaller receptive fields localized to the boundary ( see Discussion ) . In Experiments 1 and 2 , we considered a particular FRF model architecture and focused on characterizing the spatial processing that observers employ in different second-order vision tasks . However , one of the most powerful applications of this methodology is model comparison—i . e . comparing the ability of alternative models , embodying different assumptions about an FRF model architecture , to account for psychophysical data . Indeed , a major focus of much of the second-order vision literature has been to design experiments whose express goal is to test competing hypotheses of FRF architecture [29 , 39–41 , 62] . We applied our method to ask how second-stage filters for contrast modulation integrate orientation modulations across multiple kinds of first-stage filters or channels . In many previous computational studies of second-order processing , the FRF models were comprised of second-stage filters , each of which only analyzes a single orientation/spatial frequency channel [28 , 63 , 64] , with subsequent “late summation” of their signals . However , other psychophysical studies have suggested “early summation” FRF models , in which second-stage filters integrate contrast modulation across texture channels [39 , 40 , 62] . To address this question systematically , we used a boundary orientation identification task as in Experiment 1 , but with the carrier pattern containing two orthogonal orientations of micropatterns ( Fig 1a , bottom ) , which would be processed by distinct first-stage orientation-selective channels . Since the nature of spatial summation is not critical to this question , we used a coarser downsampling factor ( 12 x 12 ) , and as above , only AVG pooling was used . We considered two models of how information is integrated by the second-stage filters . In a “late summation” Model 1 ( Fig 12a , top ) , each first-stage channel is analyzed by its own pair of second-stage filters ( L/R oblique ) . Alternatively , in an “early summation” Model 2 ( Fig 12a , bottom ) , each second-stage filter receives inputs from both first-order channels . As in Experiments 1 and 2 , we find that both the models are in reasonably good agreement with observer performance on the task ( S20 Fig ) . Also there was reassuring consistency in that both the fitted models exhibited similar region-based weight maps , with an expansive second-order nonlinearity ( S8 Table ) . When comparing Models 1 and 2 using standard model comparison techniques such as the Bayes Information Criterion , or BIC [42 , 44] , there is no need to correct for overfitting since both models have the same number of parameters . This is because when parameter space dimensionality is equal , the complexity-penalizing terms of the BIC cancel , so that ΔBIC = ln p2 ( D ) − ln p1 ( D ) , where pi ( D ) denotes the likelihood of the data ( D ) under model i evaluated at the MAP estimate of the model parameters . That is , when two models are of equal complexity and equally likely a priori , we simply choose the one with higher log-likelihood of the data . The difference in log-likelihoods ( ΔBIC ) is identical to the natural log of the Bayes factor B21=p2 ( D ) p1 ( D ) . Standard effect size conventions state that when 2 ln B21 > 10 ( corresponding to > 150-fold higher probability of Model 2 ) , the preference is considered to be “very strong” [43] . We see in Fig 12b that for all three observers tested in Experiment 3 , the evidence ( measured by 2 ln B21 ) in favor of the orientation opponent model ( Model 2 ) exceeds the standard criterion ( 10 ) for a very strong preference . Ideal observer analysis also revealed a very strong preference for the orientation-opponent model fit to stimulus category labels ( 2 ln B21 = 478 . 4 ) . With a large number of trials , a strong preference for one binomial response model does not require a huge difference in predicting observer performance ( Appendix A ) . We see in Fig 12d that although Model 2 very consistently does a better job of matching observer performance than Model 1 , that the differences between predicted proportions correct are fairly small . We also wanted to compare the predictive abilities of the two models using a cross-validation approach , by evaluating their agreement with observer performance on data not used for training . Therefore we performed a bootstrap analysis in which both models were fit on randomly selected samples of 4000 stimuli and tested on the remaining 1000 ( 50 bootstraps ) . To optimize the generalization performance , each model was trained using the value of the ridge regression hyperparameter λ which yielded the best generalization ( S19 and S21 Figs ) . The results in Fig 12c show a consistent preference across observers for Model 2 ( magenta line ) . Performing a 3 x 2 factorial ANOVA ( 3 observers , 2 models , 50 observations each ) on the test set log-likelihoods revealed significant main effects of both observer ( F2 , 294 = 201 . 085 , p < 0 . 001 ) and model ( F1 , 294 = 39 . 811 , p < 0 . 001 ) , but no significant interaction ( F2 , 294 = 3 . 001 , p = 0 . 051 ) . In summary , both analysis approaches ( Fig 12b and 12c ) show a strong and consistent preference for the early summation version ( Model 2 ) .
Psychophysical system identification is critically limited by the modest amount of data that can be collected in an experiment . Although constraints or priors can help alleviate the demand for data [51 , 67 , 68] , it is also often necessary to fit simplified models requiring fewer parameters , which omit many known biological details . Here we discuss several modeling simplifications which may need to be addressed when extending this methodology to more complicated second-order stimuli and tasks . Despite the simplifications made in the present study , it represents a substantial methodological advance over previous efforts to model second-order visual perception . Although previous studies have developed image-computable FRF models , they have generally fixed not only the model architecture but also the shapes of the second-stage filters . The present study demonstrates how the second-stage filter shapes can be “learned” directly from experimental data in order to reveal the nature of spatial integration used in different tasks ( Experiments 1 and 2 ) . Most previous FRF models in human psychophysics studies were conceptual or qualitative , without quantitative fitting of model parameters to data . Two recent studies [28 , 90] estimated free parameters of an FRF model by fitting threshold data as functions of stimulus parameters . Since thresholds are typically determined at only a few levels of a one-dimensional independent variable , this yields relatively few data points , greatly limiting the number of model parameters one can estimate . In our work , we directly estimate hundreds of model parameters by fitting stimulus-response data from thousands of psychophysical trials , enabling us to characterize the shapes of the second-stage filters . In addition to extending the FRF modeling literature , our work also complements and extends previous classification image studies , which have generally been applied in the context of first-order vision [4 , 5] . Although classification images yield image-computable predictive models of observer responses , relatively few studies validate these models by testing their predictive performance on a set of stimuli not used for model estimation . We tested model performance on novel stimuli not used for model estimation using both standard measures , e . g . percentage correct , as well as more advanced methods such as double-pass analysis [58 , 59] and decision variable correlation [60] . Finally , in contrast to most classification image studies , here we systematically address the issue of model comparison . Psychophysical system identification can be formulated in the framework of estimating a generalized linear model [51 , 67 , 68] , which is unique up to choice of link function . Therefore , issues of model comparison ( beyond coefficient shrinkage ) do not naturally arise in this context . However , the FRF models relevant to second-order vision can vary greatly in architectural details ( e . g . , number of filtering layers , nonlinearities , connectivity , etc . ) , and model comparison is of great interest . In Experiment 3 , we compare two competing FRF architectures making different assumptions about how the second-stage filters integrate across orientation channels , finding a preference for a model with early summation of first-order channels by the second-stage filters , consistent with previous psychophysical work [39 , 40 , 76] . We applied our methodology to the question of whether or not observers take a region-based or an edge-based approach to segmenting a contrast boundary ( Experiment 1 ) . To the best of our knowledge , our experiment is the first time the issue of edge/region-based processing has been addressed for boundaries defined by differences in contrast , as it has for other second-order boundaries [55–57] . This question is of particular interest in light of several studies that have revealed possible neurophysiological mechanisms for contrast-boundary detection [36–38 , 91] . This work has demonstrated a population of contrast-modulation ( CM ) tuned neurons in early visual cortex , which exhibit band-pass spatial frequency and orientation tuning to second-order modulation envelope frequency , consistent with a Gabor-shaped second-stage filter applied to the outputs of a bank of first-stage filters selectively tuned to the carrier pattern [19] . If such CM-responsive neurons were the mechanism responsible for psychophysical detection of CM boundaries , one might expect the 1-D weighting profiles to have a Gabor-like shape , placing greater weight on regions near the boundary . However , we find that this was not the case and that all areas of the image are weighted equally for the orientation-identification task ( Experiment 1 , Figs 3 and 4 ) . Furthermore , in an additional control manipulation where we increased the carrier frequency in order to make sure CM neurons with smaller RFs would be relatively more engaged ( Experiment 1-HFC ) , we still did not see any change in the type of spatial integration ( Fig 5 ) . These findings are consistent with at least three distinct possibilities ( not mutually exclusive ) for the underlying neural mechanisms . One possibility is that task performance reflects CM-responsive neurons , like those described in V2 with large receptive fields ( > 4 dva ) encompassing the entire stimulus used in our experiments . Such neurons would prefer a carrier frequency much higher than their envelope frequency , which is plausible since primate V2 CM-responsive neurons can have preferred carrier:envelope ratios of up to 40:1 [38] . A second possibility is that psychophysical performance is mediated by a range of Gabor-like second-order mechanisms covering a range of spatial scales , and that our measured 1-d profiles reflect an ensemble of their contributions . Another possibility is that an important role is played by other neurons sensitive to texture patterns , such as those described in ventral stream areas [81–83 , 92] . Clearly , the fact that we can readily distinguish non-juxtaposed textures implies that there must be mechanisms for texture representation that operate in the absence of a boundary . However , in some cases texture segmentation can benefit from boundary information , as in the textural Craik-Cornsweet illusion [55] . For example , [57] demonstrated that direct juxtaposition of textures having different orientations gives rise to local junction or corner cues that can support segmentation . Such enhanced segmentation might be mediated by neurons such as those described by [93 , 94] , which are selective for such cues . However , other work on texture segmentation has shown a minimal contribution of information along the boundary . A study using natural image patches in a task of discriminating occlusion boundaries from uniform textures found that removing the boundary region had little effect on performance [18] . However , performance on natural occlusion edge and junction detection tasks can be seriously degraded when textural information , which is by its nature defined over a region , is removed [18 , 48] . Similarly , segmentation of complex textures defined by letters shows little effect of boundary information [56] . In general , the answer to this question is most likely dependent on the particular stimulus and the nature of the psychophysical task , as indicated by the very different results of our first two experiments . In our view , the question of “edge” versus “region”-based processing should be treated with some caution , since clearly for a very large stimulus extending into the far periphery there will be reduced weighting at the largest eccentricities , since visual acuity falls off sharply outside the fovea . In such a case the real question of interest would be the shape of the fall-off in weight as a function of stimulus eccentricity , and how this fall-off may relate to putative neural codes . Our method is well suited to address such issues for many different kinds of second-order boundaries and tasks , much as standard classification images can be similarly used for first-order boundaries . We further demonstrate that our modeling methodology is consistent , meaning that it can accurately recover the actual perceptual filters employed in a task , provided the fitted and generating models have corresponding architectures . Simulation of ideal observers implementing sub-optimal spatial processing , e . g . utilizing only limited subsets of the texture , revealed filters which do not match those we observed experimentally ( Fig 10a ) . In contrast , a strong agreement was found with results from simulating an observer monitoring the whole stimulus for each of two possible boundary orientations ( Fig 10b ) , strengthening our interpretation that this is the kind of spatial summation actually being employed . In Experiment 1 our results showed that human observers use every part of the image to perform the task ( Fig 3 ) , consistent with an ideal observer that integrates information from all image regions ( Fig 10b ) . In terms of possible mechanisms , we might therefore expect CM-tuned neurons with larger receptive fields that integrate over the expanse of the stimulus image to be engaged in Experiment 1 . The results in Experiment 2 showed that only the texture information near the boundary was being utilized by humans ( Fig 11 ) , again consistent with an ideal observer that is indifferent to the stimulus elsewhere . This suggests that the most useful neural substrate in Experiment 2 might be CM-responsive neurons with smaller receptive fields localized near the boundary , that integrate texture only over small extents to either side of the boundary . We demonstrate using simulation that one cannot explain the filters we observe in Experiment 2 by assuming two large off-orientation receptive fields ( S22 Fig ) , since otherwise we would have recovered filters with slightly non-vertical orientation rather than filters localized to the vertical ( Fig 11 ) . We conjecture that our results are more consistent with the possibility that observers are making use of contrast-modulation tuned neurons having smaller receptive fields localized to the boundary . In both Experiments 1 and 2 we found that humans behaved as ideal observers with regard to their spatial integration of texture . Such a result should not be regarded as inevitable or having little relevance for questions of neural mechanism . Previous psychophysical studies ( including several classification image studies ) have demonstrated that humans often do behave ideally in perceptual tasks [12 , 95–98] and furthermore such ideal behavior can be suggestive of neuronal mechanisms underlying the behavior [99–101] . However in many other cases , human psychophysical performance does not match that of an ideal observer [2 , 3 , 8 , 96 , 98 , 102 , 103] . Interestingly , [96] demonstrated that even with the same stimulus , changes in the psychophysical task could alter whether or not human observers behaved ideally . We feel that the demonstration of ideal behavior in Experiments 1 and 2 is significant , and strongly suggests possible neural mechanisms , for example suggesting a greater involvement of neurons with larger CM-responsive receptive fields in the task of Experiment 1 , and those with smaller receptive fields in Experiment 2 . In general , the relationship between neural response properties and the perceptual filters obtained in classification image studies is complex [4 , 104] . Perceptual filters do not generally correspond to fixed , "hard-wired" neural populations , but are most likely actively constructed from available neuronal populations through perceptual learning . Consistent with this idea , previous work has demonstrated that observers’ perceptual summation gets successively closer to that of the ideal observer with practice [10 , 105] . It would be of great interest for future research to explore the dynamics of the perceptual filtering strategies that observers employ in our tasks . A common ingredient to the various FRF models proposed to account for psychophysical texture segmentation data is a set of fine-scale first-stage filters summed by one or more second-stage filters defined on a much larger spatial scale . However one aspect in which such models differ is whether the individual second-stage filters analyze first-stage filter responses at a single orientation/spatial frequency , or whether they integrate across heterogeneous first-stage channels . In one standard model of FRF processing , each second-stage filter only analyzes the outputs of a single first-stage orientation/spatial frequency channel [25 , 63 , 64] . However a number of psychophysical results have called this model into question . One study using a sub-threshold summation paradigm found support for a model utilizing linear integration across carrier orientation for detecting contrast modulations [40] . Another study making use of energy-frequency analysis revealed the existence of orientation-opponent mechanisms in second-order vision [62] . Other work has suggested at least partly independent mechanisms for detecting second-order modulations defined by contrast and spatial/frequency orientation , with the suggestion that contrast modulations may be pooled across first-stage channels [39] . In Experiment 3 we find support for a model with second-stage filters that integrate across first-stage orientation channels , consistent with the latter psychophysical studies . Although CM-responsive neurons described so far exhibit narrow tuning for carrier orientation [38] , this discrepancy could be due to the use of grating carriers rather than multi-orientation micropatterns . This suggests that that possible effects of carrier orientation bandwidth might be worth future investigation , both in human psychophysics and for CM-responsive neurons . In our opinion , Experiment 3 best demonstrates the power of this methodology compared to traditional linear classification image approaches: The ability to explore a much larger space of non-linear perceptual models embodying various assumptions about neural mechanisms . Rather than simply comparing recovered filter shapes to ideal behavior as in most previous classification image studies [5] , here we directly compare two biologically plausible hypotheses of how contrast modulation is integrated across orientation channels . Our finding of clear support for an early summation model [40] suggests that an interesting direction for future neurophysiological investigation would be to examine neural responses to contrast modulations of carrier patterns containing multiple orientations . Such a study would be quite novel , as previous neurophysiological investigations of contrast modulation tuning have only considered a single carrier orientation [35 , 38] . Statistical machine learning has in recent years become a powerful and widely applied methodology in vision and in computational neuroscience . Our work demonstrates that the same machine learning methodology used to characterize neural coding [21 , 52 , 88 , 106 , 107] and first-order visual processing [5 , 51] can be fruitfully applied to extend psychophysical system identification approaches to complex visual stimuli , including second-order boundaries . We anticipate that future applications of this approach may lead to a better understanding of how multiple first- and second-order cues are combined by human observers to detect natural boundaries , and provide insights into neural and computational mechanisms of image region segmentation .
Here we derive a simple expression for the Bayes factor for two binomial models . We show using our formula that over large numbers of trials , a small difference in the accuracy of the two models can lead to a very strong preference for the more accurate model , as measured by the Bayes Factor . Let M1 , M2 be two binomial models , each predicting for a fixed stimulus , positive response ( r = 1 ) probabilities of p1 , p2 and negative response ( r = −1 ) probabilities 1 − p1 , 1 − p2 . Assume that we perform n experimental trials and observe proportion p* positive responses and ( 1 − p* ) negative responses . Let D = r1 , … , rn denote the observer responses . The Bayes Factor is B21=p ( D|M2 ) p ( D|M1 ) , ( A . 1 ) and taking the natural log we obtain lnB21=lnL2−lnL1 , ( A . 2 ) where Lk = p ( D|Mk ) for ( k = 1 , 2 ) . Expanding the log-likelihoods , we obtain lnLk=∑ri=1lnpk+∑ri=0ln ( 1−pk ) , ( A . 3 ) where pk = p ( r = 1|Mk ) . Since there are np* trials with positive responses and n ( 1 − p* ) trials with negative responses , we may simplify ( A . 3 ) to lnLk=n ( p*lnpk+ ( 1−p* ) ln ( 1−pk ) ) . ( A . 4 ) Noting that the bracketed term in ( A . 4 ) is simply the negative cross-entropy between two binomial models , and using the fact that the cross-entropy C ( p , q ) relates to the entropy H ( p ) and the KL divergence DKL ( p||q ) by the relation C ( p , q ) = H ( p ) + DKL ( p||q ) , we can re-write ( A . 4 ) as lnLk=−n ( H ( p* ) +DKL ( p*||pk ) ) . ( A . 5 ) Plugging ( A . 5 ) into ( A . 2 ) , the natural log of the Bayes factor becomes lnB21=n ( DKL ( p*||p1 ) −DKL ( p*||p2 ) ) , ( A . 6 ) and we obtain the final expression B21=en ( DKL ( p*||p1 ) −DKL ( p*||p2 ) ) . ( A . 7 ) We see from the final result ( A . 7 ) that the Bayes factor magnitude is exponential in the number of trials ( n ) , meaning that small differences in model performance ( measured by the difference in the KL divergences ) over a large number of trials can lead to very large values for the Bayes factor . Similarly , we see from ( A . 6 ) that the standard model selection criterion 2ln B21 [43] grows linearly as n , meaning that a small difference in performance may accumulate over trials to produce large values of this criterion . Concretely , consider the case where p* = 0 . 75 , p1 = 0 . 73 , p2 = 0 . 74 for n = 5000 trials . Qualitatively , we see that both models are slightly inaccurate , with Model 2 agreeing with the observer more than Model 1 , but with a difference of only 0 . 01 . Applying formula ( A . 6 ) gives us ln B21 = 3 . 8458 and therefore 2ln B21 = 7 . 6916 , corresponding to a “strong” preference for Model 2 according to standard criteria [43] . Re-working this example with a slightly larger difference in performance ( 0 . 02 ) by setting p1 = 0 . 72 yields a “very strong” preference for Model 2 ( 2ln B21 = 20 . 2224 ) , and a 2 . 46 × 104 fold preference for Model 2 . This effect is even more pronounced for predictions of very high ( or low ) success rates: setting p* = 0 . 95 , p1 = 0 . 92 , p2 = 0 . 94 yields 2ln B21 = 60 . 4679 , greatly exceeding the standard criterion ( 10 ) for “very strong” evidence in favor of Model 2 . | Many naturally occurring visual boundaries are defined by spatial differences in features other than luminance , for example by differences in texture or contrast . Quantitative models of such “second-order” boundary perception cannot be estimated using the standard regression techniques ( known as “classification images” ) commonly applied to “first-order” , luminance-defined stimuli . Here we present a novel machine learning approach to modeling second-order boundary perception using hierarchical neural networks . In contrast to previous quantitative studies of second-order boundary perception , we directly estimate network model parameters using psychophysical trial data . We demonstrate that our method can reveal different spatial summation strategies that human observers utilize for different kinds of second-order boundary perception tasks , and can be used to compare competing hypotheses of how contrast modulation is integrated across orientation channels . We outline extensions of the methodology to other kinds of second-order boundaries , including those in natural images . | [
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] | 2019 | Modeling second-order boundary perception: A machine learning approach |
Psychostimulant addiction is a heritable substance use disorder; however its genetic basis is almost entirely unknown . Quantitative trait locus ( QTL ) mapping in mice offers a complementary approach to human genome-wide association studies and can facilitate environment control , statistical power , novel gene discovery , and neurobiological mechanisms . We used interval-specific congenic mouse lines carrying various segments of chromosome 11 from the DBA/2J strain on an isogenic C57BL/6J background to positionally clone a 206 kb QTL ( 50 , 185 , 512–50 , 391 , 845 bp ) that was causally associated with a reduction in the locomotor stimulant response to methamphetamine ( 2 mg/kg , i . p . ; DBA/2J < C57BL/6J ) —a non-contingent , drug-induced behavior that is associated with stimulation of the dopaminergic reward circuitry . This chromosomal region contained only two protein coding genes—heterogeneous nuclear ribonucleoprotein , H1 ( Hnrnph1 ) and RUN and FYVE domain-containing 1 ( Rufy1 ) . Transcriptome analysis via mRNA sequencing in the striatum implicated a neurobiological mechanism involving a reduction in mesolimbic innervation and striatal neurotransmission . For instance , Nr4a2 ( nuclear receptor subfamily 4 , group A , member 2 ) , a transcription factor crucial for midbrain dopaminergic neuron development , exhibited a 2 . 1-fold decrease in expression ( DBA/2J < C57BL/6J; p 4 . 2 x 10−15 ) . Transcription activator-like effector nucleases ( TALENs ) -mediated introduction of frameshift deletions in the first coding exon of Hnrnph1 , but not Rufy1 , recapitulated the reduced methamphetamine behavioral response , thus identifying Hnrnph1 as a quantitative trait gene for methamphetamine sensitivity . These results define a novel contribution of Hnrnph1 to neurobehavioral dysfunction associated with dopaminergic neurotransmission . These findings could have implications for understanding the genetic basis of methamphetamine addiction in humans and the development of novel therapeutics for prevention and treatment of substance abuse and possibly other psychiatric disorders .
Substance use disorders ( SUDs ) involving psychostimulants such as cocaine and methamphetamine ( MA ) are heritable; however , their major genetic determinants remain poorly defined [1–4] . In particular , genome-wide association studies ( GWAS ) of psychostimulant abuse have yet to discover the underlying genetic factors or causal sequence variants . SUDs involve multiple discrete steps including initial use , escalation , withdrawal , and relapse , each of which is believed to have a distinct genetic architecture . Therefore , we and others have used model organisms to explore the genetic basis of intermediate phenotypes , including initial drug sensitivity [5] . Model systems have great potential for studying addiction-relevant intermediate phenotypes [6] because they provide exquisite control over environmental conditions , including exposure to psychostimulants . Psychostimulants activate the mesocorticolimbic reward circuitry in humans [7] and stimulate locomotor activity in mice [8] . The primary molecular targets of psychostimulants are the membrane-spanning monoaminergic transporters . Amphetamines act as substrates and cause reverse transport and synaptic efflux of dopamine , norepinephrine , and serotonin [9–11] . Sensitivity to the locomotor stimulant response to MA is heritable and may share a genetic basis with the addictive , neurotoxic , and therapeutic properties of amphetamines [8 , 12–15] . More broadly , determining the genetic basis of sensitivity to amphetamines may provide insight into the neurobiology of other conditions involving perturbations in dopaminergic signaling , including attention deficit hyperactive disorder ( ADHD ) , schizophrenia , and Parkinson’s disease [16] . This hypothesis is supported by our recent identification of a genetic correlation between alleles that increased amphetamine-induced euphoria and alleles that decreased risk of schizophrenia and ADHD [17] . We and others have reported several quantitative trait loci ( QTLs ) in mice that influence MA sensitivity [12 , 18–24] . A distinct advantage of QTL analysis is that chromosomal regions can eventually be mapped to their causal polymorphisms . However , obtaining gene-level and nucleotide-level resolution can be extremely challenging when beginning with a lowly recombinant population such as an F2 cross . A classical approach is to fine map QTLs derived from an F2 cross using successively smaller congenic strains . Whereas this approach is efficient for Mendelian alleles , there are only a few examples in which this approach has been successful in identifying alleles for more complex , polygenic traits , such as histocompatibility [25] , substance abuse [26] and depressive-like behavior [27] . In the present study , we fine mapped a QTL on chromosome 11 that modulates methamphetamine sensitivity and that segregates between C57BL/6J ( B6 ) and DBA/2J ( D2 ) inbred strains [12 , 20] . We used interval-specific congenic lines in which successively smaller D2-derived segments were introgressed onto a B6 background [28] . We also conducted transcriptome analysis of brain tissue from a congenic line that captured the QTL for reduced MA sensitivity . Our transcriptome analysis focused on the striatum , which is a brain region important for psychostimulant-induced locomotor activity and reward [29] . We used GeneNetwork [30] and in silico expression QTL ( eQTL ) analysis of several brain regions to identify cis- and trans-eQTLs that may explain changes in the transcriptome caused by this QTL . Finally , to identify the quantitative trait gene responsible for reduced MA sensitivity , we used transcription activator-like effector nucleases ( TALENs ) to introduce frameshift deletions in the first coding exon of each positional candidate gene [31] .
Several genome-wide significant QTLs that influenced MA sensitivity were previously reported in this B6 x D2-F2 cross , including QTLs on chromosomes 1 , 8 , 9 , 11 , 15 , and 16 [20] . Here , we further dissected the chromosome 11 QTL ( peak = 50 Mb; D2 < B6 ) into 5 min bins and identified a peak LOD score at 25 min post-MA administration ( Fig 1 ) . We then produced interval-specific congenic lines to fine map this QTL . The genomic intervals ( Mb ) for the congenic lines and the peak F2-derived QTL are illustrated in Fig 2A and the SNP markers that defined the congenic intervals for Lines 1–6 are listed in S1 Table . As shown in Fig 2B–2E , some of the congenic lines captured a QTL that reduced MA sensitivity whereas others did not ( see also S2A and S2B Fig and S1 Text ) . Whether or not a strain captured a QTL is indicated by a + or–sign in Fig 2A . Congenic Line 4 was the smallest congenic that captured a QTL for reduced MA sensitivity . Therefore , we produced subcongenic lines from Line 4 , as shown in Fig 3A . The SNP markers that defined the congenic intervals for Lines 4a-4h are listed in S2 Table . Production and analysis of these congenic lines was more efficient because the D2-derived allele was dominant . Therefore all lines shown in Fig 3 were heterozygous for the D2-derived congenic interval . Once again , some but not all of the congenic lines captured the QTL inherited from Line 4 ( Figs 3B , 3C , 3D and S3 , S3 Table and S1 Text ) . Based on the observation that Line 4b but not 4c captured the QTL , we were able to define a 206 kb critical interval ( Fig 3E ) . The first proximal SNP in Lines 4b was rs29424921 and first proximal SNP in Line 4c was rs29442500 . The physical location of these SNPs defined the boundaries of the critical interval ( 50 , 185 , 512–50 , 391 , 845 bp; S2 Table ) . This interval contains only two protein coding genes: Hnrnph1 ( heterogeneous nuclear ribonucleoprotein ) and Rufy1 ( RUN and FYVE domain containing 1; Fig 3E and S4 Table ) . Using Line 4c to define the distal boundary presumes that our analysis of Line 4c was powerful enough to detect the QTL if it were present . We used data generated from Line 4b to estimate the QTL effect size; based on this estimate , a sample size of N = 25 per group would be required to achieve 80% power to detect this QTL in Line 4c . We phenotyped an even larger number of mice from Line 4c ( N = 30–40 per genotype ) , but did not detect the QTL ( Fig 3D ) . Therefore , we can confidently interpret the negative results from Line 4c . Further negative results obtained from five additional subcongenic lines also support the critical interval as defined in Fig 3E ( see S3 Fig and S3 Table ) . Studies of congenic lines can be confounded by residual heterozygosity that lies outside of the congenic region . In order to address this concern , we genotyped individuals from Line 4 subcongenics at 882 SNPs using a SNP genotyping microarray . Although we did identify a single D2-derived SNP on chromosome 3 , it was observed both in wild-type and heterozygous congenic mice and was not associated with the locomotor response to MA ( see S4 Fig and S1 Text ) . Based on these results we rejected the possibility that the differences in the congenic lines were due to residual heterozygosity . In an effort to understand the molecular impact of this QTL , we used RNA-seq to identify gene expression differences in the striatum of naïve Line 4a congenics versus their naïve B6 littermates . We identified between 91 differentially expressed genes with an FDR of 5% and 174 differentially expressed genes with and FDR of 20% . The majority of these genes were downregulated in Line 4a ( S6 Table ) . Notably , Nr4a2 ( Nurr1 ) was the most significant , demonstrating a 2 . 1-fold decrease in expression ( p = 4 . 2 x 10−15; Fig 4 ) . Decreased Nurr1 expression in Line 4a was confirmed using qPCR ( S5A Fig and S7 Table ) . We used the Ingenuity Pathway Analysis ( IPA; Ingenuity Systems , Redwood City , CA , USA; www . qiagen . com/ingenuity ) software in conjunction with the genes we identified with an FDR of 5% to explore pathways that were enriched for these genes . The top three canonical pathways that we identified included the neuronal functions Glutamate Receptor Signaling , Gαq Signaling , and G-Protein Coupled Receptor Signaling ( S8 Table ) . Neither transcriptome nor qPCR analysis detected any significant difference in gene- or exon-level expression of Hnrnph1 or Rufy1 ( S5B , S5C and S6 Figs ) . The most strongly implicated IPA network was , “Cellular Development , Nervous System Development and Function , Behavior” . This network consists of several downregulated genes involved in neural development , maintenance , and signaling ( Fig 4 ) , including Bdnf , which was downregulated and connected to several downregulated genes involved in synaptic transmission , including Malat1 , the vesicular glutamate transporters VGLUT1 ( Slc17a7 ) and VGLUT2 ( Slc17a6 ) , as well as the AMPA-4 receptor subunit ( Gria4 ) , alpha-1d adrenergic receptor ( Adra1d ) , and calcium-dependent secretion activator 2 ( Cadps2 ) . The top “Diseases and Functions” annotations included Huntington’s disease , nervous system coordination , and disorder of basal ganglia ( S9 Table ) , further supporting dysfunction in striatal innervation and signaling . Htt ( huntingtin ) was the top predicted upstream transcriptional regulator followed by Creb1 ( cyclic AMP response element binding protein ) which together accounted for 23 ( 25% ) of the 91 differentially expressed genes ( S7 Fig ) . Gene Ontology ( GO ) pathways identified via WebGestalt [32 , 33] complemented the IPA results and generally indicate neuronal dysfunction . The top biological process was synaptic transmission and signaling processes , the top molecular functions involved membrane proteins including transporters and g protein-coupled receptors and the top cellular components were associated with neuronal synapses ( Table 1 ) . In order to identify genetic polymorphisms associated with changes in gene expression observed in the congenic region of Line 4a , we used GeneNetwork [30] to identify both cis- and trans-eQTLs that originated from B6/D2 polymorphisms within the Line 4a congenic region ( FDR < 20%; S6 Table ) . We identified several trans-QTLs caused by SNPs within the Line 4a region , including a link between genetic variation in Hnrnph1 and differential expression of Ipcef1 ( Tables 2 and S6 ) [30] , a gene that lies within Oprm1 ( mu opioid receptor ) and is transcribed in the reverse direction . These observations support the gene expression differences we observed using RNA-seq and indicate that our QTL regulates the expression of numerous other genes outside of the QTL interval . One of the major advantages of genetic analysis in model organisms is the ability to perform experimental manipulations to evaluate observed correlations between genotype and phenotype . We used TALENs to introduce frameshift deletions that resulted in premature stop codons into the first coding exon of each of the two protein coding genes within the 206 kb critical interval–Hnrnph1 and Rufy1 . We identified two founders that were heterozygous for 11 bp and 16 bp frameshift deletions in the first coding exon of Hnrnph1 ( Hnrnph1 +/-; Founders #28 and #22; Figs 5A and S8 ) . We did not observe any off-target deletions in the highly homologous Hnrnph2 gene nor did we observe compensatory change in striatal Hnrnph2 expression ( S9 Fig ) . Hnrnph1 +/- mice showed reduced expression of Hnrnph1 . When we used qPCR primers that hybridized to DNA sequences that were contained in both wild-type ( Hnrnph1 +/+ ) and Hnrnph1 +/- mice , there was a significant upregulation of total Hnrnph1 transcript levels in Hnrnph1 +/- versus Hnrnph1 +/+ mice ( Fig 5C and 5D ) . However , we also used qPCR primers that overlapped the deleted interval and in this case we observed a significant downregulation of Hnrnph1 +/+ transcript levels in Hnrnph1 +/- mice ( Fig 5E ) . These observations provide functional evidence that the Hnrnph1 frameshift deletion disrupted gene transcription . Similar to Lines 4 , 4a and 4b , Hnrnph1 +/- mice from Line #28 and Line #22 that were derived from Founders #28 and #22 both exhibited reduced MA sensitivity ( Fig 5F and 5G ) , thus recapitulating the congenic QTL phenotype . Reduced MA sensitivity was also observed using 30 min behavioral sessions ( S10 Fig ) . In contrast to Hnrnph1 +/- mice , Rufy1 +/- mice carrying a frameshift deletion ( S8 Fig ) did not exhibit any difference in behavior ( Fig 6 ) . To further support the likelihood of reduced neurobehavioral function in Hnrnph1 +/- mice , Hnrnph1 expression is also clearly higher than Rufy1 in the adult brain ( S6 Fig; S11 Fig ) [34] . To summarize , we observed a significant reduction in MA sensitivity in Hnrnph1 +/- mice , but not Rufy1 +/- mice that recapitulated the congenic QTL phenotype , thus identifying Hnrnph1 as a quantitative trait gene for MA sensitivity .
We used positional cloning and gene targeting to identify Hnrnph1 as a novel quantitative trait gene for MA sensitivity . First , we identified a broad , time-dependent QTL on chromosome 11 using an F2 cross between two inbred strains ( Fig 1 ) . We then narrowed a QTL from the initial 40 Mb interval to approximately 10 Mb using interval-specific congenic lines ( Figs 2 , 3 , S2 and S3 ) . Further backcrossing yielded a fortuitous recombination event that narrowed a critical interval to just 206 Kb; this region contained only two protein coding genes: Hnrnph1 and Rufy1 ( Fig 3E ) . Striatal transcriptome analysis identified potential neurobiological mechanisms , including a predicted deficit in midbrain dopaminergic neuron development and neurotransmission . The use of GeneNetwork [30] to identify eQTLs associated with our transcriptomic findings provided mechanistic insight , including a trans-QTL that maps to Hnrnph1 that could cause differential expression of Ipcef1 ( Table 2; S6 Table ) . Finally , we took advantage of the power of mouse genetics to create mice heterozygous for a frameshift deletion in either Hnrnph1 or Rufy1 . Hnrnph1 +/- mice but not Rufy1 +/- mice recapitulated the congenic QTL phenotype , providing direct evidence that Hnrnph1 is a quantitative trait gene for MA sensitivity ( Figs 5 and 6 ) . QTL mapping studies of rodent behavior have rarely provided strong evidence for causal quantitative trait genes [26 , 27 , 35] . We began pursuing this QTL more than a decade ago , when the difficulty of such projects was widely underestimated . A key limitation of our initial mapping strategy was the use of an F2 cross , in which extensive linkage disequilibrium created large haplotype blocks , resulting in the identification of very broad QTLs . Combining low resolution and high resolution QTL mapping in congenic lines revealed a more complex genetic architecture , indicating that Hnrnph1 is not the only causal gene within the F2 interval that underlies the QTL . Inheritance of two copies of the D2 segment enhanced the heterozygous phenotype in Line 1 , yet had no further effect once the size of the segment was reduced following the creation of Line 4 ( Fig 2B and 2E ) . We interpret this observation to suggest that Line 1 contains an additional , recessive QTL within the 35–50 Mb region of Line 3 that could summate with the Line 4 QTL to produce the larger effect size . This 35–50 Mb region could be fine-mapped to the causal genetic factor by introducing additional recombination events into Line 3 . This detailed level of insight into the genetic architecture of a single large-effect QTL could only be made possible by employing a sufficiently powered phenotypic analysis of interval-specific congenic lines . Thus , a key to our success in identifying a single gene was the fact that while the QTL originally identified in the F2 cross was likely the product of multiple smaller QTLs , we were able to capture one major QTL in Line 4 and in subcongenic lines which appears to correspond to a single quantitative trait gene that we have now identified as Hnrnph1 . Transcriptome analysis of Line 4a supports a neurodevelopmental mechanism by which the QTL regulates MA sensitivity . Nr4a2 ( a . k . a . Nurr1 ) was the top downregulated gene and codes for a transcription factor that is crucial for midbrain dopaminergic neuron development , survival , and cellular maintenance of the synthesis , packaging , transport , and reuptake of dopamine [36] . Nurr1 was a core component of a top-ranked gene network composed of primarily downregulated genes important for neurogenesis , neural differentiation , and synaptogenesis ( Nr4a2 / Nurr1 , Bdnf , Tbr1 , Neurod6 , Ets2 , Malat1 , Elavl2; Fig 4 ) . Accordingly , there was a downregulation of striatal signaling pathways , including glutamate ( Slc17a7 , Slc17a6 , Gng2 , and Gria4 ) , Gαq ( Gng2 , Chrm1 , Adra1b , Adra1d ) , and GPCR signaling ( Pde1b , Rgs14 , Chrm1 , Adra1b , Adra1d ) ( S8 Table ) . With regard to Gαq signaling , MA acts as a substrate for NET , causing efflux of NE [9] which then binds to α-adrenergic receptors that are coded by Adra1b and Adra1d . Notably , knockout mice for either of these receptors exhibit reduced amphetamine-induced locomotor activity [37 , 38] . Some of the differentially expressed genes in Line 4a were previously associated with variation in amphetamine reward and reinforcement , including Nr4a2 ( Nurr1 ) , Adora2a , and Slc17a7 ( Vglut1 ) [39] . Furthermore , the top predicted upstream regulator—Htt ( huntingtin; S7A Fig ) is a master regulator of a network of genes in the extended amygdala associated with protracted abstinence from chronic exposure to opioids , cannabinoids , nicotine , and alcohol [40] . Inheritance of the Hnrnph1 locus caused downregulation of a smaller reverse-transcribed gene located within the middle of Oprm1 ( mu opioid receptor ) called Ipcef1 ( p = 0 . 001; FDR = 12%; S6 Table ) . We also identified a trans-eQTL in Hnrnph1 that regulates Ipcef1 expression ( Table 2 [30] ) . Hnrnph1 was previously shown to regulate the expression Oprm1 ( mu opioid receptor gene ) via 5’ UTR-mediated repression [41] and splicing [42] . Furthermore , the human intronic SNP rs9479757 in OPRM1 was associated with heroin addiction severity and decreased binding affinity of HNRNPH1 , resulting in exon 2 skipping [43] . Thus , Hnrnph1 regulation of Ipcef1 expression could represent an additional mechanism of Oprm1 regulation [44] . The QTL that contains Hnrnph1 is predicted to perturb the neural development of the mesocorticolimbic circuitry that mediates MA behavior . Hnrnph1 ( heterogeneous nuclear ribonucleoprotein ) codes for an RNA binding protein ( RBP ) that is highly expressed throughout the brain , including the striatum , cortex , and hippocampus ( S11 Fig ) [34] and binds to G-rich elements to either enhance or silence splicing [45 , 46] . hnRNPs such as Hnrnph1 form hnRNP-RNA complexes to coordinate splicing of thousands of genes [46] . In addition , HNRNPH1 regulates 3’ UTR cleavage and polyadenylation [47] and several hnRNPs export mRNAs to neuronal processes to regulate spatiotemporal translation and post-translational modifications [48] . Synaptic activity can increase protein abundance of hnRNPs at the post-synaptic density of primary neurons [49] . The hippocampus contains focal expression of over 15 hnRNPs , including H1 ( S11 Fig [34] ) . Importantly , Hnrnph1 contains a glycine rich domain that permits nucleocytoplasmic shuttling via transportin 1 [50] and exhibits activity-dependent translocation to the cytoplasm [51] . Several hnRNPs exhibit activity-dependent localization at the synapse [49] , suggesting additional neuronal functions of Hnrnph1 in addition to splicing . We identified Hnrnph1 as a quantitative trait gene responsible for MA sensitivity . However , the quantitative trait nucleotide ( s ) remain obscure . Hnrnph1 contains 18 genetic variants within the gene , including 15 intronic SNPs , a SNP in the 5’ UTR , a synonymous coding SNP , and a single T insertion in the 3’ UTR ( S4 Table [52 , 53] ) that could cause brain region-specific differential expression of Hnrnph1 and/or its ability to regulate splicing of its transcriptome-wide targets [46 , 47] . We did not observe differential striatal expression of Hnrnph1 at the gene level or the exon level as a consequence of inheriting the Line 4a QTL ( S5 and S6 Figs ) . Our focus was limited to the striatum which is a behaviorally relevant region [16 , 29] that exhibits high Hnrnph1 expression during early adulthood ( S11 Fig ) . Therefore , the QTL could influence Hnrnph1 expression at a different time period , in a different , behaviorally relevant brain region , or in a specific subpopulation of cells . Interestingly , striatal microarray datasets in BXD strains indicate an increase in Hnrnph1 expression from postnatal day 3 to postnatal day 14 as well as a change in the strain rank order of expression [30] which suggests that genotypic differences in Hnrnph1 expression could depend on the developmental time point . Finally , because excised introns can trans-regulate gene expression , an alternative explanation is that excised , SNP-containing introns from Hnrnph1 can function as polymorphic long noncoding RNAs to perturb their trans-regulation of the transcriptome [54] . To our knowledge , there are no GWAS studies reporting genome-wide significant associations of HNRNPH1 variants with complex diseases or traits ( http://www . ebi . ac . uk/gwas/ ) . Interestingly , HNRNPH1 binding affinity and splicing can be modulated by genome-wide significant SNPs associated with bipolar disorder , major depressive disorder , and schizophrenia , including rs1006737 ( CACNA1C ) , rs2251219 ( PBRM1 ) , and rs1076560 ( DRD2 ) [55] . Thus , HNRNPH1 splicing could profoundly impact the neurobiological mechanisms underlying these disorders . Additionally , HNRNPH1 and RBFOX1/2 coordinate splicing [56 , 57] and knockdown RBFOX1 ( an autism-associated RBP involved in neural development [58] ) in human neural progenitor cells revealed over 200 alternatively spliced genes containing HNRNPH1 binding sites [56] and 524 genes containing binding sites for ELAVL2 , a neurodevelopmental RBP [59] that was downregulated in Line 4a ( Fig 4 ) . In summary , we identified Hnrnph1 as a quantitative trait gene for MA sensitivity . This is rarely accomplished in rodent forward genetic studies of behavior and will likely advance our understanding of the neurobiological basis of multiple neuropsychiatric disorders involving monoaminergic dysregulation . Identifying brain region- and cell type-specific splicing targets of Hnrnph1 could reveal therapeutic targets for these disorders , many of which have been associated with specific gene splicing events [55] . Furthermore , pharmacological perturbation of RBP function could one day serve as an effective therapeutic strategy . Recent findings in models of neurodegenerative disease show that targeting RBP signaling could be a promising treatment approach [60] .
All procedures in mice were approved by the Boston University and the University of Chicago Institutional Animal Care and Use Committees and were conducted in strict accordance with National Institute of Health guidelines for the care and use of laboratory animals . Colony rooms were maintained on a 12:12 h light–dark cycle ( lights on at 0600 h ) . Mice were housed in same-sex groups of two to five mice per cage with standard laboratory chow and water available ad libitum . Age-matched mice were 50–100 days old at the time of testing ( 0900–1600 h ) . For Lines 1–6 and Lines 4a-4h , locomotor activity was assessed in the open field [19] . Briefly , congenics , subcongenics , and wild-type littermates were transported from the vivarium to the adjacent behavioral testing room where they habituated for at least 30 min prior to testing . Mice were then placed into clean holding cages with fresh bedding for approximately five min before receiving an injection of saline on Days 1 and 2 ( 10 μl/g , i . p ) and an injection of methamphetamine on Day 3 ( MA; 2 mg/kg , i . p . ; Sigma-Aldrich , St . Louis , MO USA ) . Mice were placed into the center of the open field ( 37 . 5 cm x 37 . 5 cm x 35 . 7 cm; AccuScan Instruments , Columbus , OH USA ) surrounded by a sound attenuating chamber ( MedAssociates , St . Albans , VT USA ) and the total distance traveled was recorded in six , 5 min bins over 30 min using VersaMax software ( AccuScan ) . Mice heterozygous for a frameshift deletion in Hnrnph1 ( Hnrnph1 +/- ) or Rufy1 ( Rufy1 +/- ) were engineered ( http://www . bumc . bu . edu/transgenic/ ) , bred , and phenotyped at Boston University School of Medicine . Mice were bred and phenotyped in a manner similar to the congenics at the University of Chicago , with the exception that the open field was a smaller size ( 43 . 2 cm long x 21 . 6 cm wide x 43 . 2 cm tall; Lafayette Instruments , Lafayette , IN USA ) and mice were recorded daily for 1 h rather than 30 min to allow a more robust detection of the phenotype . Reduced MA sensitivity was also replicated in Hnrnph1 +/- mice using the 30 min protocol ( Supplementary Information ) . Behavior was videotaped using a security camera system ( Swann Communications , Melbourne , Australia ) and data were collected and analyzed using video tracking ( Anymaze , Stoelting , Wood Dale , IL USA ) . Because our primary focus was on MA-induced locomotor activity on Day 3 , we first ran a two-way repeated measures ANOVA for Day 3 using genotype and sex as factors and time as the repeated measure . Because sex did not interact with genotype or time for any of the lines on Day 3 , we combined sexes for the analysis of Days 1–3 and used repeated measures ANOVA with genotype as the main factor . Main effects of genotype and genotype x time interactions were deconstructed using one-way ANOVAs and Fisher’s post-hoc test of each time bin or t-tests in cases where there were two genotypes . A p-value of less than 0 . 05 was considered significant . B6 x D2-F2 mice ( N = 676 ) were generated , maintained , genotyped , and analyzed as previously described [20 , 22] . Genome-wide QTL analysis was performed in F2 mice using the R package QTLRel that contains a mixed model to account for relatedness among individuals [61] . We recently validated the use of permutation when estimating significance thresholds for mixed models [62] . Sex was included as an interactive covariate . For each analysis , significance thresholds ( p < 0 . 05 ) were estimated using 1000 permutations . The F2 data and R code for are publicly available on github ( https:/github . com/wevanjohnson/hnrnph1 ) . Lines 1 and 6 were obtained from Dr . Aldons Lusis’s laboratory at UCLA ( Lines “11P” and “11M” [28] ) and had previously been backcrossed to B6 for more than 10 generations . These lines contained homozygous , introgressed regions from D2 on an isogenic B6 background that spanned chromosome 11 . Because Lines 1 and 6 contained such large congenic intervals , we first phenotyped non-littermate offspring derived from homozygous congenic breeders versus homozygous B6 wild-type breeders ( The Jackson Laboratory , Bar Harbor , ME; Figs 2 and S2 ) rather than heterozygous-heterozygous breeders to avoid the otherwise high likelihood of introducing unmonitored recombination events . Thus , we ensured that each individual possessed an identical genotype within each congenic line . The same type of control group is typically employed in the initial screen of chromosome substitution strains [19 , 63 , 64] which are essentially very large congenic lines . We crossed Line 1 to B6 and phenotyped the F1 offspring alongside age-matched B6 mice . B6 cohorts were combined into a single group for the combined analysis of all three genotypes for Line 1 ( homozygous for B6 , homozygous for D2 , and heterozygous; Fig 2 ) . Next , we backcrossed Line 1 heterozygotes to B6 to generate subcongenic Lines 2–5 ( Figs 2 and S2 ) . Recombination events were monitored using genomic DNA extracted from tail biopsies and a series of TaqMan SNP markers ( Life Technologies; Carlsbad , CA; S1 Table ) . We then used heterozygous-heterozygous breeding in Lines 2–5 to produce littermates of all three genotypes for simultaneous phenotyping ( Figs 2 and S2 ) . Because the QTL in Line 4 represented the smallest congenic region and was dominantly inherited , we backcrossed Line 4 heterozygotes to B6 to generate heterozygotes and wild-type littermates for Lines 4a-4h ( Figs 3 and S3 ) . We used additional TaqManSNP markers ( Life Technologies ) to monitor recombination events and defined the precise congenic boundaries using PCR and Sanger sequencing of SNPs chosen from the Mouse Sanger SNP query database ( http://www . sanger . ac . uk/cgi-bin/modelorgs/mousegenomes/snps . pl [52] ) . Genomic coordinates are based on mm9 ( Build 37 ) . We assayed tail SNP DNA from one heterozygous congenic mouse and one B6 wildtype littermate from Lines 4a-4d ( eight mice total ) using services provided by the DartMouseSpeed Congenic Core Facility at the Geisel School of Medicine at Dartmouth College ( http://dartmouse . org/ ) . A total of 882 informative B6/D2 SNPs were analyzed on the GoldenGate Genotyping Assay ( Illumina , Inc . , San Diego , CA ) using DartMouse’s SNaP-Mapand Map-Synth software to determine the allele at each SNP location . After detecting a single off-target locus on chromosome 3 ( rs13477019; 23 . 7 Mb ) , we used a custom designed TaqMan SNP marker for rs13477019 ( Life Technologies , Carlsbad , CA USA ) to confirm the result and to genotype additional samples from Lines 4a-4h for which we had both DNA and behavioral phenotypes . Data from this SNP marker were then used to test for the effect of genotype at the chromosome 3 locus on MA-induced locomotor activity . We harvested and pooled bilateral 2 . 5 mm diameter punches of the striatum for each individual sample from naïve , congenic mice and B6 wildtype littermates from Line 4a ( N = 3 females and 5 males per genotype; 50–70 days old ) . Total RNA was extracted as previously described [23] and purified using the RNeasy kit ( Qiagen , Valencia , CA , USA ) . RNA was shipped to the University of Chicago Genomics Core Facility where cDNA libraries were prepared for 50 bp single-end reads according to the manufacturer’s instructions using the Illumina TruSeqStranded mRNA LT Kit ( Part# RS-122-2101 ) . Purified DNA was captured on an Illumina flow cell for cluster generation and sample libraries were sequenced at eight samples per lane over two lanes ( technical replicates ) on the Illumina HiSeq 2500 machine according to the manufacturer’s protocols . FASTQ files were quality checked via FASTQC and possessed Phred quality scores > 30 ( i . e . less than 0 . 1% sequencing error ) . Using the FastX-Trimmer from the FastX-Toolkit , the 51st base was trimmed to enhance read quality and prevent misalignment . FASTQ files were utilized in TopHat [65] to align reads to the reference genome ( UCSC Genome Browser ) . Read counts per gene were quantified using the HTSeq Python package and the R Bioconductor package edgeR was used to analyze differential gene expression . EdgeR models read counts using a negative binomial distribution to account for variability in the number of reads via generalized linear models [66] . “Home cage” was included as a covariate in the statistical model to account for cage effects on gene expression . The p-values obtained for differential expression were then adjusted by applying a false discovery rate ( FDR ) method to correct for multiple hypothesis testing [67] . The transcriptome dataset and code for RNA-seq analysis are available via NCBI Gene Expression Omnibus ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=cxkdoeaudvyhlqt&acc=GSE66366 ) . Oligo-dT primers were used to synthesize cDNA from total RNA to examine mRNA expression . Primer efficiencies for real-time quantitative PCR ( qPCR ) experiments were calculated using cycle threshold ( CT ) values ( SYBR® Green; Life Technologies ) derived from five , 10-fold serial cDNA dilutions; efficiencies ( E ) ranged from 90–100% ( R2 = 0 . 99–1 ) . Each sample was run in triplicate and averaged . Differential gene expression was reported as the fold-change in congenic or frameshift-deleted mice relative to B6 wild-type littermates using the 2- ( ∆∆CT ) method [68] . We used our differentially expressed gene list from the striatal transcriptome that contained both the log2 fold-change and p-values ( FDR < 5% ) and applied IPA ( www . qiagen . com/ingenuity ) to identify enriched molecular pathways , functional annotations , gene networks , upstream causes , and predicted neurobiological consequences caused by inheritance of the QTL . IPA utilizes an algorithm that assumes that an increase in the number of molecular interactions indicates an increase in the likelihood of an effect on biological function . IPA uses a manually curated database ( IPA Knowledge Base ) containing the published literature to extract gene networks containing equally treated edges that directly and indirectly connect biologically related genes ( www . qiagen . com/ingenuity ) . IPA analyses were conducted in February 2015 . To identify published cis- and trans- eQTLs that could explain gene expression differences caused by inheritance of the Line 4a congenic interval , we queried differentially expressed genes ( FDR < 20%; 174 genes total; S6 Table ) in transcriptome datasets from several brain regions in GeneNetwork [30] involving BXD recombinant inbred strains ( recombinant inbred strains derived from B6 and D2 strains ) . We considered cis- and trans-QTLs originating from SNPs located within the 50–60 Mb locus and employed an arbitrary cut-off of LRS ≥ 13 . 8 ( LOD ≥ 3 ) . We only included genes where there was an exact match of gene with the LRS location using the appropriate genome build coordinates for each dataset . TALENs vectors encoded either the right or left arm of the TALE effector that targeted the first coding exons of Hnrnph1 or Rufy1 ( Cellectis Bioresearch , Paris , France ) . Upon bacterial cloning and purification , TALENs vectors containing a T7 promoter were linearized and used as templates for in vitro mRNA synthesis ( mMessage mMachine T7 transcription kit; Life Technologies ) , and purified using MEGAclear transcription clean-up kit ( Life Technologies ) . Each mRNA cocktail was diluted in sterile buffer and injected into B6 single-cell embryos at the BUMC Transgenic Core facility ( http://www . bumc . bu . edu/transgenic/ ) . We developed a genotyping assay utilizing native restriction enzyme recognition sites within the TALENs FokI cleavage domain . Genomic DNA was extracted from mouse tail biopsies and PCR-amplified with primers targeting100 base pairs upstream and downstream of the TALENs binding domain . Amplicons were then exposed to restriction digest overnight , run on a 2% agarose Ethidium Bromide Tris-Borate-EDTA gel , and imaged with ultraviolet light . TALENs-targeted deletions were identified by the presence of undigested bands caused by a loss of the restriction site . To confirm base pair deletions in our founder lines , undigested restriction enzyme-exposed PCR amplicon bands were excised , gel-purified , and vector-ligated overnight at 4°C using the pGEM T-easy Vector Systems ( Promega ) . The ligation reaction was transformed into MAX Efficiency DH5α Competent Cells ( Invitrogen ) and plated onto Ampicillin-IPTG/X-Gal LB agarose plates for blue-white selection . Following overnight incubation at 37°C , white colonies were picked , cultured in ampicillin-enriched LB medium , and amplified . The PCR product was purified using the QIAprep Miniprep kit ( QIAGEN ) . We then sequenced the vectors for the deletions using the pGEM T7 site upstream of the insert . An Hnrnph1 forward primer ( GTTTTCTCAGACGCGTTCCT ) and reverse primer ( ACTGACAACTCCCGCCTCA ) were designed to target upstream and downstream of the TALENs binding domain in exon 4 of Hnrnph1 . Genomic DNA was used to amplify a 204 bp PCR product using DreamTaq Green PCR Mastermix ( ThermoScientific ) . PCR products were treated with the BstNI restriction enzyme ( New England Biolabs ) or a control enzyme-free buffer solution and incubated overnight at 60°C to ensure complete digestion . Enzyme-treated PCR products and untreated controls were resolved in 2% agarose gel electrophoresis with 0 . 5 μg/mL ethidium bromide to visualize under UV light . There were two BstNI restriction sites within the Hnrnph1 amplicon that were located proximal and distal to the TALENs FokI cleavage zone . Mice heterozygous for the Hnrnph1 deletion showed two bands on the gel , while B6 controls showed a single band . Similar to Hnrnph1 , a Rufy1 forward primer ( AATCGTACTTTCCCGAATGC ) and reverse primer ( GGACTCTAGGCCTGCTTGG ) targeted upstream and downstream of the TALENs binding domain in the first coding exon ( exon 1 ) . The 230 bp PCR amplicon contained a SacII restriction site that was deleted in Rufy1 +/- mice . Thus , Rufy1 +/+ mice showed a single , smaller digested band whereas Rufy1 +/- mice showed both the digested band as well as a larger , undigested band . To assess off-target activity in Hnrnph1-targeted mice , we used the UCSC genome browser to BLAT the TALENs binding domains and identified a single homologous region located within the first coding exon of Hnrnph2 . We used the same PCR- and gel-based assay to test for the deletion in Hnrnph2 with the exception that we used forward ( GCCACCAAGAGTCCATCAGT ) and reverse primers ( AATGCTTCACCACTCGGTCT ) that uniquely amplified a homologous 197 bp sequence within Hnrnph2 that contained a single Bstn1 restriction site . Digestion at the Bstn1 site produced an 81 bp band and a 115 bp band . | Both genetic and environmental factors can powerfully modulate susceptibility to substance use disorders . Quantitative trait locus ( QTL ) mapping is an unbiased discovery-based approach that is used to identify novel genetic factors and provide new mechanistic insight into phenotypic variation associated with disease . In this study , we focused on the genetic basis of variation in sensitivity to the acute locomotor stimulant response to methamphetamine which is a behavioral phenotype in rodents that is associated with stimulated dopamine release and activation of the brain reward circuitry involved in addiction . Using brute force monitoring of recombination events associated with changes in behavior , we fortuitously narrowed the genotype-phenotype association down to just two genes that we subsequently targeted using a contemporary genome editing approach . The gene that we validated–Hnrnph1 –is an RNA binding protein that did not have any previously known function in psychostimulant behavior or psychostimulant addiction . Our behavioral data combined with our gene expression results provide a compelling rationale for a new line of investigation regarding Hnrnph1 and its role in neural development and plasticity associated with the addictions and perhaps other dopamine-dependent psychiatric disorders . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Hnrnph1 Is A Quantitative Trait Gene for Methamphetamine Sensitivity |
Cell-substrate adherence is a fundamental property of microorganisms that enables them to exist in biofilms . Our study focuses on adherence of the fungal pathogen Candida albicans to one substrate , silicone , that is relevant to device-associated infection . We conducted a mutant screen with a quantitative flow-cell assay to identify thirty transcription factors that are required for adherence . We then combined nanoString gene expression profiling with functional analysis to elucidate relationships among these transcription factors , with two major goals: to extend our understanding of transcription factors previously known to govern adherence or biofilm formation , and to gain insight into the many transcription factors we identified that were relatively uncharacterized , particularly in the context of adherence or cell surface biogenesis . With regard to the first goal , we have discovered a role for biofilm regulator Bcr1 in adherence , and found that biofilm regulator Ace2 is a major functional target of chromatin remodeling factor Snf5 . In addition , Bcr1 and Ace2 share several target genes , pointing to a new connection between them . With regard to the second goal , our findings reveal existence of a large regulatory network that connects eleven adherence regulators , the zinc-response regulator Zap1 , and approximately one quarter of the predicted cell surface protein genes in this organism . This limited yet sensitive glimpse of mutant gene expression changes had thus defined one of the broadest cell surface regulatory networks in C . albicans .
Microorganisms naturally exist primarily in association with surfaces in communities called biofilms . Central to the formation of biofilms is the ability of microbial cells to adhere to substrates . Adherence mechanisms are diverse , and involve specific cell surface proteins ( adhesins ) , more complex surface structures such as pili , and secreted extracellular matrix material [1]–[4] . Adherence is often found to be highly regulated , reflecting the need for biofilms to release cells in order to colonize new sites . Biofilms are clinically significant as the basis for infections associated with implanted medical devices [5] , [6] . Adherence of a pathogen to a device surface is a critical early step in formation of these biofilms . For device-associated biofilms , definition of the mechanisms that regulate cell-substrate adherence provides insight into how these biofilms form . That understanding may in turn suggest simple therapeutic or preventive strategies . Our focus is the fungal pathogen Candida albicans , a natural commensal of our gastrointestinal and genitourinary tracts that is usually benign . It causes infections associated with venous catheters , urinary catheters , and several other implanted devices [7] , [8] . Our overall understanding of C . albicans biofilm formation has expanded dramatically in recent years , and several regulators and effectors that contribute to biofilm formation are known [1] , [9] , [10] . Several key effectors have been identified among targets of transcription factors that are required for normal biofilm formation . The approach of using a transcription factor mutant to identify functional targets has proven particularly useful because many effectors are specified by duplicated genes or gene families [1] . In this study we focus on an early step in abiotic surface biofilm formation , the adherence of yeast form cells to a substrate . We find that this process is governed by over 10% of the C . albicans transcription factors , thus indicating that adherence is coupled to numerous regulatory signals . We use nanoString profiling [11] to analyze gene expression changes for all of these transcription factor mutants . Although nanoString probes cover only a portion of the transcriptome , the sensitivity exceeds that of microarrays [11] . In addition , the probes recognize RNA directly , avoiding possible bias from cDNA conversion [11] . Our findings reveal new connections between these regulators that we validate with functional assays . In addition , our results define a group of 37 cell surface protein genes that are coordinately regulated by twelve transcription factors . This newly discovered regulon may couple cell-substrate adherence to environmental signals .
We assayed 197 transcription factor insertion mutants for altered cell-substrate adherence in a quantitative flow-cell assay , using a silicone ( poly-dimethyl siloxane ) substrate . We identified mutants in 30 genes with significantly reduced adherence compared to the wild type strain ( Figure 1A; Table S1 ) . We used three approaches to confirm that the known insertion mutation in each strain , rather than spurious mutations , caused its adherence defect ( summarized in Table 1 under “Confirmation approaches” ) . First , for 26 genes , independent insertion mutant isolates were available . We assayed adherence of those strains , and found that they also displayed reduced adherence ( Table S1 ) . Second , for 25 genes , independently constructed deletion mutants were obtained in the BWP17 or SN152 strain backgrounds [12] . Adherence assays of those strains also confirmed the mutants' reduced adherence ( Supplemental Tables S1B , S1C ) . Third , for 19 genes , we complemented the mutation by introducing a wild-type copy of the affected gene into the respective insertion or deletion mutant; we observed that wild-type levels of adherence were restored ( Table S1 ) . In total , our results verify the adherence defects for 29 of the mutants ( Table 1 ) . Cell-substrate adherence is often viewed as the first step in biofilm formation [1] , [13] . Indeed , our findings above indicate that BCR1 and ACE2 are required for cell-substrate adherence , and prior studies have shown them to be required for biofilm formation [14] , [15] . Therefore , all of the adherence-defective insertion mutants were tested for biofilm formation in vitro . Under our standard assay conditions [14] , mutants defective in SNF5 ( discussed below ) and ARG81 ( Figure S1 ) were unable to form adherent biofilms in vitro . Therefore , some adherence-defective mutants are defective in biofilm formation in vitro , while others represent a distinct functional class . The transcription factor Bcr1 has been proposed to promote cell-cell adherence [16] , but was not known to mediate cell-substrate adherence . We confirmed the substrate adherence defect of the bcr1−/− insertion mutant ( Figure 1A ) with the finding that a bcr1Δ/Δ deletion mutant had 3- to 4-fold reduced cell-substrate adherence compared to wild-type and complemented control strains ( Figure 1B ) . ( We refer to a homozygous insertion mutant as “yfg1−/−” , and a homozygous deletion mutant as “yfg1Δ/Δ” . ) We tested the major known functional targets of Bcr1 , which include adhesins Als1 , Als3 , and Hwp1 [16] , [17] , for roles in cell-substrate adherence . Deletion of ALS1 alone caused a significant adherence defect , and overexpression of ALS1 improved adherence in the bcr1Δ/Δ background ( Figure 1B ) . Deletion of either ALS3 or HWP1 did not affect adherence ( Figure 1B ) . These results indicate that Bcr1 is required for cell-substrate adherence , and that this function is mediated largely or entirely by the adhesin Als1 . We used nanoString gene expression profiling to elucidate possible targets and pathway relationships among transcriptional regulators of adherence . RNA levels were measured for 293 genes . The surveyed genes included all 113 predicted GPI-linked cell surface protein genes [18] , [19] , representative gene targets of known biofilm regulators Ace2 , Bcr1 , and Zap1 [14] , [15] , [20] , and a spectrum of genes related to hyphal formation , cell wall integrity , and stress responses ( Table S2 ) . We assayed gene expression in the 30 adherence-defective transcription factor mutants , five additional mutants with altered biofilm formation ability ( ire1−/− , gin4−/− , cbk1−/− , tec1−/− , zap1Δ/Δ [14] , [20] , [21] ) , and the reference wild-type strain DAY185 . Gene expression was assayed after growth for 8 hr at 37°C in liquid Spider medium , a medium we have used previously for analysis of biofilm-defective mutants [14] , [20] . We used these growth conditions , despite the fact that they are different from those we used in our adherence assay , for two reasons . First , we sought to compare gene expression measurements with this new platform to our previously published microarray data . In fact , the new data agreed well with previous datasets: the nanoString probe set confirmed expression patterns for 20 previously reported Bcr1-regulated genes and 5 previously reported Zap1-regulated genes [14] , [20] . Second , it seemed reasonable that gene expression comparisons among mutants might allow functional relationships to be inferred , regardless of the specific growth condition . Functional tests that we present below illustrate the value of the gene expression dataset for this purpose . The adherence-defective mutants presented a range of pleiotropy in gene expression alterations ( Table 1 ) . Mutations in WAR1 , ZFU2 , and ZNC1 had fairly mild effects , causing statistically significant changes in expression of only 16–22 of the genes assayed . Mutations in ADA2 , BCR1 , and SNF5 were relatively severe , causing statistically significant changes in expression of 138–178 genes . Only two of the newly identified mutants had significantly reduced expression of ALS1 ( try3−/− and try4−/− ) , and none had reduced expression of BCR1 , thus indicating that the new mutations may define distinct adherence mechanisms ( Table S2 ) . An overview of the dataset reveals four striking findings ( Figure 2A and 2B , Table S2 ) . First , expression of a cluster of genes that includes hyphal- and virulence-associated genes ( HYVIR cluster ) is altered in 16 of the adherence-defective mutants . Interestingly , some additional genes ( such as CRH11 , orf19 . 5626 , HSP104 ) cluster with the familiar hyphal/virulence genes , based on their co-regulation in several mutants , and may have previously unrecognized roles in these processes . Most of the mutants with altered hyphal/virulence gene expression have no previously described hyphal morphogenesis defect [12] . In the majority of these mutants , the hyphal/virulence genes are down-regulated compared to the wild type . Second , most targets of the transcription factor Ace2 ( RAM cluster , named for “Regulation of Ace2 and polarized morphogenesis” [22] ) , are regulated by transcription factors Snf5 , Cas5 , Bcr1 , and Met4 . We probe the significance of the Snf5-RAM relationship below . Third , expression of zinc uptake genes and other known targets of the transcription factor Zap1 ( ZAPT cluster [20] ) is altered by 17 adherence-defective transcription factor mutants . For this set of genes , roughly equal numbers of mutants display up- or down-regulation . Finally , a novel group of 48 genes ( CSTAR cluster [“Cell surface targets of adherence regulators”] ) displays altered expression in 11 adherence-defective transcription factor mutants . The CSTAR genes include 37 genes that specify cell wall or secreted proteins . These genes are also regulated by the transcription factor Zap1; we examine the Zap1-adherence relationship below . There were additional clusters of co-regulated genes , but we could not define common functional or structural features among them . This overview has identified a major group of co-regulated genes , the CSTAR cluster , and has defined shared features among many of the new adherence regulators . We used the gene expression data to deduce network relationships in order to define possible functional relationships among the adherence regulators ( Figure 2B ) . This analysis points toward several findings . First , many adherence regulators control expression of two or more broad classes of target genes . For example , almost all regulators of the newly defined CSTAR genes also govern expression of HYVIR genes . Second , many groups of transcription factors have similar effects on their common target gene classes . For example , Fgr27 , Zcf28 , Suc1 , Try2 , Try3 , Try4 , and Try5 are all positive regulators of CSTAR and HYVIR genes , and negative regulators of ZAPT genes . Hence they may function together in a complex or pathway . Third , some transcription factors have opposite functions , such as Ada2 and Uga33 or Zfu2 and Zcf34 . These relationships would be expected for one transcription factor that repressed expression of another , or for a repressor and an activator that recognize similar sequence motifs in front of target genes . Nine transcription factors ( Fcr3 , Zcf39 , Zcf8 , Zcf31 , War1 , Not3 , Znc1 , Taf14 , and Czf1 ) did not have well defined target gene classes , and their possible relationships to other adherence regulators were not obvious . However , the profiling data do identify prospective target genes for all of these transcription factors ( Figure 2A; Table S2 ) that may direct future studies . This network visualization suggests that many adherence regulators have common properties , and that many of these newly characterized transcription factors may converge to regulate a limited number of functional target genes or pathways . Snf5 is a subunit of the eukaryotic SWI/SNF chromatin remodeling complex [23] , [24] . Both a snf5Δ/Δ deletion mutant and our original insertion mutant were defective in silicone adherence ( Figure 1 , Figure 3A ) . In addition , snf5 mutants were defective in biofilm formation ( Figure 3A ) . Confocal microscopic images showed sparse adherent cells , and mutant biofilms had diminished biomass . The snf5 mutants also had pleiotropic phenotypic defects , including increased cell aggregation during yeast form growth , a severe defect in hyphal morphogenesis , and hypersensitivity to the cell wall inhibitors Congo Red and caspofungin ( Figure 3B ) . Complementation of the snf5Δ/Δ mutant with a single copy of SNF5 yielded phenotypes similar to the wild-type strain ( Figure 3 ) . These results indicate that loss of Snf5 function causes a spectrum of phenotypic defects . The pleiotropic phenotypes of a snf5Δ/Δ mutant may be mediated by multiple regulatory pathways , in keeping with the global impact of the SWI/SNF complex on chromatin structure [25] . A second model , based on our gene expression analysis , is that many of the snf5Δ/Δ defects are the result of reduced ACE2 expression . Although Ace2 is not known to govern cell wall integrity , it is known to affect adherence , biofilm formation , and hyphal morphogenesis [15] , [22] . The second model predicts that many snf5Δ/Δ defects will be reversed by overexpression of ACE2 in the mutant strain . To test that prediction , we fused the TDH3 promoter to the ACE2 coding region in the snf5Δ/Δ background , creating an ACE2-OE allele . Expression of ACE2 was increased to approximately 3 times the wild type expression level , as indicated by QRTPCR assays ( Figure S2 ) . NanoString profiling confirmed that the ACE2-OE construct restored RAM gene expression in the snf5Δ/Δ mutant to nearly wild-type levels ( preliminary results; Table S3 ) . Overexpression of ACE2 in the snf5Δ/Δ background restored adherence to wild-type levels ( Figure 3A ) . In addition , it restored biofilm formation ability in vitro , as assayed by both biomass and confocal microscopic imaging ( Figure 3A ) . Overexpression of ACE2 caused substantial reversal of additional pleiotropic phenotypes , including yeast cell aggregation , hyphal morphogenesis , and sensitivity to cell wall inhibitors Congo Red and caspofungin ( Figure 3B ) . These results indicate that much of the phenotypic impact of Snf5 stems from its role in ACE2 expression . To test the significance of our observations to infection , we turned to biofilm assays in vivo in a catheter infection model ( Figure 3A ) . The snf5Δ/Δ mutant had a severe biofilm defect in vivo , and this defect was reversed by complementation with one wild-type copy of SNF5 . Overexpression of ACE2 partially restored biofilm formation in vivo as well . We conclude that ACE2 is a pivotal Snf5 target gene that mediates multiple phenotypic properties , including biofilm formation in vitro and in vivo . Profiling data indicated that many adherence- and biofilm-defective mutants have altered expression of previously known Zap1-dependent genes ( ZAPT genes in Figure 2 ) . In addition , Zap1 is required along with several adherence regulators for expression of the newly described CSTAR genes . Given that a zap1Δ/Δ mutant has no detectable adherence defect ( Figure 1A ) , we considered the hypothesis that Zap1 may act redundantly with another regulator or pathway to promote adherence . Our adherence-defective transcription factor mutants would likely include such a regulator . The hypothesis predicts that overexpression of ZAP1 may improve adherence of mutants defective in the postulated redundant pathway . To test that prediction , we created derivatives of each transcription factor mutant that overexpress ZAP1 from the TDH3 promoter ( ZAP1-OE allele ) . This allele resulted in 2- to 4-fold overexpression of ZAP1 RNA in several representative mutants assayed ( Figure 4 ) . We confirmed the impact of ZAP1 deletion and overexpression on target gene expression through QRTPCR assays ( Figure 4 ) . This analysis , conducted on three biological replicates , confirmed that three CSTAR genes were expressed at lower levels in the zap1Δ/Δ mutant than the wild-type strain ( Figure 4 ) . These three genes were also expressed at reduced levels in three adherence-defective mutants ( zcf28Δ/Δ , try2−/− , and try3−/− ) , compared to the wild type . Importantly , expression of the three CSTAR genes increased when the ZAP1-OE allele was introduced into the mutants ( Figure 4 ) . These conclusions were extended with single nanoString determinations for several strains that were chosen on the basis of their adherence phenotypes presented below ( preliminary results; Table S3 ) . The ZAP1-OE construct increased CSTAR gene expression considerably in arg81Δ/Δ , zcf28−/− , uga33−/− , and try2−/− backgrounds ( Table S3 ) . In contrast , the ZAP1-OE construct had no effect on CSTAR gene expression in the zcf34−/− background . These observations suggest that ZAP1 overexpression can stimulate CSTAR gene expression in some , but not all , adherence-defective mutants . We then compared adherence of each of the 30 mutant strains with and without the ZAP1-OE allele ( Figure 5 , Table 1 ) . For ten mutants , the ZAP1-OE allele caused significantly increased adherence to a level comparable to the wild-type strain . This group included the arg81Δ/Δ , zcf28−/− , uga33−/− , and try2−/− mutants , in which ZAP1-OE caused increased CSTAR expression . The strains in which ZAP1-OE did not improve adherence included the zcf34−/− mutant , in which ZAP1-OE did not cause increased CSTAR gene expression . These findings argue that elevated expression of Zap1-dependent genes can alleviate the need for many transcription factors in promoting adherence .
Bcr1 is among the best characterized biofilm regulators [1] . Previous studies indicated that its adhesin targets Als1/3 and Hwp1 mediate cell-cell interaction in biofilms [27] . Our findings extend that view by showing that Bcr1 , through Als1 , also governs cell-substrate adherence ( Figure 6 ) . Many previously known Bcr1 target genes are induced upon hyphal development [14] , but ALS1 is expressed in yeast form cells as well [28] . Although many Bcr1-dependent genes are hyphal genes , our findings here indicate that Bcr1 function in yeast form cells is biologically significant . One striking feature to emerge from nanoString profiling is that Bcr1 governs expression of many more genes than any other transcription factor assayed except for chromatin remodeling factor Snf5 ( Table 1 ) . The set of genes assayed for expression was designed to include known Bcr1-dependent genes , so this result is not a fair measure of bcr1Δ/Δ mutant pleiotropy . However , our analysis of target gene clusters suggests that Bcr1 may be a constituent of the RAM network . Bcr1 has impact on hyphal morphogenesis [12] , [14] , like other RAM network components . In addition , we have recently found that Bcr1 is required for cell wall integrity ( S . Fanning and A . P . Mitchell , unpublished results ) , a further parallel between Bcr1 and the RAM network . The mechanistic basis for interaction between Bcr1 and the RAM network is clearly an interesting area for further inquiry . Our screen also revealed that Snf5 , which functions in chromatin remodeling , is required for cell-substrate adherence . This finding in and of itself is not surprising , given that Snf5 is expected to govern expression of a multitude of different genes . What is striking is that such a broad spectrum of snf5Δ/Δ mutant phenotypes was reversed through increased expression of only one Snf5-dependent gene , ACE2 ( Figure 6 ) . The relationship between Snf5 and Ace2 is clearly more intimate than previously appreciated , and an area that seems promising for more detailed mechanistic analysis . Our analysis of the Snf5-Ace2 relationship suggests that a second transcription factor may be partially redundant with Ace2 . Our logic is as follows . The snf5Δ/Δ mutant is hypersensitive to cell wall inhibitors , and overexpression of ACE2 in the snf5Δ/Δ mutant reverses this hypersensitivity . This observation suggests that Ace2 promotes expression of genes that fortify the cell wall . Given that the ace2Δ/Δ mutant is not hypersensitive to cell wall inhibitors , then some other transcription factor may activate those genes in the absence of Ace2 . Functional redundancy of Ace2 is consistent with a recent synthetic interaction study of the RAM network role in hyphal formation [29] . It is possible that Bcr1 is the Ace2-redundant transcription factor , because they share several target genes . In addition , overexpression of BCR1 in the snf5Δ/Δ background relieves the mutant's caspofungin hypersensitivity ( unpublished results ) . A second candidate is Cas5 , a known regulator of cell wall integrity [30] . Our profiling data reveal that Cas5 , like Bcr1 , controls many RAM pathway genes . A third candidate is transcription factor Sko1 , which is down-regulated in the snf5Δ/Δ mutant but not in the ace2Δ/Δ mutant ( Table S2 ) . Sko1 functions in C . albicans cell wall integrity [31] . Our profiling data provide only a small slice of what could be found through genome-wide analysis . However , the fact that our probe set focuses on known genes and pathways means that the results can be used efficiently to generate plausible hypotheses , as illustrated above . Our results define a connection between 11 transcription factors that govern adherence , the zinc-response regulator Zap1 , and 48 target genes that we refer to as CSTAR genes ( Figure 2A ) . Among the CSTAR genes , 37 encode predicted surface or secreted proteins . Many of the predicted CSTAR products resemble adhesins , and three of them , Hwp2 , Pbr1 , and Pga10 , have been shown to promote biofilm formation [32] , [33] . A simple hypothesis is that one or several CSTAR gene products promote cell-substrate adherence ( Figure 6 ) . Several preliminary results support a relationship between CSTAR gene products and adherence . One set of observations comes from nanoString profiling of ZAP1-overexpressing strains ( see Table S3 ) . In the arg81Δ/Δ , try2−/− , uga33−/− , and zcf28−/− backgrounds , ZAP1 overexpression causes almost all CSTAR genes to reach or exceed their wild-type expression levels . In these strains , ZAP1 overexpression rescues the adherence defect . Thus an increase in overall CSTAR gene expression levels correlates with increased adherence in these strains . In addition , we have found that most CSTAR genes are down-regulated in farnesol-treated biofilms ( S . Ganguly , W . Xu , and A . P . Mitchell , unpublished results ) , a condition that promotes biofilm detachment [34] . Although these observations are preliminary , they are consistent with the model that one or several CSTAR gene products have a positive role in adherence . Functional analysis of some CSTAR gene products suggests that their functions may be redundant [32] , [33] . Specifically , deletions of CSTAR genes HWP2 , PBR1 , and PGA10 cause only partial defects in adherence or biofilm formation [32] , [33] . These findings imply that other gene products can compensate for absence of these three CSTAR genes to promote adherence [32] , [33] . The analysis of Hwp2 indicates that it may have overlapping functions with Hwp1 and Rbt1 in promoting both mating and biofilm formation [32] , [33] . We note that Hwp1 and Rbt1 are both hyphal genes and lie in our HYVIR cluster . Almost all adherence-defective transcription factor mutants with reduced CSTAR gene expression also have reduced HYVIR gene expression ( see Figure 2B ) . An interesting possibility is that several CSTAR and HYVIR gene products make similar functional contributions to adherence . Although we have considered the CSTAR genes as a single group , there are of course features that distinguish group members . For example , some CSTAR gene products are secreted ( Sap1 , Sap2 , Sap3 ) ; some belong to protein families ( Hyr3 , Iff3 , Iff4 [35] ) ; some are transporters ( Hgt12 , Qdr1 ) . In addition , some CSTAR genes are targets of only a subset of regulators: PGA26 responds weakly to Zap1 and Zcf28; IFF4 responds weakly to Suc1; PGA46 and CSA2 respond weakly to Met4 and Try6 . These distinctions in regulation may reflect differences in transcription factor interactions or specificity , or perhaps overlapping regulatory networks that have compensatory effects . For example , Hap43 governs expression of 7 CSTAR genes [36]–[38] , so Hap43 activity may influence the phenotypes of some CSTAR regulatory mutants . Similarly , the detailed spectrum of CSTAR expression alterations in any one adherence-defective mutant may affect its phenotype . We do not view the global analysis presented here as a substitute for more detailed analysis . Rather , this global portrait provides a basis for focusing detailed analysis , and a context in which to interpret it . Prior studies have shown that C . albicans Zap1 governs late events in biofilm formation , including production of extracellular matrix and quorum sensing molecules [20] , [26] . Zap1 is required for efficient hypha formation under several conditions [12] , [39] , which is required for biofilm formation [1] , [13] . Although the zap1Δ/Δ mutant has no adherence defect under our assay conditions , Zap1 is tied to adherence because its modest overexpression , in the range of 2- to 4-fold , restores adherence of 10 transcription factor mutants to a level comparable to the wild-type strain . We believe that this role is mediated through Zap1 control of CSTAR gene expression ( Figure 6 ) . The lack of an adherence defect for the zap1Δ/Δ mutant may reflect its ability to express some critical CSTAR genes , perhaps PGA26 for example , or its ability to express potentially redundant HYVIR genes , as discussed above . Both CSTAR genes and previously described Zap1 target ( ZAPT ) genes respond to mutations in many of the newly described adherence regulators . Surprisingly , these two sets of Zap1-dependent genes are not regulated in parallel . For example , the ace2Δ/Δ , bcr1Δ/Δ , and zcf34−/− strains have altered direct ZAPT gene expression but do not display altered CSTAR gene expression . Conversely , the fgr27−/− , try3−/− , try4−/− , try5−/− , and uga33−/− strains have reduced expression of many CSTAR genes , but have either no change or an increase in ZAPT gene expression . We cannot identify prospective Zap1 binding sites [20] in the 5′ regions of CSTAR genes , so they are probably regulated indirectly by Zap1 . For example , Try4 or Try5 may be the direct activators of CSTAR genes; Try4/5 expression or activity may be stimulated by Zap1 . Zap1 target genes have been defined previously through microarray and ChIP-chip analyses [20] . However , CSTAR genes were not identified in that study . The previous analysis employed mature biofilm RNA , whereas here we have used planktonic RNA . However , we have verified that CSTAR genes are Zap1-dependent in mature biofilms as well ( unpublished results ) . We believe that our detection of CSTAR gene expression differences reflects the fact that nanoString technology is much more sensitive than microarrays [11] , and the CSTAR genes are expressed at low levels ( roughly 1% of the level of HWP1; see Table S2 ) . The identification of this novel class of target genes illustrates the well-known value of applying new technology to a scientific question . Although we have identified numerous new adherence regulators , fairly few are required for biofilm formation in vitro . However , our preliminary results suggest that the assay is relevant to biofilm formation in vivo . Mutations in ZFU2 , CRZ2 , and ZCF28 cause no biofilm defect in vitro , but block biofilm formation in the in vivo catheter model ( unpublished results ) . It has not been feasible as of yet to test all 30 adherence defective mutants in vivo , but these results point to the validity of this approach to define genes relevant to infection .
All procedures were approved by the Institutional Animal Care and Use Committee ( IACUC ) at the University of Wisconsin according to the guidelines of the Animal Welfare Act , The Institute of Laboratory Animal Resources Guide for the Care and Use of Laboratory Animals , and Public Health Service Policy . Strains were grown in yeast extract-peptone-dextrose ( YPD ) rich medium , Spider medium ( 1% nutrient broth ( BD Difco ) , 1% D-mannitol ( sigma ) , 0 . 2% K2HPO4 ( Sigma ) ) , or defined synthetic dextrose medium , prepared as previously described [26] , [31] , [40] . Unique strains used in this study are listed in Table S4 . Insertion mutants were created as previously described [41] . The 197 UAU his- strains used in the initial adherence screen , as well as the transcription factor deletion mutants [12] , are not listed here and are available at http://www . fgsc . net/candida/FGSCcandidaresources . htm . Deletion strains created in this study were made in the BWP17 background using PCR product-directed gene deletion as previously described [42] . Complementation of mutant strains was done as previously described [21] . Briefly , to complement a specific mutation , a fragment of DNA from ∼1000 bp upstream to ∼300 bp downstream of an open reading frame was amplified from BWP17 genomic DNA . Primers contained a 40 bp sequence added to the 5′ end to allow in vivo recombination into plasmid pSG1 . The plasmid pSG1 was derived by replacing the URA3-f1-lacZ sequence from the vector pRS416 with the C . albicans HIS1 including a NruI restriction site [43] . The amplified PCR fragment and NotI linearized pSG1 was co-transformed into S . cerevisiae strain AMP271 with the resulting plasmid amplified in E . coli . The complementation plasmid was then digested with NruI and transformed into the respective mutant strain to target insertion to the HIS1 locus . All complementation was confirmed by QRT-PCR as previously described [21] . Primers used to create the deletions and the complemented strains are listed in Table S5 . Creation of EHY strains were accomplished by standard C . albicans transformation protocols [44] . The specific CJN , FJS , DSY and SFY strains were transformed with NruI digested plasmid pDDB78 [45] , and selected on synthetic dextrose medium lacking histidine . Isolates were streaked for singles and 3 independent HIS+ UAU insertion isolates were confirmed by PCR . Overexpression of ZAP1 in the 30 adherence defective mutants was accomplished by replacing the endogenous ZAP1 promoter ( at one allele ) with the promoter of TDH3 as described previously [20] . For ZAP1 overexpression , primers pTDH3 ZAP1 FOR , and pTDH3 ZAP1 REV , were used to amplify the THD3 promoter , with the resulting PCR product being used for recombination into ZAP1 promoter . For complementation of mutant strains , PCR primers were designed to amplify genomic DNA of strain SC5314 from 1 kb upstream to 0 . 5 kb downstream of the open reading frame of a specific gene . Shorter distances were used when there were additional genes located within this region . The resulting PCR product was cotransformed into S . cerevisiae with EcoRI and NotI digested plasmid pDDB78 . Plasmid DNA was isolated , transformed into E . coli , and isolated plasmid DNA was digested with NruI and transformed into the respective C . albicans mutant strains . Presence of the relevant insertion mutation was verified by genomic PCR using internal and flanking primers . New gene names were assigned as follows . The S . cerevisiae ortholog of orf19 . 5871 is ScSNF5 , so we use the name SNF5 for orf19 . 5871 . Other previously unnamed genes are designated TRY genes ( Transcriptional Regulators of Yeast cell adherence ) ; we refer to orf19 . 4062 as TRY2 , orf19 . 1971 as TRY3 , orf19 . 5975 as TRY4 , orf19 . 3434 as TRY5 , and orf19 . 6824 as TRY6 . We had initially referred to orf19 . 6781 as TRY1 , but the name ZFU2 was posted at the Candida Genome Database during the course of our studies . Strains were tested for drug sensitivity as described previously [30] . Briefly , overnight cultures in YPD were diluted to an OD600 of 3 . 0 and serially diluted five-fold and spotted onto YPD , YPD plus 62 . 5 µg/ml of caspofungin , and YPD plus 200 µg/ml Congo red plates . Plates were incubated at 30°C for 24–48 hours . Yeast cell morphology was assayed as previously described [40] . Briefly , overnight cultures grown at 30°C in liquid YPD were diluted to an OD600 of 0 . 2 with fresh YPD medium and were grown at 30°C to an OD600 of ∼0 . 8 . Cells were visualized using a Zeiss Axio Observer Z . 1 microscope with a 20× NA 1 . 4 objective . Digital photographs were acquired on a Coolsnap HQ2 ( Photometrics ) camera using Axiovision ( Zeiss ) software . For hyphal growth assay , overnight cultures grown at 30°C in liquid YPD were diluted to an OD600 of 0 . 08 in Spider medium . Cultures were agitated at 220-rpm at 37°C for 180 minutes . The samples were then washed with phosphate-buffered saline ( PBS ) , and incubated for ten minutes in PBS+0 . 125 mg/ml calcofluor white ( Sigma ) [46] . Cells were visualized as described above but with a 63× objective . ImageJ was used to process the images . Biofilm formation assays were performed as previously described [14] . Briefly , overnight cultures grown at 30°C in liquid YPD were diluted to OD600 of 0 . 5 in 2 ml of Spider medium , and incubated with silicone squares coated with fetal bovine serum . After 90 min incubation at 37°C with 70-rpm agitation , the silicone squares were washed with 2 ml PBS to remove any unadhered cells , and 2 ml of fresh Spider medium were added . After 48 hr incubation at 37°C with 70-rpm agitation the silicone squares were photographed and analyzed for biofilm growth . Biofilm dry masses were performed as previously described [40] . Briefly , biofilms were grown on silicone squares for 48 hours . Silicone squares were vortexed in ddH2O to completely detach the cells from the silicone surface . The cells were collected under suction on pre-weighed 0 . 45 µm nitrocellulose filters ( Millipore ) . After four days of drying the filters were weighed . For each strain the measurement was in triplicate . Biofilms were grown as described above , except that the incubation period was 24 hours . After 24 hours growth , biofilms were gently washed with 2 ml of 1× PBS . The biofilm was then incubated in 2 ml of 1× PBS with Calcofluor stain at a final concentration of 0 . 125 mg/ml for 10 minutes at 37°C with agitation at 70-rpm [46] . A 60 mm dish ( Fisher ) was punched with a 17×17 mm square hole and a No . 1 glass coverslip was fused to the bottom of the dish with UV-curing cement ( Norland NOA-61 ) to form a shallow well . Double-sided tape was attached to the interior glass bottom of the well , to act as a spacer preventing contact of the inverted biofilm to the bottom of the dish . Once the dish was completed , 300 µl PBS+calcofluor solution was added to the well . The biofilm was then carefully removed and inverted , and gently placed onto the double sided tape . After the biofilm was inverted and affixed to the coverslip , 7 ml of PBS/calcofluor solution was added to the dish . The biofilm was then imaged with a Zeiss LSM 510 Meta/DuoScan inverted spectral confocal microscope using a 40× water immersion 1 . 2 NA objective with the laser line at 405 nm . The Zen 2009 software was used to obtain the desired Z stack images . Image J ( http://rsbweb . nih . gov/ij/ ) was used to create the side view image and apical view . A rat central-venous-catheter infection model was used to assay in vivo biofilms , as previously described [47] . Briefly , after 24 hours of C . albicans infection catheters are removed from the rat and the distal 2 cm of catheter material is removed and assayed for biofilm growth via imaging using scanning electron microscopy ( SEM ) [48] . Adherence assays were conducted with Fluxion BioFlux 200 , a flow apparatus with micron scale fluidic channels that allows visualization of adherent cells with controlled flow rates . The flow chamber consists of a glass coverslip plasma fused to the fluidic channel constructed out of polydimethylsiloxane . C . albicans cells bind to the polydimethylsiloxane but not to the glass . Strains of interest were grown overnight in YPD at 30°C , and agitated at 220-rpm . The strains were diluted to an OD600 of 0 . 2 in YPD medium . 500 µl of sample was added to each lane and each sample was run in duplicate . For each plate a reference strain was run , which later was used for fold comparison to the mutant . For the his- strains the reference strain was DAY286 , for EHY HIS+ strains the reference strain was DAY185 , and for the Homann collection the reference strain was SN250 [12] . After loading , a flow rate of 3 dyn/cm3 was applied for 30 minutes at 30°C . After 30 minutes of flow each lane had two images taken at different sites along the channel . Images were always taken at the same location in each channel for each sample . Strains with filamentous or clumping cells were not assayable . For each image the number of yeast cells adhering to the channel was tabulated . Since two pictures were taken per lane the sum of each lane was used as a single determinate and each strain thus had two trials . The average was taken for each strain and the fold change calculated ( number of yeast cells adhered in the mutant strain/number of yeast cells adhered in the reference strain ) . Error bars were calculated by standard deviation , p-values were calculated by t-test . For strains that had significant changes in adherence , a second isolate of the strain was assayed to confirm the initial results . 10 µg of isolated RNA was DNase treated ( Ambion ) , and AffinityScript multiple temperature cDNA synthesis kit ( Stratagene ) was used for first-strand cDNA synthesis . A control reaction lacking the reverse transcriptase was performed to ensure absence of DNA contamination . Quantification was performed for gene amplification for the gene of interest and the reference , TDH3 . All data was normalized to TDH3 . Primers used for PCR amplification are listed in Table S5 . QRTPCR reactions were prepared and performed on a Biorad iQ5 as previously described [21] . One key issue with nanoString determination is to choose informative genes for expression measurements . We have chosen 300 genes for assays , based on four considerations . First , we included probes for ∼150 known or predicted cell wall genes , including 113 genes with potential GPI lipid modification sites identified by an earlier study [19] . Second , we included probes for ∼50 genes known to play a role in host-pathogen interactions , such as the ALS and SAP gene families . Third , based on previous genome wide expression studies [30] , [41] , we included probes for ∼100 genes that are highly regulated during hypha development or biofilm formation , during cell wall stress , and in osmotic or oxidative stress conditions . Fourth , we included control genes for high , moderate and low expression classes . We chose two genes of each class that vary little in numerous microarray studies from our lab as internal controls . They are ACT1 , TDH3 ( high ) , ARP3 , orf19 . 5917 . 3 ( moderate ) , orf19 . 7235 , PTC1 ( low ) . The list of genes and their orf19 numbers are shown in Table S2 . Cells from overnight YPD cultures were inoculated into 50 ml Spider medium at OD 0 . 2 , and were grown for 8 hours at 37°C with 220-rpm agitation before harvesting . Cells were collected by filtering through 0 . 45 um nitrocellulose filters ( Millipore ) . Half of each culture ( 25 ml ) was collected on one filter paper and let dry at room temp for dry weight measurements , the other half was collected on a separate filter and immediately frozen at −80°C for RNA extraction . Total RNA was extracted using the Qiagen RNeasy Plant kit ( Cat #74904 ) . 80 ng of total RNA was mixed with the nanoString probe set and incubated at 65°C overnight ( 12–18 hours ) . The reaction mix was then loaded on the nanoString nCounter Prep Station for binding and washing , using the default program . The resultant cartridge was then transferred to the nanoString nCounter digital analyzer for scanning and data collection . A total of 600 fields were captured per sample . Three independent samples were prepared and processed for each mutant ( six samples for the wildtype control strain DAY185 ) . We performed nanoString analysis on 30 transcription factor mutants with reduced yeast form adherence , 2 transcription factor mutant strains ( tec1 , zap1 ) that have wild-type levels of adherence , and 3 protein kinase mutant strains that are known to have severe defects in cell wall integrity and biofilm formation ( ire1 , gin4 and cbk1 ) [21] . All 35 mutants are listed in Table S2 . The raw data , in a form of digital counts for each of the 300 genes in every sample , were first adjusted for binding efficiency and background subtraction using the manufacturer included positive and negative controls , following nCounter data analysis guidelines . Second , mutant strain data sets were normalized to the control wildtype strain DAY185 using three groups of control genes: ACT1 , TDH3 ( high ) , ARP3 , orf19 . 5917 . 3 ( moderate ) , orf19 . 7235 , PTC1 ( low ) . Normalization factors were calculated for each group , and the average of the three was used to normalize the whole data set . We noticed that the normalization factors calculated for the three groups ( high , moderate and low ) were very consistent , usually within 10% difference . The normalized data sets for 35 mutants , each containing expression data for 293 genes , were shown in Table S2 and were further analyzed ( we took out the 6 control genes , and OSM1 , which is the same as ALS4 . OSM1 was annotated as a separate gene adjacent to ALS4 , but was later corrected as a part of the ALS4 gene . Our readings on OSM1 and ALS4 were almost identical in all mutants and wildtype ) . We used MultiExperimentViewer ( MeV v4 . 6 . 2 ) to cluster the data sets . The normalized data sets were used to determine if the expression level of a gene in a mutant was significantly different from that in the wild-type control by two-tailed Student t-test . For ones that are significantly different ( P<0 . 05 ) , the average of three determinations for a gene in a mutant was divided by the average of six determinations for the respective gene in the wild-type control to calculate the fold change . For ones that are not significantly different ( P>0 . 05 ) , we set the fold change as 1 , so that they would not affect clustering analysis . The data ( fold changes comparing to wildtype ) were log2 transformed , and hierarchical clustered by averaging linkage clustering based on Manhattan Distance , and optimized for gene leaf order . Color scale limits were set at “−2 . 0 , 0 . 0 , 2 . 0” , meaning that the brightest yellow represents 4 fold upregulation comparing to wild-type , the brightest blue represents 4 fold downregulation , and black represents no change ( or the change is not considered significant by t-test ) . We also performed the same clustering analysis using the original normalized data sets ( i . e . without using the t-test to eliminate ones with p-value >0 . 05 ) . The resultant clusters were very similar to what we obtained using the p-value adjusted data sets . The clustering diagram shown in figure 2A is from the original data sets . | Most microorganisms adhere to surfaces in nature , leading to formation of complex communities called biofilms . Pathogen adherence to medical devices is the basis for device-associated infection . We have focused on the control of adherence in the fungal pathogen Candida albicans . We find that this process is under control of thirty transcriptional regulators . Our analysis of gene expression in regulatory mutants with altered adherence provides new understanding of the relationships among known regulators . In addition , we find evidence for a large regulatory network that connects one quarter of all cell surface protein genes . | [
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] | 2012 | Portrait of Candida albicans Adherence Regulators |
ASYMMETRIC LEAVES 1 ( AS1 ) is a MYB-type transcription repressor that controls leaf development by regulating KNOX gene expression , but the underlying molecular mechanism is still unclear . In this study , we demonstrated that AS1 can interact with the histone deacetylase HDA6 in vitro and in vivo . The KNOX genes were up-regulated and hyperacetylated in the hda6 mutant , axe1-5 , indicating that HDA6 may regulate KNOX expression through histone deacetylation . Compared with the single mutants , the as1-1/axe1-5 and as2-1/axe1-5 double mutants displayed more severe serrated leaf and short petiole phenotypes . In addition , the frequencies of leaf lobes and leaflet-like structures were also increased in as1-1/axe1-5 and as2-1/axe1-5 double mutants , suggesting that HDA6 acts together with AS1 and AS2 in regulating leaf development . Chromatin immunoprecipitation assays revealed that HDA6 and AS1 bound directly to KNAT1 , KNAT2 , and KNATM chromatin . Taken together , these data indicate that HDA6 is a part of the AS1 repressor complex to regulate the KNOX expression in leaf development .
The initiation of leaf primordia is established by recruitment of cells from the flanks of the shoot apical meristem ( SAM ) . Meristem activity in the shoot apex is specified in part by the class I KNOTTED-LIKE HOMOBOX ( KNOX ) genes [1]–[3] . Lateral organs , such as leaves , are initiated on the flank of SAM , and down-regulation of KNOX genes is essential to facilitate this process [1] , [4] . Moreover , the silencing of KNOX genes is important in developing organs since the ectopic KNOX expression during organogenesis resulted in patterning defects and over-proliferation of cells [5]–[7] . Thus , the balance between stem cell differentiation and proliferation that is decisive for plant development is attained , in part through the proper regulation of the KNOX expression . In Arabidopsis , the KNOX family can be further divided into three classes . Class I KNOX genes are similar to KNOTTED1 ( KN1 ) in maize , including BREVIPEDICELLUS ( BP ) /KNAT1 , KNAT2 , KNTA6 and SHOOTMERISTEMLESS ( STM ) . These genes are expressed in the SAM and down-regulated in leaf primordia [8] . Class II KNOX genes comprise KNAT3 , KNAT4 , KNAT5 and KNAT7 , which are broadly expressed . Class III only contains KNATM , which is a novel KNOX gene lacking the homeodomain . It was demonstrated that KNATM functions together with KNAT1 and BELL proteins by forming heterodimer [9] . Moreover , ectopic expression of KNATM resulted in the curled down and serrated rosette leaves in wild type plants [9] . KNOX repression is mediated by the orthologous MYB domain proteins ROUGH SHEATH2 ( RS2 ) in maize ( Zea mays ) and ASYMMETRIC LEAVES1 ( AS1 ) in Arabidopsis thaliana [10]–[13] . In addition , AS1 interacts with the LATERAL ORGAN BOUNDARIES ( LOB ) domain protein AS2 and directly represses the expression of BP/KNAT1 and KNAT2 [14]–[16] . Previous studies revealed that AS1 and AS2 may recruit a chromatin-remodeling protein Histone Regulatory Homolog 1 ( HIRA ) to regulate the expression of target genes [17] . Moreover , HIRA has also been shown to interact with a histone deacetylase ( HDAC ) in animal cells [18] . In this study , we investigated the interaction of AS1 with the histone deacetylase HDA6 and their involvement in leaf development . We demonstrated that HDA6 can interact with AS1 in vivo and in vitro . The hda6 mutant , axe1-5 , displayed curling and serrated leaves as well as shorter petioles , suggesting that HDA6 is involved in leaf development . Additionally , HDA6 and AS1 associate directly with the promoters of KNAT1 , KNAT2 and KNATM . Taken together , our data suggest that HDA6 is a part of the AS1 repression complex to regulate the expression of KNOX genes .
AS1 is a MYB-type transcription repressor that controls leaf patterning by repressing class-1 KNOX gene expression [16] . However , the molecular mechanism how AS1 represses KNOX gene expression is still unclear . In yeast and mammalian cells , many transcription repressors were found to recruit HDACs to regulate their target genes [19] . To further understand the molecular mechanism of AS1-dependent KNOX repression , we analyzed the interaction of AS1 with HDA6 , a RPD3-type HDAC in Arabidopsis [20] , [21] by using BiFC assays . The coding sequences of HDA6 and AS1 were fused to the N-terminal 174-amino acid portion of yellow fluorescent protein ( YFP ) in the pEarley-Gate201 vector ( pEarleyGate201-YN ) or the C-terminal 66-amino acid portion of YFP in the pEarleyGate202 vector ( pEarleyGate202-YC ) [22] . The Agrobacterium cells containing these constructs were co-transfected into Nicotiana benthamiana leaves . The yellow fluorescence was observed at the nuclear when HDA6-YN and AS1-YC were transient expressed in N . benthamiana leaves , indicating that HDA6 interacted with AS1 in vivo ( Figure 1A ) . In contrast , the yellow fluorescence was not observed in the negative controls ( Figure S1 ) . The interaction between HDA6 and AS1 was further confirmed by in vitro pull down assays . When purified MBP-AS1 recombinant protein was incubated with glutathione S-transferase ( GST ) -HDA6 protein , HDA6-GST was pulled down by MBP-AS1 ( Figure 1B ) , indicating that HDA6 was directly associated with AS1 . Co-immunoprecipitation ( CoIP ) assays were also used to analyze the interaction between HDA6 and AS1 . A stable transgenic plant expressing 35S:GFP-HDA6 in the hda6 mutant ( axe1-5 ) was generated [23] . Overexpressing 35S:GFP-HDA6 in axe1-5 complemented the mutant phenotype , suggesting that the GFP-HDA6 fusion protein is functional . Crude extracts ( input ) of axe1-5 , as1-1 and axe1-5/35S:GFP-HDA6 were immunoprecipitated by the AS1 antibody , then analyzed by western blotting . As shown in Figure 1C , GFP-HDA6 was clearly co-immunoprecipitated by endogenous AS1 . Furthermore , AS1 protein was also co-immunoprecipitated by GFP-HDA6 when immunoprecipitated by the GFP antibody ( Figure 1C ) . Taken together , our data strongly indicate that HDA6 interacts with AS1 in vitro and in vivo . Previous studies indicated that AS1 and AS2 can associate together both in yeast cells by yeast two-hybrid assays and in vitro by ELISA experiments using purified His-AS1 and GST-AS2 recombinant proteins [14] . By using BiFC assays , we also found that AS1 and AS2 can interact with each other in N . benthamiana leaves ( Figure S2 ) . Furthermore , both AS1 and AS2 can also interact with itself ( Figure 2A ) . These observations indicated that AS1 and AS2 can form both homo- and hetero-dimers . The yellow fluorescence was observed at the nucleus when AS1-YN and AS1-YC , AS2-YN and AS2-YC , or AS1-YN and AS2-YC were transient expressed in N . benthamiana leaves ( Figure 2A and Figure S2 ) . Moreover , the in vivo interaction between HDA6 and AS2 was also found by using BiFC ( Figure 2B ) . Collectively , these results together with the finding that HDA6 interacts with AS1 suggested that HDA6 , AS1 and AS2 function together in the same protein complex . We further tested the protein-protein interactions among HDA6 , AS1 and AS2 in the protoplasts isolated from the mutants . By using BiFC assays , we found that HDA6 interacted with AS1 in the nucleus of as2-1 mutants ( Figure S3 ) . Likewise , the interaction of HDA6 and AS2 was also found in the nucleus of as1-1 mutants . In addition , we also showed that AS1 interacted with AS2 in the nucleus of axe1-5 mutants . Our data indicate that loss of one component of HDA6 , AS1 and AS2 does not affect the interaction of two others in Arabidopsis . Previously , we reported that the Arabidopsis HDA6 is required for flowering time control and the hda6 mutant , axe1-5 , displayed a delayed flowering phenotype [23] . In addition , axe1-5 mutants also displayed the curling leaves under both long-day ( LD ) and short-day ( SD ) conditions ( Figure 3A ) . Similar curling and serrated leaves were also found in another hda6 mutant , sil1 [25] ( Figure 3A ) , and the HDA6-RNAi plants ( Figure S4 ) . hda6 mutants displayed the down curling phenotype on both the distal and lateral axis ( Figure 3A ) . These results demonstrated that HDA6 functions not only in controlling adaxial-abaxial axis , but also in proximal-distal axis and in medial-lateral axis . The as1 and as2 mutants of Arabidopsis thaliana exhibit pleiotropic phenotypes in leaf development , including the curling and serrated leaves [26] . To examine the genetic interaction between HDA6 and AS1 or AS2 , we generated as1-1/axe1-5 and as2-1/axe1-5 double mutants and compared the leaf phenotype of single and double mutants . Under LD conditions , as1-1/axe1-5 and as2-1/axe1-5 double mutant plants showed more severe leaf phenotypes compared with as1-1 and as2-1 single mutant plants ( Figure 3B and 3C ) . We also measured the lengths of petioles and lamina in wild type and mutant plants . Compared with wild type , the lengths of the petioles were decreased in axe1-5 mutants ( Figure 3D ) . as1-1/axe1-5 and as2-1/axe1-5 double mutants displayed shorter petioles compared with as1-1 and as2-1 single mutant plants ( Figure 3B , 3C and Figure 3D ) . However , the lamina lengths of as1-1/axe1-5 and as2-1/axe1-5 did not show significant changes compared with the single mutants ( Figure 3E ) . We further measured the frequencies of leaf lobe formation in axe1-5 , sil1 , as1-1/axe1-5 and as2-1/axe1-5 mutants . The frequencies of leaf lobes were significantly increased in as1-1/axe1-5 and as2-1/axe1-5 double mutants ( Table 1 ) . as2 mutants produced leaflet-like structures on the petioles [26] . In as1-1/axe1-5 and as2-1/axe1-5 double mutants , the frequencies of leaflet-like structures were increased ( Table 2 ) , and some of the leaf lobes were similar to leaflet-like structures ( Figure 3C ) . These results suggested that HDA6 acts with AS1 and AS2 in regulating leaf development . We further analyzed the gene expression by quantitative reverse transcription ( qRT ) -PCR in mutant plants . Compared with Col wild type , no significant changes were found in the expression of AS1 and AS2 in the axe1-5 ( Figure S5 ) . As shown in Figure 4 , the expression of KNAT1 , KNAT2 and KNATM was increased in axe1-5 compared to Col wild type . Consistent with the previous study [13] , the transcript levels of KNAT1 and KNAT2 were elevated in as1-1 and as2-1 mutant plants . In addition , the expression of KNATM was also up-regulated in as1-1 and as2-1 mutant plants . Moreover , the expression of KNAT1 , KNAT2 and KNATM was highly increased in as1-1/axe1-5 and as2-1/axe1-5 double mutants compared with their corresponding single mutants . These data indicate that HDA6 may function synergistically with AS1 and AS2 in regulating the expression of KNOX genes . We also analyzed the expression of PHB , PHV , CUC1 and CUC2 , which were involved in leaf development through the miRNA regulated pathway [27]–[30] . However , no significant different was found in the expression of PHB , PHV , CUC1 and CUC2 ( Figure S5 ) . To determine whether the high expression of KNOX genes in the mutants is related to histone hyperacetylation in chromatin , ChIP assays were used to analyze the histone H3 acetylation levels of KNAT1 , KNAT2 and KNATM . The relative enrichment of histone H3 acetylation was determined by real-time PCR using primers specific for the proximal promoter ( within 500 bp upstream of the transcription starting sites ) and transcription start regions of individual genes . As shown in Figure 5 , levels of histone H3 acetylation were slight elevated in the proximal promoter and transcription start regions of KNAT1 , KNAT2 and KNATM in axe1-5 , suggesting that HDA6 may regulate these genes expression by chromatin deacetylation . We further analyzed histone acetylation levels of KNAT1 , KNAT2 and KNATM in as1-1 , as2-1 and the double mutants . As shown in Figure 5B , hyperacetylation of histone H3 was found in the promoter and first exon of KNAT1 , KNAT2 and KNATM in as1-1/axe1-5 and as2-1/axe1-5 double mutants . In contrast , hyperacetylation of histone H3 was not found in as1-1 and as2-1 single mutants . These results suggested that hyperacetylation of histone H3 in KNAT1 , KNAT2 and KNATM found in as1-1/axe1-5 and as2-1/axe1-5 double mutants was caused by the hda6 mutation . Histone H3K4Me3 is another chromatin mark associated with active genes . We also investigated the histone H3K4Me3 level in axe1-5 mutants . However , no significant changes in the H3K4Me3 of KNAT1 , KNAT2 and KNATM were found ( Figure S6A ) . H3K9Me2 was reported as a chromatin marker associated with gene repression . No significant changes in the level of histone H3K9Me2 was found in axe1-5 mutants ( Figure S6B ) . The direct association between AS1 and HDA6 suggested that AS1 may recruit HDA6 to repress the downstream target genes . Previous studies demonstrated that the AS1 repressor complex binds directly to the regulatory motif I ( CWGTTD ) and motif II ( KMKTTGAHW ) on the promoters of the KNAT1 and KNAT2 [16] . We also found the conserved motif I and motif II in two promoter regions ( KNAMT-X and KNAMT-Y ) of KNATM ( Figure 6A and Figure S7 ) . To investigate whether AS1 binds directly to KNAT1 , KNAT2 and KNATM , ChIP analyses using the AS1 antibody were performed in Col wild type and as1-1 mutants . Consistent with the previous report [16] , AS1 can bind to the promoters of KNAT1 and KNAT2 ( Figure 6B ) . In addition , AS1 can also bind directly to KNATM ( Figure 6B ) . In comparison , AS1 cannot bind to the control genes , ACTIN2 and TUB2 . To analyze whether the binding of AS1 to KNAT1 , KNAT2 and KNATM requires the presence of AS2 , we also performed ChIP assays using the as2-1 mutants . We found the loss of binding of AS1 to the KNOX chromatin in the as2-1 mutant ( Figure 6C ) , suggesting that AS2 is required for the binding of AS1 to the KNOX genes . To examine whether HDA6 can binds directly to KNAT1 , KNAT2 and KNATM . , transgenic plants expressing HDA6-Myc were subjected to ChIP analysis using an anti-Myc antibody . As shown in Figure 6D , ChIP analyses revealed that HDA6 can bind to the promoters of KNAT1 , KNAT2 and KNATM . We also analyze whether HDA6 recruitment is dependent on AS1 . ChiP assays were performed using an anti-Myc antibody in transgenic plants expressing the HDA6-Myc in as1 mutants . As shown in Figure 6D , HDA6 cannot bind to KNAT1 , KNAT2 and KNATM in as1 mutants , suggesting that AS1 is required to recruit HDA6 . To analyze whether the HDA6 binding is dependent on its catalytic activity , we performed ChIP assays using an anti-FLAG antibody in transgenic plants ( HDA6 all 5 mut in axe1-5 ) expressing the FLAG-tagged HDA6 bearing the five amino acid mutation of the active site in axe1-5 mutants [31] . As show in Figure 6E , the active site mutant HDA6 can still bind to KNAT1 , KNAT2 and KNATM , suggesting that HDA6 recruitment is independent of its catalytic activity . Taken together , our findings suggested that HDA6 , AS1 and AS2 act together and directly repress the expression of KNOX genes in Arabidopsis .
The Arabidopsis genome sequence contains 9 KNOX genes , which can be further classified into 3 classes [32] . In leaves , AS1 and AS2 down-regulate class I KNOX genes , but not STM; conversely , STM represses AS1 expression in the SAM [12] , [33] . Downregulation of KNOX genes expression is a vital step in leaf initiation , and silencing of these genes needs to be maintained for normal organogenesis [13] , [15] . In this study , we demonstrated that hda6 mutants displayed the curling and serrated leaves and shorter petioles . Compared with the single mutants , as1-1/axe1-5 and as2-1/axe1-5 double mutants show more severer phenotypes on curling leaves , petiole lengths , and leaflet-like structures , supporting that HDA6 acts synergistically with AS1 and AS2 in the regulation of leaf development . KNAT1 and KNAT2 were previously found to be repressed by AS1 and AS2 [14]–[16] . Our results indicated that the transcript levels of KNAT1 , KNAT2 and KNATM were altered in axe1-5 , as1-1 and as2-1 mutants . Furthermore , the expression of KNAT1 , KNAT2 and KNATM was highly increased in as1-1/axe1-5 and as2-1/axe1-5 double mutants compared to their corresponding single mutants . In addition , levels of histone H3 acetylation was elevated in KNAT1 , KNAT2 and KNATM loci in axe1-5 , as1-1/axe1-5 and as2-1/axe1-5 mutants , suggesting that HDA6 is required for the repression of KNOX genes by chromatin deacetylation . ChIP analyses revealed that HDA6 and AS1 bound directly to the promoters of KNAT1 , KNAT2 and KNATM . These data indicate that HDA6 and AS1 function together in controlling KNOX gene expression through histone dacetylation . In addition , AS1 is required to recruit HDA6 in KNOX repression HDA6 cannot bind to KNAT1 , KNAT2 and KNATM in as1 mutants , suggesting that AS1 is required to recruit HDA6 in KNOX repression . Microarray gene expression analyses revealed that a large number of loci are differently expressed in hda6 mutants [23] , [34] , indicating that HDA6 may play multiple roles in different development processes . Recent studies suggested that the expression of KNOX genes is only one important factor for leaf development [28] , [30] . Further analysis is required to determine whether HDA6 is involved in other leaf development pathways . AS1 is a Myb domain transcription factor related to RS2 in maize and PHANTASTICA in Antirrhinum [12] . Mutations in AS1 result in abnormal leaves , with marginal outgrowths or lobes [12] , [13] , [33] , [35] AS2 encodes a LOB domain protein containing a leucine-zipper motif [36]–[38] . Mutations in the as2 gene cause a phenotype similar to as1 mutants [13] , [33] . Previous studies indicated that AS1 and AS2 can associate together both in vitro and in yeast cells [14] . By using the BiFC assay , we found that AS1 and AS2 can interact and form homo and hetero-dimer in plant cells . These data suggested that AS1 and AS2 function in the same protein complex . A recent study indicated that AS1 functions as a transcriptional repressor and binds directly to its KNOX targets when in a complex with AS2 [16] . It was found that the AS1–AS2 repressor complex binds directly to the regulatory motif I ( CWGTTD ) and motif II ( KMKTTGAHW ) in the promoters of the KNAT1 and KNAT2 [16] . Similar to KNAT1 and KNAT2 , we also found the conserved motif I and motif II in the promoter of KNATM . KNATM is a novel Arabidopsis Class III KNOX gene that has a MEINOX domain but lacks the homeodomain [9] . ChIP assayes revealed that AS1 can bind directly to the promoter regions of KNAT1 , KNAT2 and KNATM . These data suggested that in addition to KNAT1 and KNAT2 , the AS1–AS2 complex is also targeted to KNATM by binding to the conserved motifs I and II . To our knowledge , this is the first study demonstrating that KNATM is regulated by AS1 and AS2 . Recent studies suggested that the AS1–AS2 complex binds to the KNAT1 and KNAT2 promoters and recruit the chromatin-remodeling protein HIRA to maintain the chromatin in a stable repressive state [15] , [16] , [39] . In mammalian cells , HIRA was shown to interact with a histone deacetylase [18] . Moreover , it was observed that Arabidopsis seedlings treated with TSA , an inhibitor of HDACs , produced abaxialized filamentous leaves , indicating the involvement of HDACs in leaf morphogenesis [24] . In this study , we provided direct evidence indicating that HDA6 is involved in leaf morphogenesis by interacting with AS1 and AS2 to regulate the KNOX expression . Compared with the single mutants , as1-1/axe1-5 and as2-1/axe1-5 double mutants show more severe phenotypes on curling leaves , petiole lengths , and leaflet-like structures , supporting that HDA6 acts with AS1 and AS2 to regulate leaf development . Taken together , our results demonstrated that histone deacetylation is one of the epigenetic components involved in AS1–AS2 complex-mediated KNOX repression . HDA6 may therefore be part of the AS1–AS2 repression complex to repress the target gene expression . Our data indicate that loss of one component of HDA6 , AS1 and AS2 does not affect the interaction of two others in Arabidopsis . Previous studies indicated that the interaction between AS1 and AS2 is required for their binding to the promoters of KNOX genes , because neither AS1 nor AS2 alone was able to bind to the target DNA sequences in vitro [40] . We observed the loss of binding of AS1 to the KNOX chromatin in the as2-1 mutant , suggesting that AS2 is required for the AS1 binding . Furthermore , HDA6 cannot bind to KNOX chromatin in as1-1 mutants , indicating that AS1 is required to recruit HDA6 . Taken together , both AS1 and AS2 are required for the recruitment of HDA6 to chromatin in repression of KNOX genes . A recent work has also shown that the Polycomb Repressive Complexes ( PRCs ) repress KNOX transcription [40] . It was found that CLF-containing PRC2 regulates KNOX genes by trimethylation of histone H3K27 [41] . Thus , AS1 and AS2 may also recruit other chromatin factors such as PRCs to regulate class I KNOX genes . Taken together , our results suggested that HDA6 is one of the epigenetic components involved in the AS1–AS2 complex-mediated KNOX repression during leaf development in Arabidopsis .
Arabidopsis thaliana was grown in 23°C under LD ( 16 h light/8 h dark ) or SD ( 8 h light/16 h dark ) conditions . axe1-5 , sil1 , as1-1 and as2-1 are in the Col background , whereas the HDA6 RNAi lines CS24038 and CS24039 are in Ws background . Arabidopsis leaves ( 0 . 2 g ) were ground with liquid nitrogen in a mortar and pestle and mixed with 1 ml Trizol Reagent ( Invitrogen ) to isolate total RNA . After treated with DNase ( Promega ) , two microgram of total RNA was used for the first-strand cDNA synthesis . cDNA was synthesized in a volume of 20 µl that contained the Moloney Murine Leukemia Virus Reverse Transcriptase buffer ( Promega ) , 1 . 5 µM poly ( dT ) primer , 0 . 5 mM deoxyribonucleotide triphosphates , 25 units RNasin ribonuclease inhibitor , and 200 units Moloney Murine Leukemia Virus Reverse Transcriptase at 37°C for 1 h . cDNAs obtained from reverse transcription were used as a template to run real-time PCR . The following components were added to a reaction tube: 9 µL of iQ SYBR Green Supermix solution ( Bio-Rad ) , 1 µL of 5 µM specific primers , and 8 µL of the diluted cDNA template . Thermocycling conditions were 95°C for 3 minutes followed by 40 cycles of 95°C for 30 s , 60°C for 30 s , and 72°C for 20 s , with a melting curve detected at 95°C for 1 minute , 55°C for 1 minute , and detected the denature time from 55°C to 95°C . Each sample was quantified at least triplicate and normalized using Ubiquitin 10 as an internal control . The gene-specific primer pairs for quantitative RT-PCR are listed in Table S1 . ChIP assay was carried out as described [42] . Chromatin extracts were prepared from 10 day old seedlings treated with formaldehyde . The chromatin was sheared to an average length of 500 bp by sonication and immunoprecipitated with specific antibodies including anti-acetylated histone H3K9K14 ( Catalogue no . 06-599 , Millipore ) , anti-trimethylated histone H3K4 ( Catalogue no . 04-745 , Millipore ) , anti-c-Myc ( Catalogue no . M4439 , Sigma ) and anti-FLAG ( Catalogue no . F1804 , Sigma ) . The DNA cross-linked to immunoprecipitated proteins was analyzed by real-time PCR . Relative enrichments of various regions of KNAT1 , KNAT2 and KNATM in axe1-5 , as1-1 and as1-1/axe1-5 over Col were calculated after normalization to ACTIN2 . Each of the immunoprecipitations was replicated three times , and each sample was quantified at least in triplicate . The primers used for real-time PCR analysis in ChIP assays are listed in Table S2 . To generate the constructs for BiFC , full-length coding sequences of HDA6 , AS1 and AS2 were PCR-amplified using Pfu polymerase ( Finnzymes ) . The PCR products were subcloned into the pENTR/SD/D-TOPO or pCR8/GW/TOPO vector and then recombinated into the pEarleyGate201-YN and pEarleyGate202-YC vectors [22] . The resulting constructs were transformed into the Agrobacterium GV3101 and the Agrobacteria containing these constructs were cotransfected into five week old Nicotiana benthamiana leaves . For the protoplast transient expression , HDA6 , AS1 and AS2 fused with pEarleyGate201-YN or pEarleyGate201-YC were co-transfected into protoplasts by PEG transfection [43] . Transfected leaves and protoplasts were imaged using TCS SP5 ( Leica ) Confocal Spectral Microscope Imaging System . Pull-down assays were performed as previously described [44] with some modifications . 2 µg Myelin basic protein ( MBP ) and MBP-AS1 recombinant proteins were incubated with 30 µl of MBP resin in a total volume of 500 µl of MBP binding buffer ( 20 mM Tris-HCl , pH 7 . 5 , 200 mM NaCl , 1 mM EDTA ) for 2 h at 4°C , and the binding reaction was washed 3 times by the binding buffer , then 2 µg GST-HDA6 recombinant protein was added and incubated for additional 2 h at 4°C . After extensive washing ( at least 8 times ) , the pulled down proteins were eluted by boiling , separated by 10% SDS-PAGE , and detected by western blotting using an anti-GST antibody . Coimmunocipitation assays were performed as previous described [23] . The 20-day-old axe1-5/35S:GFP-HDA6 , axe1-5 and as1-1 plants were harvested and ground in liquid nitrogen . Total proteins were extracted in an extraction buffer ( 50 mM Tris-HCl , pH 7 . 4 , 150 mM NaCl , 2 mM MgCl2 , 1 mM DTT , 20% glycerol , and 1% CA-630 ) containing protease inhibitor cocktail ( Roche ) . Cell debris was pelleted by centrifugation at 14 , 000 g for 30 min . The supernatant was incubated with anti-AS1 or anti-GFP specific antibody overnight at 4°C by gently rotation , then 50 µl of protein G agarose beads ( Millipore ) was added . After 3 h of incubation at 4°C by gently rotation , the beads were centrifuged and washed five times with a washing buffer ( 50 mM Tris-HCl , pH 7 . 4 , 150 mM NaCl , 2 mM MgCl2 , 1 mM DTT , 10% glycerol , and 1% CA-630 ) . Proteins were eluted with 40 µl of 2 . 5× sample buffer and analyzed by western blotting using anti-AS1 and anti-GFP ( Santa Cruz Biotechnologies ) antibodies . | AS1 is a MYB-type transcription repressor that controls leaf patterning by repressing class-1 KNOX gene expression . The molecular mechanism by which AS1 represses KNOX gene expression is still unclear . In this study , we found that AS1 interacted with the histone deacetylase HDA6 . Furthermore , HDA6 repressed KNOX gene expression by histone deacetylation . hda6 mutants displayed serrated leaf and short petiole phenotypes . Additionally , hda6/as1-1 double-mutant plants showed a more severe phenotype compared to the single mutants , indicating that HDA6 may act together with AS1 in controlling leaf development . Taken together , our data indicated that HDA6 is an important component of the AS1 repressor complex in regulating the KNOX gene expression . | [
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] | 2012 | Histone Deacetylase HDA6 Is Functionally Associated with AS1 in Repression of KNOX Genes in Arabidopsis |
The finger ridge count ( a measure of pattern size ) is one of the most heritable complex traits studied in humans and has been considered a model human polygenic trait in quantitative genetic analysis . Here , we report the results of the first genome-wide linkage scan for finger ridge count in a sample of 2 , 114 offspring from 922 nuclear families . Both univariate linkage to the absolute ridge count ( a sum of all the ridge counts on all ten fingers ) , and multivariate linkage analyses of the counts on individual fingers , were conducted . The multivariate analyses yielded significant linkage to 5q14 . 1 ( Logarithm of odds [LOD] = 3 . 34 , pointwise-empirical p-value = 0 . 00025 ) that was predominantly driven by linkage to the ring , index , and middle fingers . The strongest univariate linkage was to 1q42 . 2 ( LOD = 2 . 04 , point-wise p-value = 0 . 002 , genome-wide p-value = 0 . 29 ) . In summary , the combination of univariate and multivariate results was more informative than simple univariate analyses alone . Patterns of quantitative trait loci factor loadings consistent with developmental fields were observed , and the simple pleiotropic model underlying the absolute ridge count was not sufficient to characterize the interrelationships between the ridge counts of individual fingers .
Finger ridges and ridge patterns are highly heritable , durable , and age-independent human traits and have been studied as a model quantitative trait in humans for over 80 years [1] . They develop between approximately the 13th and 18th weeks of gestation , and in the absence of trauma remain essentially unchanged throughout life . The cutaneous mechanoreceptive afferent neurons that innervate the fingertips develop in alignment with the ridges [2] , lending support to the theory that fingerprints play a role in gripping [3] and tactile perception [4] . While relatively little is known about the developmental processes underlying fingerprint patterns , these results suggest that factors influencing the direction and complexity of ridge pattern formation also influence the receptive fields of the mechanoreceptors . The development of ridge patterns coincides with the regression of embryonic volar pads on fingers , and the type and size of patterns are largely determined by the size and timing of subsidence of these pads [5] . Any genetically or environmentally determined growth disturbances that affect the limbs in the critical period of ridge formation may also affect normal development of ridges and ridge patterns . Finger ridge count is also subject to a sex chromosome dosage effect , with the largest count encountered in females with X monosomy ( Turner's syndrome ) and the lowest in the X , Y polysomies [5] . Hence , dermatological traits can assist in determining the nature and timing of developmental disturbance . Traditionally , the ridge count is defined as the number of ridges that intersect or touch the line drawn from the easily recognized triradius ( where three ridges meet ) to the center of the pattern [6] . The most common pattern , a simple loop ( 60%–70% of all patterns [6] ) , characterized by a single triradius , is most advantageous for tactile perception [7] and precision grip [3] . Whorls have two triradii yielding two counts , while simple arches have no true triradii , resulting in a zero count . When the ridge count is used as a measure of a maximum pattern size on fingers , only the largest count from each finger is scored , and their sum is defined as the total ridge count . Alternatively , the sum of all possible counts on all ten fingers can be calculated yielding an absolute ridge count ( ARC ) [5] , a measure of the total pattern size . Both total ridge counts and ARC are highly heritable . Genetic effects have been found to account for 90%–95% of the variation on these measures . Estimates using either traditional correlation-based methods [5 , 6] or structural equation models fitted to twin and sibling [7] or family [8–10] data have found additive genetic effects to account for around 90% of the variation . That the remaining genetic variation arises from dominance and/or higher order genetic effects was initially suggested by skewness in the distribution of these measures [6] and supported by the modeling of twin and sibling data [7] . The ridge counts of individual fingers are interrelated ( correlations range from ∼0 . 4 to 0 . 8 ) and highly heritable , with the lowest heritability ( ∼0 . 50 ) observed for the thumb and little fingers [5 , 6 , 8] . These findings have led to the development of a variety of models to explain the genetics of finger ridge count , the simplest of which postulates pleiotropic gene effects that assume a single genetic factor determining the general magnitude of the counts and random influences accounting for between-finger variation [6] . More complex models , such as those developed by Martin et al . [7] , have found shared genetic effects common to all digits , in addition to patterns of covariation suggestive of developmental fields acting across the fetal hand , implying heterogeneous gene action between digits . The aim of this study was to identify loci influencing finger ridge counts by conducting a genome scan on 2 , 114 twin and singleton offspring from 922 twin families ( equivalent to 2 , 826 quasi-independent sib pairs ) . The data were collected from two twin cohorts; an adolescent sample ( which included non-twin siblings ) [11 , 12] and an adult sample [13] . As described in the Materials and Methods section below , prints were not available for digits IV and V for participants from the adult study . Given the evidence for developmental field effects from quantitative genetic analysis , we conducted both univariate ( ARC ) and multivariate ( simultaneously modeling ridge counts ( radial + ulnar ) for each of the ten fingers ) variance components linkage analyses . The aim was to determine whether loci influencing ridge count acted in a simple pleiotropic fashion ( i . e . , all fingers were influenced by the same loci to the same extent ) , or if more complicated pleiotropic patterns indicative of field effects were present ( e . g . , a quantitative trait locus's ( QTL's ) maximum influence was seen on the little finger and the effects tapered off towards the thumb ) .
As shown in Figure 1 , the strongest evidence for univariate linkage for ARC was seen at 1q42 . 2 ( 250 cM , Logarithm of odds ( LOD ) = 2 . 04; point-wise p-value = . 002 , genome-wide p-value = . 29 ) . At this position , the QTL explained 21% of the variance in ARC , while the multivariate test revealed low QTL factor loadings across the five fingers , explaining 7 . 7% , 12 . 7% , 10 . 0% , 13 . 8% , and 9 . 3% of the variation in individual counts from thumb to little finger , respectively ( Figure 2A ) . As may be expected for a locus at which small pleiotropic effects were found for all digits , the multivariate test for linkage was less powerful than the univariate test ( multivariate LOD = . 46 ) , because the pattern of factor loadings is most economically summarized by the mean ( or total ) score with a single degree of freedom [14] . The next highest univariate LOD scores were found at 15q26 . 1 ( 95cM , LOD = 1 . 52; point-wise p-value = . 006 , genome-wide p-value = . 62 ) and 7p15 . 3 ( 35cM , LOD= 1 . 26; point-wise p-value = . 01 , genome-wide p-value = . 79 ) . Loci with LOD scores greater than 1 are listed in Table 1 . The strongest evidence for multivariate linkage was seen at 5q14 . 1 ( 95 cM , multivariate LOD = 3 . 34 ) . As shown in Figure 2B , the pattern of loadings is consistent with a developmental field factor whose influence is greatest on the ring finger , falling off to either side . It is interesting that the highest heritability for ridge count is seen for the middle three fingers [6 , 8] . The QTL loading was strongest for the ring finger ( 24 . 1% of the variation ) , explained around 6 . 6% and 11 . 2% of variance in ridge counts on the index and middle fingers , respectively , 2 . 6% in little finger ridge count , and less than 1% in thumb ridge count . Post-hoc univariate linkage analyses for each finger individually ( shown in Figure 3 ) confirmed this pattern of factor loadings . Pointwise simulations ( described below ) revealed that a LOD score this extreme arose by chance in 1/4 , 000 simulations , yielding a pointwise empirical p-value of 0 . 00025 . There was no evidence for linkage to the X chromosome . The highest LOD score observed ( LOD = 0 . 25 ) was for the univariate analyses at a locus at 25 cM . It is possible that the assumption of dosage correction may have obscured linkage to a gene acting in a pseudoautosomal manner . However , a post-hoc univariate analysis ( not shown here ) of the total absolute ridge count , in which there was no assumption of dosage compensation , did not yield any further evidence for linkage .
As this is the first linkage analysis for finger ridge count , these peaks are novel and there are numerous candidate genes lying under them that warrant investigation . The linkage region on Chromosome 5 contains a number of zinc-finger genes ( ZFYVE16 , ZCCHC9 , and ZBED3 ) . Similarly , the peak on Chromosome 19 ( 85 cM ) spans a cluster of zinc-finger genes . The Chromosome 15 ( 55 cM ) peak is within 5 cM of the Fibrillin 1 gene ( FBN1 ) , which is involved in maintenance of elastic fibers and anchoring epithelial cells to the interstitial matrix . Mutations in this gene result in severe developmental malformations of the hands ( among other phenotypes ) and are a major cause of Marfan syndrome and a range of other conditions that result in arachnodactyly or brachydactyly . Given findings that ridge count is correlated with finger length in a sample of individuals with Marfan syndrome [15] , it seems plausible that polymorphic variations in this gene that influence the development of the digits may have a secondary influence on the normal development finger pads and dermal ridges . Early studies attempted to link the presence of an arch pattern ( i . e . , a zero ridge count ) on any finger to blood groups , finding no linkage to the rhesus ( 1p36 . 2–1p34 ) or P1 ( 22q13 . 2 ) blood groups but some evidence for linkage to the haptoglobin locus ( 16q22 . 1 ) [16] . The small peak in both the univariate and multivariate genome scans on Chromosome 16 at 110 cM is approximately 15 cM distal to the haptoglobin locus , so it is possible that this peak may in fact replicate these early findings . As evident from Table 1 , a variety of factor loading patterns were observed during the multivariate genome scan . At the multivariate level , the genetic and environmental factor loadings ( summarized in Table 2 ) showed patterns consistent with developmental fields , such as the peaks on Chromosome 5 ( 95 cM ) and Chromosome 1 ( 35 cM ) , in which the covariation between adjacent fingers was higher than between more distal fingers . At other loci , such as the second highest multivariate linkage peak , on Chromosome 15 ( 55 cM ) , the peak loaded predominantly on a single pair of digits . These findings were anticipated by earlier multivariate genetic analyses of ridge counts that found evidence of both common and group ( or field ) genetic factors responsible for the high level of covariation between ridge counts on different digits , including some of the QTL factor patterns seen here [7] . Although the multivariate peak on Chromosome 5 was primarily driven by the fourth digit , it is unlikely that this result is a consequence of the missing data in the adult sample . The missing data from digits IV and V were not imputed , and may be considered missing completely at random ( as the decision not to collect prints on digits IV and V was unrelated to the ridge counts that would have been observed ) [17] . The analytic approach used in these analyses ( raw continuous data maximum likelihood analyses implemented in Mx [18] ) has previously been shown to yield unbiased results when the distribution in normal or slightly skewed and missing values are missing completely at random [19] . In addition , the post hoc univariate analyses ( Figure 3; in which all available data for each digit were analyzed in separate univariate analyses ) provide support for the multivariate results . As expected , the highest peak was observed for the 4th digit , followed by the 3rd and the 2nd . The absence of significant univariate results and the general dearth of peaks from the univariate analysis of ARC suggest that the simple pleiotropic model specified by using a sum score such as ARC alone is not sufficient to characterize the rich biological interrelationships influencing finger ridge count . However , in some areas , such as the peak on Chromosome 1 , we did observe QTL loadings that were consistent with pleiotropic effects . In addition , given the disparity between the digits in their contributions to the multivariate peak on Chromosome 5 , assessing the individual contributions of the fingers may also provide information that can aid in the interpretation of multivariate linkages . These analyses have shown that even for conceptually and theoretically simple phenotypes such as finger ridge count , using a single approach to linkage analyses ( such as a sum score ) may place biologically implausible restrictions upon the model , significantly reducing the power to detect effects and the interpretability of results . A limitation of our study is the well-known low power of sib-pair linkage analysis for unselected samples , which can in part be ameliorated through multivariate analysis [14 , 20] . The apparent advantages of multivariate analysis revealed here are somewhat exaggerated by our inability to calculate the total ARC for adult twins because only digits I to III were counted in that study . However , as discussed above , using the raw data maximum likelihood option in Mx , joint analysis of the six fingers for which adult data are available , together with the almost complete adolescent set , increases power of the multivariate analysis by making use of every measured data point while providing estimates of factor loadings unbiased by missing values [17] . In conclusion , we report the first linkage scan for finger ridge count , finding a significant peak on Chromosome 5 and suggestive peaks on Chromosomes 1 and 15 . Both pleiotropic QTL effects consistent with development fields and nonpleiotropic effects influencing single fingers were observed . In addition , we have demonstrated that a comprehensive approach involving both multivariate analyses of constituent phenotypes and univariate analyses of a sum score can be more informative than simple univariate analyses in the presence of complex pleiotropic models .
Rolled fingerprints were collected from twin pairs and their available siblings by research nurses trained to ensure that prints contained the centre of the pattern and all triradii . In the adult study , because of a shortage of time in the protocol , prints were only collected from the first three fingers of each hand ( it is much harder and takes longer to obtain good prints of digits IV and V ( the ring and little fingers ) using traditional methods ) . From 1994 to 2005 , prints were collected using a fingerprint ink pad and archival quality paper . From 2005 onwards , prints were collected using an electronic rolled fingerprint scanner ( Smiths Heimann Biometrics ACCO1394 , http://www . shb-jena . com/ACCO1394_scanner_AQ . pdf ) . The majority of ridge counts analyzed here were scored from the inked prints and counted by eye using a binocular dissecting microscope ( counting was performed by three of the authors , BM , DZL , and SEM ) . The number of ridges lying between the center of the pattern ( core ) and the triradius/triradii ( delta ) were counted using standard conventions [6] . Ridge counts for ten individuals were scored from the electronic prints using a purpose-built software package [21] . Summary statistics are given in Table 3 . Since ARC is calculated by summing the ridge counts of all ten fingers , it could not be calculated for individuals with missing data , including all the adult twins . The phenotypic correlations between digits and ARC are shown in Table 4 . To improve computational efficiency phenotypes were corrected for mean differences between males and females , and transformed to z-scores prior to analysis . Genotypic data were available for 1 , 230 twins and sibs with fingerprint phenotypes from the adolescent and 664 twin individuals from the adult study ( as detailed in Table 3 ) . In addition , 110 nongenotyped monozygotic pairs from the adolescent study were included in these analyses to allow the within-family shared variance to be partitioned into that due to additive and dominant genetic effects , respectively [22] . The cleaning and error checking of the genotypic data for the adolescent and adult studies have been described in detail elsewhere [23 , 24] . In summary , the adolescent genotypic data was composed of three waves of genotyping . Each wave of genotyping included overlapping samples and markers in order to check data quality . Following error checking and data cleaning [24] , the resulting genotypic dataset comprised 796 markers ( 35 of which are duplicates ) at an average spacing of 4 . 8 cM ( Kosambi ) . Similarly , the adult genotypic data was composed of four waves of genotyping , with duplicate individuals and markers between each wave of genotyping . Following error checking and data cleaning [23] , the resulting genotypic dataset comprised a dense map of 1770 markers ( 394 of which are duplicates ) at an average spacing of 6 . 1 cM ( Kosambi ) . Since the adolescent and adult marker sets only partly overlapped , Identity By Descent ( IBD ) estimates and information contents were obtained separately for the two samples using a standard map[25] at 5cM intervals across the genome using MERLIN 1 . 0 . 1[26] . As the IBD estimates were made at fixed points along the chromosomes , the data from the two samples could then be jointly modeled at 5-cM intervals . Characteristics of these participants and the genotypic information available are summarized in Table 3 . All linkage analyses were conducted by variance components analysis using raw data maximum likelihood methods implemented in Mx1 . 63 [18] . For the autosomal univariate variance components QTL analysis of ARC , a likelihood ratio chi-square test was used to compare the fit of the alternate model , in which the total variance was modeled as the sum of the additive genetic , dominant genetic , QTL , and unique environmental variances to a null model in which variance due to the QTL was set to zero . Significant and suggestive thresholds and genome-wide empirical p-values were obtained by simulation . Data for 1 , 000 simulated unlinked genome scans that preserved the pedigree structures , information content , and missing data patterns were obtained using MERLIN–simulate [26] . IBDs were extracted from the simulated data , each replicate was analyzed in the same way as the observed data , and the highest LOD score for each chromosome was recorded . Empirical significant and suggestive thresholds were then estimated by extracting the LOD scores that were obtained with a probability of 0 . 05 ( i . e . , once in every 20 null replicates ) and once per genome scan , respectively . Pointwise empirical p-values were obtained by calculating how often a result as extreme as that which was observed , for the given map position , occurred by chance within the simulated data . Genomewide empirical p-values were obtained by extracting the highest LOD score from each simulation replicate and recording how often a result as extreme as that which was observed occurred by chance within these simulated data . For the autosomal multivariate analysis of individual finger ridge counts , use of raw data within Mx analyses allowed the inclusion of individuals with missing data ( in particular , all the adults twins missing prints for digits IV and V ) . A similar alternate model was used . To provide the most conservative test of QTL variance , saturated factor models were fitted for A , D , and E components , with the constraint for A and D matrices that the factor loadings , patterned as a 5 × 5 Cholesky decomposition , be equal for corresponding fingers on left and right hands . In this way , we sought to capture the major QTL features while minimizing capitalization on chance . As summarized in Figure 4 , the alternate model thus contained five additive genetic factors , five dominant genetic factors , a single QTL factor , and ten unique environmental factors ( patterned as a full 10 × 10 Cholesky decomposition ) . The single QTL factor influenced all phenotypes and contained five estimated parameters , one for each of the five pairs of digits . In the null model , the QTL factor loadings were set to zero . A likelihood ratio chi-square test with five degrees of freedom was used to test the difference in fit of these two models . To obtain LOD score equivalents for this test , was converted to a p-value , which was then transformed to a standard LOD score . Given the time required to conduct the multivariate linkage analysis ( over an hour per marker on our Linux server ) , calculation of empirical significance values was not feasible . Instead , a pointwise empirical p-value was calculated at the highest peak using simulated IBD data information derived from ped files made using MERLIN–simulate ( 4 , 000 replicates ) . For X chromosome linkage analyses , we implemented a simple extension of the X-linked variance components model [27] , in which an extra additive genetic variance component is modeled with the coefficient of relatedness ( usually set to 1/2 in the autosomal case ) corrected for the sexes of the siblings for each sib-pair combination . As for the autosomal additive polygenetic case , covariation among relatives due to additive X-linked variance arises because of alleles shared IBD . Assuming complete random X-inactivation ( lyonization ) , then , the coefficient of X-chromosome relationship [28] was set to 1/2 for brother–brother pairs , 3/8 for sister–sister pairs , and 1/4 for opposite-sex pairs . In the multivariate analyses , the additional X-linked additive genetic variance component was patterned as a 5 × 5 Cholesky decomposition , with parameters constrained to be equal for corresponding fingers on left and right hands . As for the autosomal case , in the multivariate analyses a single QTL factor was modeled that loaded on all phenotypes and contained five estimated parameters , one for each of the five pairs of digits . The QTL model also assumed complete random X-inactivation , so that the X-linked QTL variance in females was set to be half that of males . Following the suggestion of Kent et al . [29 , 30] , separate unique environmental effects were estimated for males and females to allow for genotype or environment interactions with sex . Based on the results of these analyses , a post hoc decision was made not to obtain empirical p-values for these X-linked analyses .
The National Center for Biotechnology Information ( NCBI ) Online Mendelian Inheritance in Man ( OMIM ) database ( http://www . ncbi . nlm . nih . gov/sites/entrez ? db=OMIM ) accession numbers for the syndromes discussed in this paper are: Marfan syndrome , #154700 and Dermatoglyphics—arch on any digit , 125570 . The NCBI OMIM database accession numbers for the genes discussed in this paper are Fibrillin 1 ( FBN1 ) , *134797; Haptoglobin blood group , *140100; P1 blood group , #111400; and Rhesus blood group , +111700 . The NCBI Entrez ( http://www . ncbi . nlm . nih . gov/sites/gquery ) database accession numbers for the genes discussed in this paper are ZBED3 , 84327; ZCCHC9 , 84240; and ZFYVE16 , 9765 . | Finger ridge count ( an index of the size of the fingerprint pattern ) has been used as a model trait for the study of human quantitative genetics for over 80 years . Here , we present the first genome-wide linkage scan for finger ridge count in a large sample of 2 , 114 offspring from 922 nuclear families . Our results illustrate the increase in power and information that can be gained from a multivariate linkage analysis of ridge counts of individual fingers as compared to a univariate analysis of a summary measure ( absolute ridge count ) . The strongest evidence for linkage was seen at 5q14 . 1 , and the pattern of loadings was consistent with a developmental field factor whose influence is greatest on the ring finger , falling off to either side , which is consistent with previous findings that heritability for ridge count is higher for the middle three fingers . We feel that the paper will be of specific methodological interest to those conducting linkage and association analyses with summary measures . In addition , given the frequency with which this phenotype is used as a didactic example in genetics courses we feel that this paper will be of interest to the general scientific community . | [
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Ridge Count Shows Significant Multivariate Linkage to 5q14.1 |
Chagas disease is the highest impact parasitic disease in Latin America . We have proposed that changes in Trypanosoma cruzi-specific immune responses might serve as surrogate indicators of treatment success . Herein , we addressed in a long-term follow-up study whether cure achieved after treatment can be predicted by changes in non-conventional indexes of anti-parasite serological and T cell activities . T . cruzi-specific T cell responses , as measured by interferon-γ ELISPOT and T . cruzi-specific antibodies assessed by ELISA , hemagglutination and immunofluorescence tests as well as by a multiplex assay incorporating 14 recombinant T . cruzi proteins were measured in 33 patients at 48–150 months post-benznidazole treatment . Cure — as assessed by conventional serological tests — was associated with an early decline in T . cruzi-specific IFN-γ-producing T cells and in antibody titers measured by the multiplex serological assay . Changes in the functional status and potential of T . cruzi-specific T cells , indicative of reduced antigen stimulation , provided further evidence of parasitological cure following benznidazole treatment . Patients showing a significant reduction in T . cruzi-specific antibodies had higher pre-therapy levels of T . cruzi-specific IFN-γ- producing T cells compared to those with unaltered humoral responses post-treatment . Monitoring of appropriate immunological responses can provide earlier and robust measures of treatment success in T . cruzi infection .
Chagas disease is the highest impact parasitic disease in Latin America and the most common cause of infectious myocarditis in the world [1] . The goal of treatment of humans in the chronic phase of Trypanosoma cruzi infection is to prevent the development of heart disease and infection by via blood transfusion , congenital transmission and organ transplants [2] . However , treatment in adult chronic patients is not widely used mainly because of the lack of early metrics of treatment efficacy and the potential adverse effects of these therapeutics [3] . Several studies in adult patients with mild disease symptoms have demonstrated the clinical benefits of treatment with benznidazole [4 , 5] . However , the results of the recently published BENEFIT clinical trial [6] has raised questions about the benefits of benznidazole treatment in subjects with established cardiomyopathy , thus emphasizing that therapeutic interventions would have greatest benefit when delivered early in the infection . The current criterion of a positive response to treatment is the complete loss of reactivity in serially performed conventional serological tests ( ELISA , hemagglutination and immunofluorescence ) , as well as the lack of progression to more severe clinical conditions of Chagas disease . The decline in serologic titers using current standard tests is very slow , often requiring > 24 months for antibody titers in conventional tests to begin to fall; complete conversion to negative serology can take more than 10 years [4 , 7–11] . Likewise , disease progression also occurs over decades and does not occur in all infected individuals [4 , 5] . Consequently , the development of surrogate markers of treatment efficacy is needed for an early assessment of successful treatment and the evaluation of new therapeutic approaches in the chronic phase of T . cruzi infection . CD4+ and CD8+ T cells derived from patients with chronic T cruzi infection have been shown to produce a variety of cytokines [12–18] . However recent studies using polychromatic flow cytometry revealed that CD4+ and CD8+ T cells with the capacity to produce only one cytokine ( i . e . monofunctional T cells ) in response to T . cruzi antigens is a common feature in adults with chronic Chagas disease [19–21] . Of note , monofunctional T cells are more prevalent in patients long-standing infections , generally accompanied by advanced cardiomyopathy [20 , 21] , while polyfunctional T cells are often found in children who have shorter term infections [19] . This is consistent with the profile of pathogen-specific T cells in other infections where long-term antigen persistence maintains an active pathogen-specific T cell population but with increasing impairment of T cell function over time . This process known as immune exhaustion has been described for persistent viral , bacterial and protozoan infections [22–27] and is characterized by the loss of IL-2 production , cytokine polyfunctionality , as well as proliferative capacity followed ultimately , by defects in the production of IFN-γ , TNF-α , chemokines and degranulation potential [24] . Several other features of exhausted T cells , such as high expression of inhibitory receptors , a low expression of the IL-7 receptor and high dependence on the presence of antigen for T cell maintenance have been documented in patients with very long-term T . cruzi infections [20 , 28–30] . We have proposed that changes in T . cruzi-specific IFN-γ-producing T cells [30] and declines in parasite-specific antibodies as measured by the non-conventional multiplex method might serve as surrogate indicators of treatment success , as determined in a 3-5-year post-treatment follow-up study in chronic Chagas disease patients [7 , 30] . We hypothesize that treatment decreases parasite load , thus diminishing the antigen necessary to continually activate T . cruzi-specific T cells and B cells . In patients successfully cured of the infection , a stable change in T and B cell phenotype and activation , in line with antigen-independent immunological memory , would be expected . In this study , the evolution of the functional profile of T . cruzi-specific T cells and of the humoral immune response to multiple T . cruzi antigens , in association with changes in conventional serological tests — an accepted marker of treatment efficacy — was assessed in 33 subjects chronically infected with T . cruzi over ~8 years following treatment with benznidazole . We present evidence that cure — assessed by conventional serological tests — achieved many years after treatment with benznidazole was associated with an early decline in T . cruzi-specific IFN-γ-producing T cells , and in antibody titers measured by the multiplex assay . Changes in the activation status and potential of T . cruzi-specific T cells , indicative of reduced antigen stimulation , provided additional evidence of parasitological cure following benznidazole treatment . These results further support the case for using immunological markers as indicators of treatment efficacy in T . cruzi infection .
T . cruzi–infected adult volunteers aged 23–54 years were recruited at the Chagas Disease Section of Hospital Interzonal General de Agudos Eva Perón , Buenos Aires , Argentina . T . cruzi infection was determined by indirect immunofluorescence assay , hemagglutination , and enzyme-linked immunoassay techniques [31] performed at the Instituto Nacional de Parasitologia Dr . Mario Fatala Chaben , Buenos Aires , Argentina . Chronically infected subjects were evaluated clinically and stratified according to a modified version of Kuschnir grading system [7 , 32] . Individuals in group 0 had normal electrocardiograph , normal chest radiograph , and normal echocardiograph findings ( n = 27 , median age = 39 years , range = 23–54 years ) , and subjects in group 1 had normal chest radiograph and echocardiograph findings but abnormal electrocardiograph findings ( n = 6 , median age = 42 years , range , 30–50 years ) . Treatment consisted of benznidazole , 5 mg/kg per day for 30 days [5–9] . Clinical , serological and immunological analysis was performed prior and after treatment . Patients enrolled in this study did not change the clinical status during the follow-up period . This protocol was approved by the institutional review boards of the Hospital Interzonal General de Agudos Eva Perón , Buenos Aires , Argentina and the University of Georgia , GA , USA . Signed informed consent was obtained from all individuals before inclusion in the study . PBMCs were isolated by density gradient centrifugation on Ficoll-Hypaque ( Amersham ) and were cryopreserved in a solution of 20% dimethylsulfoxide in heat-inactivated fetal calf serum for later analysis . Blood to be used for serum analysis was allowed to coagulate at 4°C and centrifuged at 1000 g for 15 min for sera separation . The number of T . cruzi–specific IFN-γ– and IL-2–secreting T cells was determined by ex vivo ELISPOT using a commercial kit ( ELISPOT Human IFN-γ or IL-2 ELISPOT Set; BD ) , as described elsewhere [33] . To avoid inter-experiment variations , assays were conducted with paired samples from different time points assayed in the same experiment . Each time point was assessed 1–3 times . mAb anti-CD3-fluorescein isothiocyanate ( FITC ) , anti-CD134 ( FITC ) , anti-IFN-γ ( FITC ) , anti-CD25 ( PE ) , anti-CD154 ( PE ) , anti-CD3-peridinin chlorophyll protein ( PerCP ) , anti-CD4 ( PerCP ) , anti-CD27-allophycocyanin ( APC ) , anti-TNF-α ( APC ) and anti-CCR7-phycoerythrin-Cy7 ( PE-Cy7 ) and anti-CD4 ( APC-Cy7 ) were purchased from BD Pharmingen , USA . PBMCs isolated from T . cruzi-infected subjects were stimulated with 15 μg/ml T . cruzi amastigote lysate or medium alone in 48-well plates at 37°C in a CO2 incubator for 16–20 h . Ten micrograms of brefeldin A per ml was added to the samples for the last 6 h of incubation . After stimulation , PBMCs were removed from the plates and stained for cell surface markers followed by fixation and permeabilization with cytofix/cytoperm and intracellular staining with a combination of monoclonal antibodies specific for IFN-γ , TNF-α and CD154 ( CD40L ) . In order to confirm that cytokine/co-stimulation expression was derived from T cells , antihuman CD3 was added in polyfunctional staining assays in combination with CD4 , IFN-γ and TNF-α or CD4 , IFN-γ and CD154 , respectively . Typically , 500 , 000 lymphocytes were acquired on a FACScalibur ( Becton Dickinson Immunocytometry Systems , USA ) and analyzed using FlowJo software ( TreeStar , Inc . , USA ) . Lymphocytes were identified based on their scatter patterns and CD4 expression for the combination of IFN-γ , TNF-α and CD154; and based on scatter patterns as well as CD3 and CD4 expression for the combination of IFN-γ and TNF-α or IFN-γ and CD154 . Boolean combination gating was then performed to calculate the frequencies of expression profiles corresponding to the seven possible combinations of functions by using FlowJo . After subtracting the background values , the proportions of the different subsets were expressed as percentages of total cytokine or CD154-positive cells . Responses to the T . cruzi lysate were considered positive , for any particular subset , if the frequency of cytokine/CD154-positive T cells was threefold higher than the frequency in medium alone and above 0 . 07% of total CD4+ T cells , since the limit of detection was set at 0 . 01% . Serum specimens were screened for antibodies reactive to a panel of 14 recombinant T . cruzi proteins in a Luminex-based format , as previously described [34] . Serological responses to each individual T . cruzi protein were considered to have decreased during the study period if the mean fluorescence intensity in at least one recombinant protein declined by 50% relative to that of the time 0 ( pretreatment ) sample assessed concurrently . Comparisons on the changes in T . cruzi-specific antibodies after treatment , measured by conventional serological tests , were performed using the Mann-Whitney U test . T cell responses at different time points were compared by Friedman range test . Comparisons of proportions were performed by use of the χ2 test and Fisher’s exact test . Differences were considered to be statistically significant at P<0 . 05 .
We have previously shown in a 3–5 year follow-up study that the frequency of peripheral IFN–γ-producing T cells responsive to T . cruzi antigens declined as early as 12 months after treatment with benznidazole and subsequently became undetectable in a proportion of treated subjects [30] . In some cases , these individuals with declining T cell responses experienced rebounds in parasite-specific T cell responses several years after treatment . Additionally , some subjects had undetectable IFN-γ-producing T cells ( i . e . below background levels ) prior to treatment that became detectable after treatment , whereas the frequencies of IFN-γ-producing T cells did not change relative to pretreatment in a fourth subset of subjects [30] . Herein , we report a 4-12-year follow-up ( median 8 years ) of humoral and cellular T cell responses in 33 of these subjects . All subjects for which IFN-γ ELISPOT responses fell below the level of detection between 12–36 months following treatment with benznidazole ( n = 12 ) showed a later rebound in IFN-γ-producing T cells ( i . e . range 24–72 months post-treatment ) [Table 1 , Group 1; Fig 1A] . In contrast , in the remaining subjects , T cell responses did not change significantly during long-term follow up ( Table 1 , Groups 2–4; Fig 1B–1D ) , Likewise , IFN-γ ELISPOT responses are relatively stable in 6 untreated subjects with a 48–60 month-follow-up ( Fig 1E ) . Monitoring of T . cruzi-specific humoral immune responses assessed by the conventional serological tests , as well as by the multiplex assay that examines responses to 14 individual T . cruzi proteins [34] , was conducted at least yearly following treatment with benznidazole . The levels of T . cruzi-specific antibodies measured by conventional serology significantly declined over time in subjects with decreased or rebounding IFN-γ-ELISPOT responses following treatment with benznidazole ( Table 2 and Fig 2A and 2B ) whereas antibody titers remained relatively stable in the other patient groups ( Table 2 , Fig 2C and 2D ) . Of note the seven patients who showed conversion from seropositive to seronegative–the standard metric of infection cure—on at least 2 of the 3 conventional serological tests were patient groups 1 and 2 ( Table 2 , Fig 2A and 2B ) . Conversion from seropositive to seronegative was observed on average >5 years post-treatment ( 24–96 months ) and was sustained up to 12 years post treatment ( Fig 2B , subject PP31 ) . In concordance with conventional serology , a multiplex assay utilizing recombinant proteins from T . cruzi also revealed a higher rate of declining antibody titers among subjects with decreased or rebounding ELISPOT responses ( Table 2 , Fig 3A–3D ) . Seventeen out of nineteen patients with a rebound or a significant decrease in IFN-γ-producing T cells following treatment with benznidazole showed a fall in the levels of antibodies specific for one more recombinant proteins in comparison to 4 out of 13 in the group of patients in which T cell responses remained unchanged or became detectable after treatment ( Table 2 , Fig 3A–3D ) . Notably , the multiplex assay detected declines in antibody levels as early as 2–24 months post-treatment ( Fig 3A–3D ) while declines in conventional serologic tests were not evident until 24–48 months post-treatment ( Fig 2A and 2B ) . Conversion from seropositive to seronegative by conventional serological tests can take up to 9 years to occur ( Fig 2A and 2B ) . Thus , declines in T . cruzi-responsive IFN-γ-producing T cells and T . cruzi-specific multiplex-detected antibodies following benznidazole treatment preceded and were predictive of conversion to negative conventional serology , the accepted standard of treatment success . As previously reported [30] , IL-2-producing T cells were low in chronically T: cruzi-infected subjects and changed in concert with IFN-γ T cell responses after treatment with benznidazole ( Fig 2A–2E ) . Treatment success as measured by declining T . cruzi-specific antibody responses was not associated either with the age of subject at initiation of treatment or the baseline T . cruzi-specific antibody titers . However , subjects with declining antibody titers as a group had higher pre-treatment frequencies of IFN-γ- and IL-2 producing T cells as compared to patients who showed no change in humoral responses following treatment ( Fig 4 ) . Since rebound in T . cruzi-specific T cells making IFN-γ was associated with declining serological titers , suggestive of a decreased presence of parasite antigen , we hypothesized that these T . cruzi-responsive T cells re-emerging long-term after treatment would result in enhanced functional capacity of T . cruzi-specific T cells . Group 1 subjects exhibited an increase in single CD4+CD54+ and CD4+IFN-γ+ T cells ( Fig 5B–5D ) coincident with a decrease in single CD4+TNF+ T cells ( Fig 5B–5E ) following treatment with benznidazole . Some subjects also showed an increase in dual IFN-γ+CD154+ T cells ( Fig 5C–5E ) or polyfunctional T cells with the ability to express IFN-γ; TNF-α; and CD154 ( Fig 5E ) , These findings show that successful treatment resulted in a change of the functional profile of parasite-specific T cells with a restoration of the co-stimulatory function , generally impaired in chronic infections .
One of the primary drawbacks in treatment of chronic T . cruzi infections is the difficulty of assessing treatment efficacy [4 , 35 , 36] , principally in the short term . In this study , we investigated if the early , post-treatment changes in T . cruzi-specific T cell and antibody responses , previously reported by our group [30] , are predictors of treatment efficacy . To answer this question we compared these non-conventional immune assessments with the conversion from positive to negative conventional serology — the accepted standard of cure — in a longitudinal over ~8-year post-benznidazole treatment follow-up study . Our study revealed that cure—as determined by seronegative conversion by conventional serology—was strongly correlated with an early decline in both T . cruzi-specific T cells and in the levels of antibodies specific for a panel of T . cruzi antigens . Significant declines in IFN-γ-producing T cells and multiplex-monitored antibody responses post-treatment also preceded detection of reductions in anti-T . cruzi antibodies detectable by conventional serological tests . In contrast , subjects exhibiting stable T cell responses post-treatment were generally associated with unaltered conventional and multiplex-assessed humoral responses . Thus , this work identifies dependable and early markers of treatment efficacy in Chagas disease . These results support and extend our previous studies [7] indicating the superiority of assaying responses to >10 recombinant proteins using a multiplex format over conventional serologic tests . Other studies have also demonstrated that the use of recombinant proteins as antigens can often detect changes in parasite-specific antibodies earlier than the complex T . cruzi antigen preparations normally used in many conventional tests [37 , 38] . However , in 15 out of the 33 patients evaluated in this study slight or no changes in T . cruzi-specific humoral and cellular T cell responses were observed , suggesting a failure of treatment and confirming previous studies showing that benznidazole treatment is not uniformly successful curing T . cruzi infection [4 , 6] . Some subjects with declining or negative anti-T . cruzi antibody levels and T cell responses experienced rebounds in T cell responses , prompting the question of whether these T cell reflected renewed antigen stimulation , and thus persistence of T . cruzi infection . However rebounding IFN-γ-producing T cells were associated with decreasing serological titers by both conventional and multiplex assays and two of the seven subjects who converted to negative conventional serology–the accepted standard of cure—exhibited this rebound in T cell responses . Therefore , it seems likely that these parasite-specific T cells in rebound responses are maintained in the absence of or very low levels of antigen , a characteristic of TCM . Such responses are evident in mice cured of T . cruzi infection by benznidazole treatment [8 , 39 , 40] . Herein , benznidazole treatment resulted in a different functional quality of CD4+ T cells with a prominent decline in single producers of TNF-α and an increase in either monofunctional or polyfunctional CD4+ T cells expressing CD154 after treatment . Several studies have shown that constant antigen stimulation during chronic infections might skew T cell responses to single TNF-α-producing T cells [41] and low CD154 expression [42 , 43] which are restored after suppression of antigen load [41 , 44] . Other studies have also shown that therapy with benznidazole in the chronic phase of the infection resulted in a shift toward a type- T cell profile profile [45–47] . Collectively , these findings further support that parasite persistence in chronic T . cruzi infection induces significant alterations in T cell function . An interesting observation that deserves further investigation is that subjects who showed the greatest decrease in T . cruzi-specific antibodies following treatment also had on average higher baseline levels of IFN-γ-producing T cells compared with subjects with modest or no changes in humoral responses . Studies in the experimental models have suggested that the quality of the anti-T . cruzi immune response plays a role in the efficacy of benznidazole treatment [48–51] . Studies in larger patient groups and in experimental models are needed to confirm these findings . This study validates the ability of appropriate and sensitive immunological tests to provide early evidence of treatment efficacy in chronic Chagas disease . Providing tools to not only monitor but to more rapidly predict treatment success or failure will facilitate the development of new and better therapeutic options in Chagas disease . | This study demonstrates that alterations in immunological parameters early after treatment with benznidazole in Chagas disease patients are predictors of treatment efficacy . Cure was associated with an early decline in T . cruzi-specific IFN-γ-producing T cells and in antibody titers and with high basal levels of T . cruzi-specific T cells . | [
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] | 2016 | Treatment Success in Trypanosoma cruzi Infection Is Predicted by Early Changes in Serially Monitored Parasite-Specific T and B Cell Responses |
The hydatid disease parasite Echinococcus granulosus has a restricted lipid metabolism , and needs to harvest essential lipids from the host . Antigen B ( EgAgB ) , an abundant lipoprotein of the larval stage ( hydatid cyst ) , is thought to be important in lipid storage and transport . It contains a wide variety of lipid classes , from highly hydrophobic compounds to phospholipids . Its protein component belongs to the cestode-specific Hydrophobic Ligand Binding Protein family , which includes five 8-kDa isoforms encoded by a multigene family ( EgAgB1-EgAgB5 ) . How lipid and protein components are assembled into EgAgB particles remains unknown . EgAgB apolipoproteins self-associate into large oligomers , but the functional contribution of lipids to oligomerization is uncertain . Furthermore , binding of fatty acids to some EgAgB subunits has been reported , but their ability to bind other lipids and transfer them to acceptor membranes has not been studied . Lipid-free EgAgB subunits obtained by reverse-phase HPLC were used to analyse their oligomerization , ligand binding and membrane interaction properties . Size exclusion chromatography and cross-linking experiments showed that EgAgB8/2 and EgAgB8/3 can self-associate , suggesting that lipids are not required for oligomerization . Furthermore , using fluorescent probes , both subunits were found to bind fatty acids , but not cholesterol analogues . Analysis of fatty acid transfer to phospholipid vesicles demonstrated that EgAgB8/2 and EgAgB8/3 are potentially capable of transferring fatty acids to membranes , and that the efficiency of transfer is dependent on the surface charge of the vesicles . We show that EgAgB apolipoproteins can oligomerize in the absence of lipids , and can bind and transfer fatty acids to phospholipid membranes . Since imported fatty acids are essential for Echinococcus granulosus , these findings provide a mechanism whereby EgAgB could engage in lipid acquisition and/or transport between parasite tissues . These results may therefore indicate vulnerabilities open to targeting by new types of drugs for hydatidosis therapy .
Cystic echinococcosis ( CE ) , one of two major types of hydatid disease , is a worldwide zoonosis caused by the larval stage ( metacestode ) of Echinococcus granulosus sensu lato ( E . granulosus s . l . ) , which includes a series of species traditionally considered to comprise different strains or genotypes of E . granulosus [1 , 2] . The larva forms unilocular bladder-like cysts ( referred to as hydatid cysts ) that establish and gradually grow within the viscera ( mainly liver and lungs ) of a wide range of mammalian species ( mainly domestic ungulates ) as well as humans [1] . CE is considered a chronic , complex and neglected disease , which is re-emerging as an important public health problem [3–6] . For many years surgery was considered the only effective therapy , although it is not recommended for patients with cysts disseminated into different organs , and had a relatively high morbidity , relapse and mortality rates [7 , 8] . Currently , the advent of antihelminthic drugs ( benzimidazole carbamates , mainly mebendazole and albendazol ) has led to an alternative therapy which comprises pre- and post-operative chemotherapy , combined with percutaneous drainage of hydatid cysts ( a procedure known as PAIR for puncture , aspiration , injection , reaspiration ) [7 , 8] . In comparison with surgery this approach showed greater clinical and anti-parasitic efficacy ( lower rates of morbidity , mortality , and disease recurrence and shorter hospital stays [9] ) . In addition , antihelminthic drugs are chosen for the treatment of uncomplicated cysts , as well as for long-term post-surgical treatment [8] . Benzimidazoles bind to β-tubulin and interfere with microtubule formation , thus affecting motility , cell division , secretion processes , as well as perturbing the uptake of glucose by helminths [10 , 11] . Nevertheless , benzimidazole treatment has shown limited efficacy against large cysts and the occurrence of side-effects has also been reported [12] . Therefore , the development of novel drugs against E . granulosus s . l . therapy is required . Advancing knowledge of parasite biology would facilitate the identification of new drug targets for a more specific CE therapy . Antigen B ( EgAgB ) is an abundant lipoprotein of hydatid cyst fluid that has been postulated as a carrier of essential lipids for E . granulosus s . l . [13–16] . This is based on the fact that cestodes have lost both degradative and biosynthetic pathways for common fatty acids and sterols [17 , 18] , and EgAgB contains a heterogeneous mixture of lipids including free and esterified fatty acids and sterols [15] . Thus , EgAgB may be of foremost importance for parasite lipid metabolism , representing an interesting target for chemotherapy . EgAgB is an alpha helix-rich 230 kDa lipoprotein [15] , which has been considered to be the most specific Echinococcus-genus antigen for human serodiagnosis of hydatid disease [19–21] as well as being an immunomodulatory parasite component [22 , 23] . It belongs to a cestode-specific family of proteins that bind hydrophobic ligands , referred to as hydrophobic ligand binding proteins ( HLBPs ) . The ligands present in the EgAgB complex account for approximately 40–50% of the total mass of the native antigen and consist of a variety of neutral ( mainly triacylglycerides , sterols and sterol esters ) and polar ( mainly phosphatidylcholine ) lipids [15] . At the protein level , native EgAgB contains around a dozen apolipoproteins or subunits [15] , which are encoded by a polymorphic multigene family comprising five clades named EgAgB1 to EgAgB5 [24–29] . Interestingly , EgAgB isoforms are expressed differentially during the life-cycle stages of the parasite , as well as within distinct tissues of a given developmental stage; EgAgB1 to EgAgB4 are expressed in the metacestode stage whereas EgAgB5 seems to be expressed in the adult stage . Furthermore , in the metacestode , EgAgB1 to EgAgB4 are expressed in the germinal layer , but EgAgB3 seems to be the most abundantly expressed in protoscoleces [29] . Similar evidence of differential expression of antigen B subunits has also been obtained for the closely related species E . multilocularis [30] . The proteins encoded by EgAgB genes are each approximately 8 kDa in mass , and the different isoforms designated EgAgB8/1 to EgAgB8/5 . Comparison of their amino acid sequences shows that EgAgB8/1 , EgAgB8/3 and EgAgB8/5 are more similar to each other than to EgAgB8/2 and EgAgB8/4 ( Fig . 1 ) . One of the features of EgAgB subunits is their ability to self-associate into large complexes . Analysis of native EgAgB from hydatid cyst fluid showed oligomers of 16 , 24 and 32 kDa that are built from the 8 kDa subunits [31] , as found in SDS-PAGE analysis under reducing conditions [32] . Recombinant subunits of EgAgB8/1 , EgAgB8/2 and EgAgB8/3 are also capable of self-associating into oligomers of 16 and 24 kDa , and also into high-order oligomers of more than 100 kDa , estimated by size exclusion chromatography [33] or native polyacrylamide gel electrophoresis [34] . These results indicate that recombinant EgAgB subunits share structural features with native EgAgB , which agrees with the fact that EgAgB subunits present an electrostatic profile compatible with molecular aggregation [16 , 33] and suggest that lipids may not be indispensable for oligomerization . Nevertheless , the contribution of the lipid moiety to the oligomerization process has not yet been formally considered . As already mentioned , EgAgB particles contain a mixture of lipid classes ranging from highly hydrophobic lipids to a variety of phospholipids [15] . The lipid binding properties of different members of EgAgB family have been partially characterised and constitute a piece of information that may contribute to elucidate EgAgB functions . EgAgB8/1 and EgAgB8/2 ligand binding properties were analysed by Chemale and collaborators , who used recombinant subunits ( delipidated using the hydrophobic resin Lipidex 1000 ) and fluorescent probes to examine their interaction with fatty acids [35] . They observed that both subunits were capable of binding the fatty acid analogue 16-AP ( 16- ( 9-anthroyloxy ) palmitate ) , but not the fluorescent probes DAUDA ( 11- ( dansylamino ) undecanoic acid ) , ANS ( 1-anilinonaphthalene-8-sulfonic acid ) or DACA ( dansyl-DL-alpha-aminocaprylic acid ) . In these studies , EgAgB8/1 and EgAgB8/2 were found to bind fatty acids with similar affinity . However , differences in the ability to bind lipids among other EgAgB members could not be ruled out . In fact , the 150 kDa HLBP entity of Taenia solium ( TsM150 ) contains two classes of HLBP subunits ( 7 and 10 kDa members ) which are capable of binding different lipid probes; members of the 7kDa-TsM150 subfamily bind DAUDA , ANS and DACA , but not 16-AP , whereas members of the 10kDa-TsM150 subfamily only bind 16-AP [36] . Furthermore , analysis of the ability of EgAgB subunits to bind lipid classes different from fatty acids has not been analysed , which , considering the biochemical composition of native EgAgB-associated lipids [15] , is a potentially serious gap in our knowledge . As previously stated , the fact that EgAgB carries lipids that are essential to E . granulosus s . l . suggests a role for EgAgB in the uptake and delivery of these lipids [37] . If so , then EgAgB would be expected to transfer ligands between donor and acceptor membranes and/or membrane-embedded receptor proteins . The ability of EgAgB to transfer ligands or to interact with membranes has not yet been examined in detail . In the present work , we report a novel method of preparing lipid-free EgAgB8 subunits , which allowed the proper analysis of their oligomerization capacity and lipid binding properties . For this purpose , we purified the recombinant subunits EgAgB8/2 and EgAgB8/3 as representatives of the two distinct types of subfamilies within the multigenic EgAgB family ( Fig . 1 ) , and then removed their co-purifying ligands ( mainly phospholipids ) by reverse-phase high performance liquid chromatography ( RP-HPLC ) . We found that lipid-free EgAgB8/2 and EgAgB8/3 subunits were able to self-associate into larger oligomers , suggesting that lipids are not indispensable for oligomerization . Regarding their lipid binding properties , both subunits bound a stearic acid anthroyloxy-derivative with similar affinity , but not a fluorescent cholesterol analogue . Furthermore , using small unilamellar vesicles we found that EgAgB8/2 and EgAgB8/3 subunits are potentially capable of transferring fatty acids analogues to phospholipid membranes employing different mechanisms , in which electrostatic interactions might play an important role . Overall , these results indicate previously unsuspected features of EgAgB particle assembly and suggest interaction with cell membranes ( possibly of both parasite and host ) that present potentially new classes of therapeutic target .
Inorganic salts were acquired from Sigma Chemicals ( USA ) , Merck ( Germany ) or Carlo Erba ( France ) . Organic solvents were purchased from JT Baker ( USA ) , Merck ( Germany ) or Carlo Erba ( France ) . For purification and delipidation of recombinant EgAgB8 subunits Gluthathione Sepharose 4B resin was obtained from GE Healthcare Life Sciences ( Sweden ) , reduced glutathione and thrombin from human plasma were purchased from Sigma Chemicals ( USA ) and C8-bonded silica column was purchased from Vydac ( USA ) . For lipid analysis silica TLC plates ( 20 x 20 cm ) were obtained from Merck ( Germany ) . For size exclusion chromatography ( SEC ) experiments , Superdex 200 HR 10/30 column was from GE Healthcare Life Sciences ( Sweden ) , bovine serum albumin ( BSA ) , carbonic anhydrase and cytochrome c were acquired from Sigma Chemicals ( USA ) . For crosslinking experiments , N-ethyl-3- ( 3-dimethylaminopropyl ) -carbodiimide ( EDC ) was purchased from Sigma Chemicals ( USA ) . For ligand binding assays , fatty acid analogues 12- ( 9-anthroyloxy ) stearic acid ( 12-AS ) and DAUDA were obtained from Molecular Probes ( USA ) , whereas cholesterol analogue dehydroergosterol ( DHE ) was obtained from Sigma Chemicals ( USA ) . For ligand transfer assays , egg phosphatidylcholine ( EPC ) , brain phosphatidylserine ( PS ) , heart cardiolipin ( CL ) and N- ( 7-nitro-2 , 1 , 3-benzoxadiazol-4-yl ) -phosphatidylcholine ( NBD-PC ) , ) were purchased from Avanti Polar Lipids ( USA ) . Genes encoding EgAgB8/2 and EgAgB8/3 subunits subcloned into pGEX plasmids were generously donated by Dr . Arnaldo Zaha ( Federal University of Rio Grande do Sul , Brazil ) . EgAgB8/2 and EgAgB8/3 gene sequences were confirmed employing Macrogen sequencing facility ( Macrogen Inc . , Korea ) . EgAgB recombinant subunits EgAgB8/2 and EgAgB8/3 were expressed in Escherichia coli BL21 Codon Plus pRIL , as glutathione S-transferase ( GST ) fusion proteins , purified by affinity chromatography on immobilized glutathione and recovered by thrombin cleavage as previously described [38] . The removal of hydrophobic ligands derived from the bacterial expression system was achieved by RP-HPLC in a HPLC System ( Merck-Hitachi , Japan ) with a C8-bonded silica as stationary phase and water/acetonitrile/trifluoroacetic acid mobile phase , based on a procedure described by Meenan and collaborators to delipidate other lipid binding proteins ( LBPs ) [39] . After elution and freeze drying , proteins were refolded in a large volume of phosphate buffered saline , pH 7 . 4 ( PBS ) and then concentrated using centrifugal filter units ( Millipore EMD ) . Proper delipidation of recombinant subunits was controlled by analysing the lipid content of protein fractions subjected and not-subjected to RP-HPLC method . Lipids of pre and post-HPLC fractions were extracted using the Folch method [40] , analysed by thin layer chromatography ( TLC ) and compared with standards , as previously described for the analysis of the lipid moiety of native EgAgB [15] . Sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE , 15% ) followed by Coomassie blue staining was used to assess subunits purity [41] . Protein concentration was estimated by measuring the absorbance at 280 nm , employing molar extinction coefficients of 7030 and 1340 M−1cm−1 for EgAgB8/2 and EgAgB8/3 , respectively ( calculated from their amino acid sequences employing Biology Workbench 3 . 2 free software [Computational Biology Group , Department of Bioengineering , University of California , San Diego] ) . Lipid-free EgAgB8 subunits were analysed by circular dichroism ( CD ) spectroscopy to examine their structure . CD spectra of EgAgB8/2 and EgAgB8/3 ( 30 μM ) at 25°C in PBS were recorded on a Jasco J-810 spectropolarimeter . Data in the Far-UV ( 195–250 nm ) region were collected in 1-mm path cuvettes using a scan speed of 20nm/min with a time constant of 1 second . Molar ellipticity [θ] ( deg cm2 dmol−1 ) was calculated as described elsewhere [42] . Secondary structure calculations of EgAgB subunits were undertaken employing k2d program ( http://kal-el . ugr . es/k2d/k2d . html ) [43 , 44] . SEC experiments were carried out in an Âkta FPLC System ( GE Healthcare Life Sciences , Sweden ) . Briefly , 100 μL of lipid-free EgAgB8/2 ( 324 μM ) or EgAgB8/3 ( 320 μM ) were loaded separately on a Superdex 200 HR 10/30 column equilibrated in PBS . A flow rate of 0 . 5 mL/min was used , and elution profiles were recorded following the UV absorption at 215 and 280 nm . The column was calibrated using bovine serum albumin ( 66 kDa ) , carbonic anhydrase ( 29 kDa ) and cytochrome c ( 12 kDa ) as protein standards under the same chromatography conditions . Samples , as well as standard proteins , were analysed twice under the same conditions . Molecular weight estimation of the proteins was undertaken as described previously [45] . In order to investigate the stability of the oligomeric EgAgB complexes , serial dilutions of the proteins were also loaded under the same conditions . Cross-linking with EDC was carried out by adding a fresh solution of EDC to 30 μM lipid-free EgAgB8 subunits in PBS up to a final concentration of 5 , 10 and 20 mM EDC . Controls without adding EDC were carried out under the same conditions at the same time . The mixture was incubated for 30 minutes at 25°C under continuous stirring . The reaction was stopped by adding SDS-PAGE sample buffer , the reaction products were analysed on a 15% SDS-PAGE followed by silver staining . In parallel , non-delipidated recombinant EgAgB8 subunits , and native EgAgB ( purified from hydatid cysts as previously described [15] ) were used for comparison . In addition , cross-linking experiments were performed using a non-related lipid binding protein ( ABA-1-A1 from Ascaris suum ) as a control . Fluorescence measurements were performed at 25°C in a Fluorolog-3 Spectrofluorometer ( Horiba-Jobin Yvon ) using 2 mL samples in a quartz cuvette . Fluorescent probes DHE , DAUDA and 12-AS were used to determine lipid binding properties of EgAgB8/2 and EgAgB8/3 . Briefly , 0 . 5 μM of these probes were incubated at 25°C for 3 min in buffer 40 mM Tris , 100 mM NaCl , pH 7 . 4 ( TBS ) with increasing concentrations of EgAgB8 subunits . Emission spectra were recorded at 350–515 , 365–665 or 400–500 nm , employing an excitation wavelength of 325 , 345 and 383 nm for DHE , DAUDA and 12-AS , respectively . For 12-AS titration curves , fluorescence data were fitted using SigmaPlot software and different fittings were tested . The “one site saturation ligand binding” model showed the best r values ( r ∼ 0 . 99 ) , and was thus selected to estimate the binding constants ( Kd ) . Average values for three independent experiments are reported . Changes in the intrinsic tryptophan ( Trp-derived ) fluorescence of EgAgB8/2 ( 5 μM or 2 μM ) were monitored upon the addition of oleic acid ( 0 . 5 to 6 . 5 μM ) or cholesterol ( 0 . 7 to 19 . 4 μM ) . The emission spectra were recorded at 310–400 nm , employing an excitation wavelength of 295 nm . Small unilamellar vesicles ( SUV ) were prepared by sonication and ultracentrifugation as described previously [46] . The standard vesicles were prepared to contain 90 mol % of EPC and 10 mol % of NBD-PC , which served as the fluorescent quencher of the anthroyloxy-derivative . To increase the negative charge density of the acceptor vesicles , either 25 mol % of PS or CL was incorporated into the SUVs replacing an equimolar amount of EPC . Vesicles were prepared in TBS except for SUV containing CL which were prepared in TBS with 1 mM EDTA . The relative partition coefficient ( Kp ) of 12-AS between EgAgB and NBD-containing SUVs was determined employing a method described by Massey and collaborators [47] . The Kp was defined as: Kp=[12ASEgAgB][EgAgB]×[SUV][12ASSUV] ( 1 ) where [12ASEgAgB] and [12ASsuv] are the concentrations of 12-AS bound to EgAgB and vesicles , respectively , and [EgAgB] and [SUV] are the concentrations of protein and vesicles . The Kp for 12-AS partitioning was determined by measuring 12-AS fluorescence ( 440 nm ) at different molar ratios of SUV:EgAgB after addition of SUVs into a solution containing 7 . 5 μM EgAgB8/2 or EgAgB8/3 and 0 . 5 μM 12-AS in buffer TBS at 25°C . The Kp was calculated by fitting the equation described by De Gerónimo and collaborators [48] to our data , as follows: Frel=a×KpKp+[SUV][EgAgB]−b×[SUV][EgAgB] ( 2 ) where Frel is the difference between the fluorescence of 12-AS at a given SUV:EgAgB ratio and the fluorescence of 12-AS with an excess of vesicles , relative to the maximum difference in 12-AS fluorescence; [SUV] and [EgAgB] are the molar concentrations of SUV and EgAgB , respectively; and “a” and “b” are the fitting parameters . The partition coefficient was used to establish the conditions , for the 12-AS transfer assay , that ensure essentially unidirectional transfer , as detailed below . A Förster Resonance Energy Transfer ( FRET ) assay was used to monitor the transfer of 12-AS from EgAgB to acceptor model membranes as described previously for other LBPs [49–56] . All the transfer experiments were conducted employing a Stopped-Flow RX2000 module ( Applied Photophysics ) attached to the spectrofluorometer . Transfer assay conditions were established according to Kd and Kp values previously obtained . Kd was used to ensure low levels of unbound 12-AS ( < 5% ) , and Kp to determine SUV:EgAgB molar ratio in order to assure unidirectional transfer to SUVs . EgAgB8/2 or EgAgB8/3 subunits with bound 12-AS were mixed with NBD-PC SUVs . The NBD moiety is an energy transfer acceptor of the anthroyloxy donor group , therefore the fluorescence of the anthroyloxy fatty acid ( AOFA ) is quenched when the ligand is bound to SUVs which contain NBD-PC . Upon mixing , transfer of AOFA from protein to membrane is directly monitored by the time dependent decrease in anthroyloxy fluorescence . Final transfer assay conditions were 15:1 mol:mol EgAgB:12-AS ratio . SUVs were added ranging from 1:10 mol:mol to 1:40 mol:mol EgAgB:SUVs in TBS buffer at 25°C . Controls to ensure that photobleaching was eliminated were performed prior to each experiment , as previously described [50] . To analyse the influence of membrane surface charge on fatty acid transfer rate , SUVs with 25% negatively charged phospholipids ( PS or CL ) were employed . Data were analysed employing SigmaPlot software and all curves were well described by a single exponential function . For each experimental condition , at least five replicates were performed . Average values for three separate experiments are reported . Statistical analysis of the data was performed applying one-way analysis of variance ( ANOVA ) followed by Tukey's Post Hoc Test from GraphPad Prism software . GenBank accession numbers for EgAgB8 subunits: EgAgB8/1: AAD38373 , EgAgB8/2: AAC47169 , EgAgB8/3: AAK64236 , EgAgB8/4: AAQ74958 , EgAgB8/5: BAE94835 .
We purified lipid-free EgAgB8 subunits in order to analyse their capacity to oligomerize , their lipid binding properties , as well as their ability to transfer fatty acids to membrane vesicles . Among the five distinct EgAgB8 subfamilies , EgAgB8/2 and EgAgB8/3 were chosen because they represent the two distinct subfamilies within EgAgB family as mentioned above ( Fig . 1 ) . The recombinant subunits rEgAgB8/2 and rEgAgB8/3 were purified as GST fusion proteins from E . coli and recovered after thrombin treatment , as previously described [38] . We anticipated that rEgAgB8/2 and rEgAgB8/3 subunits would bind lipids during their synthesis in E . coli . We therefore analysed by TLC the lipids recovered by Folch extraction from the purified recombinant subunits and compared them with those from E . coli . Fig . 2A shows that fatty acids and phospholipids , mainly phosphatidylethanolamine ( PE ) and cardiolipin ( CL ) , are the major lipids present in E . coli under our extraction conditions . Extractions from both rEgAgB8 subunits yielded mainly PE and CL ( Fig . 2B ) . The absence of phosphatidylcholine , the main phospholipid found in native EgAgB [15] , may be due to the lack of this phospholipid in E . coli ( Fig . 2A ) . Our next step was to identify a method to efficiently remove bacterial ligands from the proteins . Despite published precedent [35] , we found that chromatography with Lipidex 1000 is ineffective at removing lipid from helminth LBPs [39 , 57 , 58] . We therefore exploited a method for removal of co-purifying ligands from other recombinant LBPs produced in E . coli [39] , based on RP-HPLC of unfolded protein using a C8-bonded silica and water/acetonitrile/trifluoroacetic acid as stationary and mobile phases , respectively . Both rEgAgB8/2 and rEgAgB8/3 subunits were found to bind to C8 column and to elute from this phase at about 70:30 ( v/v ) acetonitrile/water . After careful refolding employing a large volume of aqueous buffer , the efficacy of this delipidation procedure was assessed by comparing the lipid moiety of treated ( post-HPLC ) recombinant subunits compared to non delipidated ( pre-HPLC ) subunits . We found that RP-HPLC successfully removed E . coli ligands from EgAgB8 subunits ( Fig . 2B ) . In order to check the structural integrity of the proteins after delipidation , we analysed the CD spectra of lipid-free rEgAgB8 subunits after refolding . The spectra of both recombinant subunits presented two minima at 208 and 222 nm , and were consistent with predominantly alpha-helical structures ( 66% and 30% for rEgAgB8/2 and rEgAgB8/3 , respectively ) as is shown in Fig . 3 . These results are similar to CD data obtained for non-delipidated EgAgB8 subunits ( 35–40% of alpha-helix [33] ) and native EgAgB ( between 42 and 65% [14 , 59] ) . Overall , these results showed that the delipidation method based on RP-HPLC provided lipid-free subunits without significant alteration , at least of their secondary structures , permitting us to analyse the interactions of the protein components in isolation . As said , since previous oligomerization studies were performed with non-delipidated proteins , the involvement of lipids in the oligomerization of rEgAgB subunits cannot be ruled out [33 , 34] . In order to examine whether lipid-free forms are capable of self-associating , we analysed rEgAgB8 subunits by SEC in aqueous solutions . This showed that lipid-free rEgAgB8/2 and rEgAgB8/3 were eluted in defined peaks that were not commensurate with monomeric forms , according to the elution of the standard ( Fig . 4 ) . The apparent molecular weight of these subunits indicated the presence of oligomers of 62 and 39 kDa for rEgAgB8/2 and rEgAgB8/3 , equivalent to the formation of oligomers of 7–8 subunits and of 4–5 subunits , for lipid-free rEgAgB8/2 and rEgAgB8/3 , respectively . Previous reports using non-delipidated recombinant proteins revealed the presence of larger complexes of around 164 kDa for EgAgB8/2 and 113 kDa for EgAgB8/3 , as well as secondary peaks of higher molecular mass for both proteins [33] . Nevertheless , our results suggest that oligomerization of EgAgB subunits is not absolutely dependent on the presence of lipids . In order to further examine the self-assembly of EgAgB8 apolipoproteins , we performed covalent cross-linking experiments analysing the formation of oligomers by SDS-PAGE under reducing conditions ( Fig . 5A ) . We found that in the absence of EDC both subunits were predominantly present as monomers , and EDC addition led to the formation of different cross-linked products . In the range of 5 mM to 20 mM EDC , lipid-free rEgAgB8/2 formed mainly a ∼45 kDa oligomer , whereas lipid-free rEgAgB8/3 formed a heterogeneous array of oligomers ( a ladder-like pattern from around 16 to more than 97 kDa ) . Cross-linked products of higher molecular mass were found for both apolipoproteins . These results suggest that rEgAgB8 subunits have an intrinsic ability to self-assemble , but with differences in the structural organization acquired by rEgAgB8/2 and rEgAgB8/3 in aqueous solution , since the oligomers that they form have different apparent molecular weights according to SEC and EDC cross-linking analysis . For comparison , we also analysed the cross-linked products of non-delipidated rEgAgB8 subunits to assess the role of lipids in EgAgB oligomerization . This showed that non-delipidated proteins form even larger oligomers of apparent MW around 60–90 kDa and higher than 97 kDa ( Fig . 5B ) . These results agree with those previously described by Monteiro and collaborators , obtained using glutaraldehyde as cross-linker [33] , and indicate that , whilst lipids are not absolutely required , they may participate in the formation of larger complexes . In parallel , the cross-linking of natural , parasite-derived EgAgB led to the formation of oligomers of high molecular mass , showing a similar pattern to non-delipidated recombinant subunits ( Fig . 5C ) , also in agreement with previous reports [34] . Both , non-delipidated EgAgB subunits and parasite-derived EgAgB , presented larger complexes even in the absence of cross-linker ( Fig . 5B and 5C ) , in contrast to lipid-free EgAgB subunits ( Fig . 5A ) . This suggests that lipids are involved in the oligomerization of EgAgB that is commonly observed in SDS-PAGE analysis under reducing conditions [32] . As a control for cross-linking experiments , we used lipid-free ABA-1-A1 ( a helix-rich small LBP from the nematode parasite Ascaris suum ) , a protein which is not expected to oligomerize even at high concentrations [39] . Both lipid-free and non-delipidated ABA-1-A1 were treated under the same conditions and oligomers were not detected ( Fig . 5D ) . It therefore appears that lipids are not essential for EgAgB subunit self-association , although they may participate in the oligomerization process contributing to the organization of very large EgAgB particles . rEgAgB8/2 and rEgAgB8/3 exhibited different behaviors , although lipid-free rEgAgB8/2 forms an oligomer of greater size than rEgAgB8/3 , and rEgAgB8/3 appears to form more heterogeneous oligomers . These results are in concordance with previous findings on non-delipidated rEgAgB8/3 subunits , which formed the more heterogeneous oligomeric states in comparison with rEgAgB8/1 and rEgAgB8/2 [34] . Since the lipid moiety of native EgAgB includes a wide range of lipids , we analysed the capacity of our particular recombinant subunits to bind different fluorescent lipid probes , such as the fatty acid analogues 12-AS and DAUDA , and the cholesterol analogue DHE . These probes have been widely used for characterising the lipid binding properties of LBPs because of their environment-sensitive spectral properties . They have very low fluorescence emission values when free in aqueous solution , but exhibit a significant increase in emission intensity , often accompanied by a blue shift in emission spectrum , when bound to a protein's apolar ligand binding site [60] . We found that 12-AS showed a significant increase in its fluorescence emission , accompanied by a blue shift from 456 nm to 446 nm when lipid-free rEgAgB8/2 or rEgAgB8/3 subunits were added to the probe solution ( Fig . 6A and 6C ) . For both subunits , titration experiments described curves that reached saturation , in accordance with a ligand binding stoichiometry consistent with 1:1 binding per monomer ( Fig . 6B and 6D ) , with a Kd of 0 . 16 ± 0 . 09 μM for rEgAgB8/2 ( r = 0 . 9976 ) and 0 . 34 ± 0 . 02 μM for rEgAgB8/3 ( r = 0 . 9927 ) . In contrast , negligible enhancement of fluorescent emission was observed for the fatty acid analogue DAUDA upon adding lipid-free rEgAgB8/2 or rEgAgB8/3 ( S1 Fig . ) . Lipidex-treated recombinant subunits EgAgB8/1 and EgAgB8/2 behaved similarly in previous studies since they bound the fatty acid anthroyloxy derivative , 16-AP , but not DAUDA [35] . The Kd values determined for the binding of these probes by our delipidated subunits and by Lipidex-treated subunits were similar , but the latter exhibited a lower value for binding sites for monomer ( n value of approximately 0 . 3 ) , further emphasising the distinction between methods employed to remove bacterial lipids bound to the recombinant subunits . These findings suggest that the hydrophobic Lipidex resin method is not appropriate to achieve adequate delipidation . In addition , the fatty acid binding properties of EgAgB subunits could be compared with those of other HLBPs on the basis of the use of similar probes for this analysis , although in these previous studies HLBP delipidation was not reported [36 , 61 , 62] . In particular , 16-AP was employed for characterizing the binding capacity of HLBPs , from Taenia solium metacestode ( TsM150 ) [36] and Moniezia expansa ( MeHLBP ) [61] . EgAgB subunits seem to be more similar by amino acid sequence to the 10 kDa than to the 7 kDa TsM HLBP subfamily . Moreover members of the former ( referred to as recCyDA , recb1 and recm13h ) but not of the latter ( referred to as RS1 ) subfamily bind 16-AP [36] . Moreover , recb1 and recm13h were positioned into the same clade with EgAgB , whereas RS1 belonged to a different clade [62] . Furthermore , a recently described HLBP from T . solium , whose subunits grouped together with MeHLBP rather than with the 7 and 10 kDa TsM HLBP subfamilies , is also capable of binding 16-AP [62] . In the case of the MeHLBP monomers , the binding is consistent with a stoichiometry value of 1:1 , and a Kd value of 2 μM , suggesting that lipid-free rEgAgB8 subunits exhibit a higher affinity for AOFA probes [61] . Taken together , these results indicate that various HLBPs are potentially capable of binding fatty acids , and that differences in their ligand binding properties are not easy to predict from their presumed evolutionary relationships . On the other hand , the fluorescent cholesterol analogue , DHE , was found not to be bound by neither rEgAgB8/2 nor rEgAgB8/3 ( S2 Fig . ) , suggesting that lipid-free EgAgB8/2 and EgAgB8/3 subunits do not directly interact with cholesterol . The presence of cholesterol in EgAgB purified from hydatid cyst fluid [15] , suggests that it may be incorporated into the particle once EgAgB subunits have already bound their lipid ligands . On the other hand , the interaction of cholesterol with other EgAgB subunits cannot be ruled out . In order to employ natural ligands instead of fluorescent ones we evaluated the effect of cholesterol and oleic acid on the intrinsic rEgAgB8/2 fluorescence using Trp16 as a reporter . This methodology has been successfully used for other HLBP members [61 , 63] and could not be applied to rEgAgB8/3 characterisation since this subunit does not contain Trp residues . Changes in Trp16 fluorescence were not observed when cholesterol or oleic acid were added to lipid-free rEgAgB8/2 , indicating that the environment of Trp16 was not sensitive to the binding of these lipids ( S3 Fig . ) . These results agree with previous observations obtained using Lipidex-treated rEgAgB8/2 and natural fatty acids as ligands [35] , and suggest that Trp16 is not close to the putative ligand binding site for fatty acids present in this subunit . Overall , our results , together with previous data , suggest that lipid-free rEgAgB8/2 and rEgAgB8/3 apolipoproteins have a selective capacity to bind lipids , showing affinity at least for 16- and 18-C fatty acids , but not for cholesterol , indicating that these components of the natural EgAgB lipoprotein particles would not interact directly with cholesterol . How lipids carried by EgAgB complexes may be distributed within the parasite is unknown , so , we attempted to characterise the capacity of these subunits to transfer lipids to membranes . Since fatty acids , but not cholesterol , were bound by delipidated rEgAgB8 subunits , assays were designed to examine the transfer of 12-AS to phospholipid artificial membranes ( SUVs ) . To establish the conditions for the transfer measurements we used the Kd values obtained for rEgAgB8/2 and rEgAgB8/3 to ensure that less than 5% of 12-AS remained free in solution . We also determined the Kp of 12-AS between rEgAgB8 subunits and vesicles to assure unidirectional transfer of 12-AS from rEgAgB8 to the vesicles . In order to do this , SUVs containing a FRET acceptor of the anthroyloxy group donor ( NBD-PC ) were added to a solution of 12-AS:EgAgB8/2 or 12-AS:EgAgB8/3 complex . The 12-AS fluorescence decay upon incremental increase in SUV concentration for both 12-AS:EgAgB8 complexes is shown in Fig . 7 , indicating the transfer of 12-AS from EgAgB8 subunits to NBD-PC-containing vesicles . Using Eq . 2 ( see Materials and Methods ) a KP value of 0 . 62 ± 0 . 09 was obtained for rEgAgB8/2 and of 0 . 88 ± 0 . 15 for rEgAgB8/3 . Both indicate that there is a slightly higher preference of 12-AS for the phospholipid membranes . Once these conditions were established , we analysed the transfer rates of 12-AS bound to rEgAgB8/2 or rEgAgB8/3 to the vesicles . Firstly we examined the transfer rates as a function of SUV concentration to determine whether the limiting step for ligand transfer is the effective protein-membrane interaction or the dissociation of the protein-ligand complex , as has been previously established for other LBPs [49–56] . A representative time trace of 12-AS fluorescence change upon SUV addition to EgAgB8/2:12-AS or EgAgB8/3:12-AS complexes is shown in Fig . 8A . We found that the 12-AS transfer rate from rEgAgB8/2 to EPC-SUVs increased significantly from 0 . 039 ± 0 . 003 s−1 to 0 . 084 ± 0 . 005 s−1 ( p < 0 . 05 ) when SUV:protein ratio raised from 10:1 to 40:1 . In the case of rEgAgB8/3 , a trend towards an increase in 12-AS transfer rate ( from 0 . 07 ± 0 . 02 s−1 to 0 . 08 ± 0 . 02 s−1 ) was observed , but this trend did not reach statistical significance ( Fig . 8B ) . These results using zwitterionic SUVs suggest that the mechanism of 12-AS ligand transfer differed between EgAgB subunits; for rEgAgB8/2 the limiting step for transfer is the direct contact with the vesicle ( collisional mechanism ) , whereas for rEgAgB8/3 the dissociation of 12-AS from the complex seems to be the limiting step ( diffusional mechanism ) . We then proceeded to examine whether electrostatic interactions between EgAgB subunits and SUVs could affect the ligand transfer mechanism . For this purpose we carried out similar transfer rate assays using negatively charged vesicles , which were obtained by incorporating PS or CL into the acceptor vesicles ( one net negative charge per molecule of PS , two net negative charges per molecule of CL ) . Using PS-SUVs ( Fig . 8C ) , 12-AS transfer rates of both rEgAgB8 subunits were similar to those observed for zwitterionic SUVs . Once again , the 12-AS transfer rate showed a statistically significant increase for rEgAgB8/2 , but not for rEgAgB8/3 when the SUV:protein ratio exhibited a 4-fold increase . This suggests that the transfer of 12-AS to phospholipid membranes by EgAgB8 apolipoproteins was not significantly modified by the increase in the negative charge of the vesicles caused by PS incorporation . In contrast , when CL-SUVs were used , 12-AS transfer rates of both EgAgB8 proteins were remarkably increased . Moreover , for both EgAgB8 subunits , the transfer rates reached higher values at higher SUV:protein ratios ( Fig . 8D ) , ranging from 0 . 43 ± 0 . 03 s−1 to 3 . 1 ± 0 . 6 s−1 for rEgAgB8/2 and from 0 . 9 ± 0 . 5 s−1 to 3 . 6 ± 0 . 6 s−1 for rEgAgB8/3 . These results indicate that the greater increase in the negative charge of SUVs caused by CL incorporation enhanced the ability of rEgAgB8/2 and rEgAgB8/3 to transfer their ligands to vesicles , via a collision-mediated mechanism . Overall , these results suggest that electrostatic interactions between EgAgB8 subunits and phospholipid membranes are of foremost importance for determining the rate at which these proteins can transfer their ligands . Nevertheless , an increase in the affinity of EgAgB8 subunits to CL-SUVs could not be ruled out . Furthermore , the greater ability of rEgAgB8/2 to transfer 12-AS to CL-SUV vesicles than rEgAgB8/3 ( p < 0 . 05 , S4 Fig . ) may be related to the negative and positive charge distribution in these proteins [16] . This report provides new approaches towards an understanding of cestodes HLBPs and how they may interact with phospholipid membranes to transfer their ligands . Since there are no reports for other HLBP members , EgAgB lipid transfer properties can only be compared to those of other LBP families [49–56] . As mentioned above , although the mechanisms employed by EgAgB8/2 and EgAgB8/3 to transfer 12-AS to EPC vesicles appear to be different , in both cases transfer rates are of the same order of magnitude . Previous studies have shown that LBPs that exchange ligands by collisional or diffusional processes also differ in the range of rates employed to transfer their ligands to acceptor membranes . For example , mammalian intestinal fatty acid binding protein ( IFABP ) and Schistosoma japonicum FABP ( Sj-FABPc ) , both employing collisional mechanisms , transfer their ligands at a higher rate compared to diffusional proteins such as liver FABP and ABA-1-A1 protein from A . suum [49 , 51] ) . Furthermore , for proteins that transfer ligands through a collision-mediated mechanism , transfer rates increase proportionally to vesicle concentration ( e . g . intestinal FABP , Sj-FABPc , EgFABP1 from E . granulosus or YLSCP2 from the yeast Yarrowia lipolytica [49 , 51 , 55 , 56] ) , and this is the case for EgAgB8/2 . On the other hand , diffusional proteins have not shown variations in transfer rates as vesicle concentration or vesicle composition changes ( e . g . , liver-FABP , ABA-1-A1 or Ov-FAR-1 protein from Onchocerca volvulus [49 , 51] ) . Remarkably , the EgAgB8/3 transfer rate of 12-AS does not change with increasing concentrations of EPC or PS-SUVs , but a significant increase was observed with CL-SUVs , suggesting EgAgB8/3 employs a mechanism that is different to those described so far . Overall , a comparison of our results with those reported for other LBPs suggests that while EgAgB8/2 behaves as a typical collisional protein , EgAgB8/3 does not , even compared to other α-helical proteins such as ABA-1-A1 , Ov-FAR-1 or YLSCP2 . Whether this is an exclusive feature of EgAgB8/3 subunit or is a conserved behaviour of certain subunits of other HLBPs , needs further investigation . Finally , our results showed that EgAgB8/2 and EgAgB8/3 subunits are able to deliver their cargo to phospholipid membranes , supporting the hypothesis that EgAgB is involved in lipid transport between parasite and host tissues [16] . Nevertheless , the capacity of EgAgB particles to transfer fatty acids to the parasite or to the host´s cells remains to be formally demonstrated . The small proteins that collectively form the large Antigen B complexes present in the hydatid cysts of Echinococcus spp . and the similar entities in the cysticerci of other highly pathogenic cestodes , comprise the most abundant proteins present in the fluids of these parasites . Their native structures and role in lipid dynamics of the parasites remain to be elucidated , a difficulty being that our understanding of how the various components of the large Antigen B complexes form , what ligands they bind , and how , is at best rudimentary . In particular , the separate or synergistic roles of the small protein isoforms and lipid components remain mysterious . The advance that we report here is to provide for the first time a method for the complete removal of lipids from single recombinant isoforms of the protein components without detectably compromising their structures or biochemical activities , and the demonstration that self-assembly of complexes does not absolutely depend on lipids . It still appears , however , that lipids may enhance the process for the formation of the large complexes found in the natural product . In the natural particles , the complexes comprise heterogeneous mixtures of several different isoforms of the EgAgB proteins , with different types of lipids [15] . We have therefore provided the basis for the analysis of self-assembly that should eventually permit elucidation of potentially cooperative interactions of EgAgB isoforms and lipids . In early studies it has been suggested that EgAgB subunits are elongated and amphipathic molecules which could form multimeric structures thermodynamically more stable than individual monomers [33] . Recently , the prediction of the tertiary structure of EgAgB subunits suggests that the position of hydrophilic and hydrophobic amino acids defines pocket-like regions where hydrophobic ligands could interact with the proteins , and a partial charge distribution showing a plausible electrostatic profile for molecular oligomerization [16] , in concordance with the experimental data we obtained in this work . Based on the information obtained from molecular modelling studies we can now begin to identify the motifs in EgAgB apolipoproteins that are involved in oligomerization or ligand binding . The developments we report also allowed us to address the question of how EgAgB complexes may interact with cell membranes of the parasite in order to acquire , transfer and deliver lipids within the parasites . We found that they can deliver their cargo to phospholipid membranes through direct physical interaction with them , and that the charge composition of the membranes is crucial to this process . The way is now open to examine such processes using parasite cell lines or miniature hydatid cysts which are now available [64–67] . It has been more than 40 years since EgAgB was described as an abundant lipoprotein in the hydatid cyst fluid [13] , and it has been successfully employed as an Echinococcus genus-specific target antigen for human serodiagnosis [19–22] . We know that lipids , such as cholesterol and fatty acids , are essential for E . granulosus s . l . [18] and that EgAgB is able to bind them in vivo [15] . Thus , EgAgB’s unusual construction and its participation in lipid uptake and delivery , together with the fact that it has no homologue in other animal phyla , places it in an excellent position to be seriously considered for therapeutic intervention . Our findings on EgAgB’s unusual lipid-binding , self-assembly , and membrane interaction properties therefore potentially present new avenues for drug developments that could disable a range of physiological processes essential to the parasite’s establishment and survival such as membrane construction and lipid-based signalling systems . | Echinococcus granulosus is a causative agent of hydatidosis , a parasitic disease that affects humans and livestock with significant economic and public health impact worldwide . Antigen B ( EgAgB ) , an abundant product of E . granulosus larvae , is a lipoprotein that carries a wide variety of lipids , including fatty acids and cholesterol . As E . granulosus is unable to synthesize these lipids , EgAgB likely plays an important role in parasite metabolism , participating in both the acquisition of host lipids and their distribution between parasite tissues . The protein component of EgAgB consists of 8 kDa subunits encoded by separate genes . However , the biochemical properties of EgAgB subunits , particularly their ability to bind and transfer lipids , are poorly known . Herein , using in vitro assays , we found that EgAgB subunits were capable of oligomerizing in the absence of lipids and to bind fatty acids , but not cholesterol . Moreover , EgAgB subunits showed the ability to transfer fatty acids to artificial phospholipid membranes . These results indicate new points of attack at which the parasite might be vulnerable to drugs . | [
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] | [] | 2015 | Lipid-Free Antigen B Subunits from Echinococcus granulosus: Oligomerization, Ligand Binding, and Membrane Interaction Properties |
Spike-timing dependent plasticity ( STDP ) is a widespread plasticity mechanism in the nervous system . The simplest description of STDP only takes into account pairs of pre- and postsynaptic spikes , with potentiation of the synapse when a presynaptic spike precedes a postsynaptic spike and depression otherwise . In light of experiments that explored a variety of spike patterns , the pair-based STDP model has been augmented to account for multiple pre- and postsynaptic spike interactions . As a result , a number of different “multi-spike” STDP models have been proposed based on different experimental observations . The behavior of these models at the population level is crucial for understanding mechanisms of learning and memory . The challenging balance between the stability of a population of synapses and their competitive modification is well studied for pair-based models , but it has not yet been fully analyzed for multi-spike models . Here , we address this issue through numerical simulations of an integrate-and-fire model neuron with excitatory synapses subject to STDP described by three different proposed multi-spike models . We also analytically calculate average synaptic changes and fluctuations about these averages . Our results indicate that the different multi-spike models behave quite differently at the population level . Although each model can produce synaptic competition in certain parameter regions , none of them induces synaptic competition with its originally fitted parameters . The dichotomy between synaptic stability and Hebbian competition , which is well characterized for pair-based STDP models , persists in multi-spike models . However , anti-Hebbian competition can coexist with synaptic stability in some models . We propose that the collective behavior of synaptic plasticity models at the population level should be used as an additional guideline in applying phenomenological models based on observations of single synapses .
Spike-timing dependent plasticity ( STDP ) is a form of activity-dependent synaptic plasticity that appears throughout the nervous system [1 , 2 , 3] . In STDP , pairs of pre- and postsynaptic action potentials potentiate a synapse when the presynaptic spike precedes the postsynaptic spike , and depress it for the reverse order [4 , 5] . However , when multiple pre- and postsynaptic spikes occur across a synapse over a short interval of time , the resulting plasticity depends on their timing in a more complex manner . For example , pair-based STDP models predict that “pre-post-pre” and “post-pre-post” triplets of spikes with the same pairwise intervals should induce the same plasticity , but experiments indicate that these two triplet patterns have different effects [6 , 7] . This and similar contradictions motivated the development of multi-spike models of STDP , which go beyond pairwise interactions of pre- and postsynaptic spikes ( see [8] for a review ) . Here , we analyze three such models that are based on experimental results to determine how they affect populations of synapses converging onto a postsynaptic neuron . We focus on two basic features: stability and competition . Stability is a property of the distribution of synaptic weights arising from an STDP model , and we will distinguish three cases: unstable , partially stable , and stable . In the unstable case , synaptic weights perpetually increase under STDP , unless some upper limit is imposed ( in principle , weights could also decrease perpetually , but this is atypical ) . Although we briefly consider soft bounds to limit the range of synaptic weights , we primarily consider hard bounds . When hard limits are imposed on an unstable STDP model , the synaptic weights cluster tightly against the upper bound . Another more interesting case is partial stability , in which individual synaptic weights increase or decrease indefinitely , but the average of the weights across a synaptic population stays fixed . When hard bounds are imposed on a partially stable STDP model to limit the increases and decreases of individual synapses , the synaptic weights tend to cluster at either end of their allowed range , forming a U-shaped distribution [9] . Finally , when an STDP model is stable , no hard bounds need to be imposed , and synaptic weights form a unimodel distribution [10 , 11 , 12 , 13] . We are interested in determining whether , and under what parameter values , different multi-spike STDP models lead to stable , partially stable or unstable synaptic weight distributions . The impact of STDP on the weights of synapses onto a postsynaptic neuron depends on correlations between their presynaptic spike patterns . This can be studied by dividing the inputs to a neuron into two groups , one with correlated presynaptic activity and the other with uncorrelated presynaptic spiking . In this context , competition refers to the propensity of either a correlated or an uncorrelated group of synapses to gain control of the postsynaptic spiking , while the other group become less influential , both as a consequence of STDP . In cases that we call “Hebbian” , the synapses with correlated input become stronger than those with uncorrelated input . In other “anti-Hebbian” cases , the reverse occurs and the correlated synapses become weaker than the uncorrelated . We are interested in whether synaptic plasticity is Hebbian or anti-Hebbian , by this definition , in various multi-spike STDP models and for different parameter values of those models . The three multi-spike STDP models that we consider were proposed on the basis of different experimental results . In the “suppression model” , inspired by experimental results in cortical slices , the plasticity-inducing effect of each pre- or postsynaptic spike is suppressed by preceding spikes [6 , 14] . The “triplet model” , inspired by experiments in hippocampal slices , includes the effect of neighboring pre-post pairings as well as depression exerted by preceding presynaptic spikes and potentiation by preceding postsynaptic spikes [15] . The third model we consider , the “NMDAR-based model” , is phenomenologically based on the kinetics of the N-Methyl-D-Aspartate receptor [16] . This model was proposed before experimental results on multi-spike effects in STDP were available , and hence it was not explicitly aimed at accounting for multi-spike interactions , unlike the first two models . However , as we show in the Results section , it demonstrates a rich repertoire of multi-spike interactions , and can behave similar to either of the first two models depending on its parameters . Therefore , we feel it deserves to be considered as a multi-spike STDP model , even though it may not have been intended as such initially . We begin by reviewing results for pair-based STDP to establish our approach and introduce ways of characterizing the effects of plasticity . We then apply this approach and these characterizations to the multi-spike STDP models . For each case , we first consider the parameters originally proposed for the model , and then systematically explore a range of parameter values to evaluate stability and competition . In light of the results obtained , we conclude by discussing relationships between the models at the biophysical level , and the computational implications of each model at the synaptic population level .
To explain our method for analyzing synaptic stability and competition and also to provide a benchmark of comparison for the multi-spike STDP models , we first examine a pair-based STDP model . In this model , synapses are modified only on the basis of the intervals between pairs of pre- and postsynaptic spikes . When a synapse receives a larger ensemble of spikes , such as triplets or quadruplets , plasticity is induced by the pre-post pairs within the ensemble independent of the higher-order structure of the ensemble . As stated in the introduction , similarly spaced “pre-post-pre” and “post-pre-post” triplets induce the same amount of synaptic modification in this model ( Fig 1A and 1E ) . The parameters of the pair-based model include the maximum amounts by which synapses can be potentiated or depressed , A+ and A− , and the time constants for the potentiation and depression windows , τ+ and τ− ( Eq 10 ) . These parameters also appear in the multi-spike models . To quantify the average modification of a synapse under STDP , we first calculate the probability of a pairing interval Δt for spikes arriving at the synapse and then average synaptic modification ( Eq 10 ) over that probability . We assume that the pre- and postsynaptic spike trains are both Poisson . The rate of the presynaptic spike train takes the constant value rpre . The baseline rate of postsynaptic firing is denoted by rpost ( Eq 8 ) . When a postsynaptic action potential is generated , presynaptic spikes are equally likely to arrive at any later time because the postsynaptic spike has no effect on presynaptic activity . However , when a presynaptic spike arrives at a particular synapse , it transiently increases the postsynaptic firing rate by an amount proportional to the strength of that synapse ( Eq 9 ) . As a result , a postsynaptic action potential is more likely to be induced shortly after the arrival of a presynaptic spike . Including both the baseline rate and this brief enhancement , the average synaptic modification or the “drift” for a synaptic strength w is ( see Methods ) dwdt= ( A+τ+−A−τ− ) rprer¯post+A+τ+τsrprew ( τs+τ+ ) ( Vth−Vr ) τm . ( 1 ) The first term in this equation relates the change in synaptic strength of a particular synapse , w , to the average strength of all the excitatory synapses , ⟨w⟩ , through the dependence of the baseline firing rate r¯post on this average . This term is the same for all synapses , so we call it the “baseline drift” . The second term depends on the synaptic strength of the particular synapse being considered , and it arises from the transient increase of postsynaptic firing rate following a presynaptic spike at this synapse . We call it the “w-dependent drift” . The rate of change of the average of all the excitatory synaptic weights is given by the sum of the baseline drift and the average of the w-dependent drift , d⟨w⟩dt= ( A+τ+−A−τ− ) rprer¯post+A+τ+τsrpre⟨w⟩ ( τs+τ+ ) ( Vth−Vr ) τm . ( 2 ) The average synaptic strength in the steady-state is the values of ⟨w⟩ that sets the right side of Eq ( 2 ) to zero ( i . e . a fixed point ) . One such fixed point occurs when all the synapses are zero ( ⟨w⟩ = 0 ) . This makes the postsynaptic neuron silent ( r¯post=0 ) and sets both the baseline and average w-dependent drifts to zero . This state is uninteresting and simply reflects the fact that no plasticity occurs when the postsynaptic neuron is silent . If the synaptic strengths are not zero , the average w-dependent drift is always positive because presynaptic spikes always enhance postsynaptic firing . As a result , a nontrivial fixed point for the average synaptic weight can occur only if the baseline drift is negative ( A− τ− > A+ τ+ ) so that it can cancel the w-dependent drift ( Fig 1B , closed circle ) . This fixed point is stable , because the positive w-dependent drift dominates if the average weight is smaller that the fixed-point value , and the negative baseline drift dominates if it is larger . Mathematically , stability requires the slope of the average drift to be negative at the fixed point ( Fig 1B ) , which always holds for the nontrivial fixed point of the pair-based model . In summary , the steady-state average synaptic strength in pair-based STDP has a stable nontrivial mean if the depression window is larger than the potentiation window ( A− τ− > A+ τ+ ) . This fixed point is unique , so the mean of the steady-state distribution of synaptic weights converges to this value regardless of its initial value . The stability of the mean is not a sufficient condition for the steady-state distribution of synaptic strengths to be fully stable , each synapse must also have a stable deviation from the mean . The strength of a particular synapse can be expressed as w = ⟨w⟩ + δw , where δw is the deviation of the synapse from the mean . If the deviation tends to grow over time , the synapses will drift away from the mean and the distribution will be partially stable and U-shaped ( bimodal ) . If the deviation tends to decrease , the synapses will cluster around the mean and the distribution will be stable and unimodal . Assuming that the mean synaptic strength is at steady-state and that the deviation of an individual synapse ( out of a few thousand ) does not alter the mean significantly , the change of the deviation over time is governed solely by the w-dependent drift and can be derived from Eq ( 1 ) as dδwdt=A+τ+τsrpreδw ( τs+τ+ ) ( Vth−Vr ) τm . ( 3 ) Because the coefficient of δw in this equation is positive ( Fig 1B , inset ) , the deviations tends to grow , and the final distribution of synaptic strengths for pair-based STDP is partially stable and U-shaped even though the mean is stable ( Fig 1C , ref . [9] ) . To check the accuracy of Eqs ( 2 ) and ( 3 ) , we computed the synaptic drift by averaging the amount of induced synaptic modification in simulations lasting 03 s of simulated time , without implementing synaptic modification ( Fig 1B , gray shade ) . In general , the agreement is good; the discrepancy between the analytic and simulation results at low average synaptic strengths is due to the fact that our approximation for the transient postsynaptic firing rate ( Eq 9 ) is only accurate when the mean excitatory input is significantly larger than the mean inhibitory input . In the parameter regime where the potentiation window is larger than the depression window ( Fig 1E–1H ) , the mean synaptic weight only has the trivial and unstable zero fixed point ( Fig 1F ) , so the distribution is unstable and all of the synaptic strengths grow until they hit the upper bound , regardless of their initial values ( Fig 1G ) . When the mean synaptic strength is stable and the w-dependent drift is positive , it is possible for STDP to discriminate between two groups of synapses based on the degree of correlation in their presynaptic spike trains . If the spike trains arriving at one group of synapses are correlated and those of the other synapses are not , the correlated group induces a larger transient increase in the postsynaptic firing rate and hence a larger w-dependent drift . Therefore the correlated group is more likely to become stronger than the mean , and the uncorrelated group tends to become weaker to maintain the balance around the mean ( Fig 1D ) . This results in a Hebbian competition among the synapses [9] . On the other hand , when there is no stable mean , all the synapses tend to grow regardless of their correlation and no competition takes place , although the correlated synapses still end up stronger than the uncorrelated group ( Fig 1H ) . Therefore , the condition for Hebbian competition through pair-based STDP is the existence of a stable mean , i . e . A− τ− > A+ τ+ , which is equivalent to partial stability . Importantly , as long as the steady-state mean of the synaptic strength is within the allowed range , this condition is not changed by modifying the lower or upper bounds of the synaptic strengths . This is also the case for the multi-spike models discussed in the following sections . Experimental results on synapses in hippocampal cultures reveal a marked asymmetry in the plasticity induced by post-pre-post and pre-post-pre spike sequences , in contrast to the predictions of the pair-based model ( Fig 1A and 1E ) . Post-pre-post sequences induce potentiation , and pre-post-pre has little or no effect [7] . In addition , in experiments on cortical synapses , the balance between potentiation and depression shifts toward potentiation when the frequency of pre-post pairing events increases , another property not captured by pair-based STDP [17] . These results motivated Pfister & Gerstner [15] to propose the triplet model , which takes into account interactions of spikes beyond pre-post pairings . In addition to the effect of pre-post pairings , the triplet model includes additional depression due to previous presynaptic spikes and additional potentiation from earlier postsynaptic spikes ( Fig 2A ) . This is accomplished through a presynaptic depression variable and a postsynaptic potentiation variable assigned to each synapse ( Eq 11 ) . In the absence of incoming presynaptic spikes , the presynaptic depression variable decays exponentially with time constant τpre . Likewise , the value of postsynaptic potentiation variable decreases exponentially in the absence of postsynaptic spikes with time constant τpost . When a presynaptic spike reaches the synapse , the presynaptic depression variable abruptly increases by the amount Apre , and when a postsynaptic spike occurs , the postsynaptic variable increases by Apost ( Eq 12 ) . This is how the triplet model accounts for the asymmetry of synaptic modification in response to triplets . For a pre-post-pre triplet , the first presynaptic spike induces extra depression on the synapse , while for a post-pre-post triplet the first postsynaptic spike induces extra potentiation ( Fig 2B ) . The triplet model that we consider sums the contributions of all previous pre- and postsynaptic spikes as well as all pre-post pairings ( all-to-all ) . Pfister & Gerstner [15] also provided a version of the triplet model based only on nearest neighboring spikes , but the qualitative behavior of both versions is similar . As we did for pair-based STDP , we can derive equations governing the evolution of the mean synaptic strengths and deviations around the mean for individual synaptic weights . The average values of the presynaptic depression and postsynaptic potentiation variables , obtained from substituting rates for spikes in Eq 12 , are Apre τpre rpre and Apostτpostr¯post . Using these values and averaging the synaptic modification ( Eq 11 ) over the probability of pre-post pairings , the drift of the mean of the synaptic weights in the triplet model is d⟨w⟩dt=A+τ+rprer¯post+Apostτpostτ+rprer¯post2−A−τ−rprer¯post−Apreτpreτ−rpre2r¯post+ ( A++Apostτpostr¯post ) τ+τsrpre⟨w⟩ ( τs+τ+ ) ( Vth−Vr ) τm . ( 4 ) As in the pair-based model , the last term in this equation is the w-dependent drift and the other terms make up the baseline drift . The dynamics of deviations of individual synapses from the mean is governed by the w-dependent drift , so dδwdt= ( A++Apostτpostr¯post ) τ+τsrpreδw ( τs+τ+ ) ( Vth−Vr ) τm . ( 5 ) As in the pair-based model , the coefficient of δw is always positive , so individual weights will drift away from the mean for any choice of parameters , making individual synaptic weights unstable . The parameters of the original model were fit by Pfister & Gerstner [15] separately to match experimental data from hippocampal cultures and cortical slices , resulting in two sets of parameters . Our simulation results indicate that , for both sets of parameters , the distribution of synaptic weights is unstable , so that all the synaptic weights cluster around the upper bound . In addition , no competition takes place between correlated and uncorrelated synapses with these parameter sets . This led us to consider properties of the triplet model for a range of parameter values . As in our discussion of pair-based STDP , we study the triplet model when pair-based potentiation is larger than pair-based depression ( A− = 0 . 005 mV , A+ = 1 . 01A− ) and when pair-based depression is larger than pair-based potentiation ( A+ = 0 . 005 mV , A− = 1 . 01A+ ) . In each case , we varied the ratio between postsynaptic potentiation and presynaptic depression ( Apost/Apre ) systematically , while keeping Apre constant at 0 . 001 mV . We first examine the fixed points of the mean synaptic weight ( Fig 3A and 3F ) . When Apost/Apre is small , the average synaptic weight has two nontrivial fixed points ( Fig 3B and 3G ) . The first is stable ( Fig 3B and 3G , filled circle ) and the second is unstable ( Fig 3B and 3G , open circle ) . The appearance of the unstable fixed point in the triplet model is due to the dependence of the postsynaptic potentiation on the postsynaptic firing rate . This added potentiation increases when the mean synaptic weight increases , eventually overcoming the combined effect of presynaptic and pair-based depression . The existence of two fixed points makes the steady-state distribution of synaptic weights sensitive to the initial distribution . If the mean of the initial distribution is greater than the unstable fixed point , the distribution will be unstable and all of the weights will be pushed toward the upper bound ( Fig 3D and 3I , right ) . If the mean of the initial distribution is lower than the unstable fixed point , the mean of the steady-state distribution converges to the stable fixed point and individual weights drift away from the mean toward the lower and upper bounds , resulting in partial stability and a U-shaped distribution similar to the pair-based model ( Figs 3D and 2I , left ) . When Apost/Apre reaches a critical value , the two fixed points coalesce and annihilate each other , and only the trivial unstable fixed point remains ( Fig 3C and 3H ) . In this case , regardless of the initial distribution , the final distribution is unstable and tightly clustered near the upper bound ( Fig 3D and 3I , bottom ) . As we argued in the case of the pair-based model , synaptic competition can only take place when the steady-state distribution has a nontrivial stable mean and is partially stable . In the triplet model , when Apost/Apre is relatively small and the initial mean synaptic weight is lower than the unstable fixed point , this condition is fulfilled ( Fig 3A and 3F , dark gray areas; Fig 3E , left ) . However , if the stable and unstable fixed points are too close together , there is no guarantee of synaptic competition ( Fig 3J , left ) . The reason for this is that when a subset of the synaptic inputs are correlated , presynaptic spikes tend to arrive in tandem and induce large transients in the postsynaptic firing rate , causing large fluctuations in the mean synaptic weight . This can cause the mean synaptic strength to fluctuate beyond the unstable fixed point , destabilizing the weight distribution . As a result , the parameter regime for synaptic competition in the triplet model is highly restricted to the region of small Apost/Apre with A+ < A− ( Fig 3A , dark gray ) . Even within this small region , if the correlation coefficient among the correlated synapses is high , competition does not take place and all the synapses tend to the upper bound ( S1 Fig ) , due to the fluctuations mentioned above . Thus , it is not surprising that the original parameters obtained by Pfister & Gerstner [15] did not lead to competitive synaptic plasticity . In summary , the novel properties of the triplet model , as compared to the pair-based model , are the sensitivity to the initial distribution of weights and a tighter parameter range for Hebbian competition . Plasticity experiments in cortical slices using triplets of spikes showed different effects than the hippocampal results . In the synapses of the visual cortex of rats , pre-post-pre triplets induce potentiation whereas post-pre-post triplets induce depression [6] . These results led Froemke et al . [6] to propose the suppression model , in which plasticity is induce by nearest neighbor pre- and postsynaptic spikes . The plasticity is computed from the standard pair-based STDP curve , but the effect of the presynaptic spike in each pair is suppressed by previous presynaptic spikes and , similarly , the plasticity induced by the postsynaptic spike in each pair is suppressed by previous postsynaptic spikes ( Fig 4A ) . The suppression is maximal immediately after each pre- or postsynaptic spike , and it decreases exponentially as the interval between consecutive pre- or postsynaptic spike increases ( Eq 13 ) . The suppression accounts for the asymmetry of synaptic modification in response to triplets . In the case of a pre-post-pre triplet , the first pair ( pre-post ) induces potentiation , but the amount of depression induced by the second pair ( post-pre ) is suppressed by the first presynaptic spike . For a post-pre-post triplet , the first pair ( post-pre ) induces depression , but the potentiation induced by the second pair ( pre-post ) is suppressed by the first postsynaptic spike ( Fig 4B ) . The parameters of the model were originally set to match the synaptic modification seen in the experiments ( ref . [6]; Table 2 ) . Our numerical simulations with these parameters show that the steady-state distribution is unstable and tightly clustered around the upper bound . When correlations are induced in half of the synaptic inputs , no competition takes place and all the weights are potentiated indiscriminately . To observe a range of behaviors of this model , we set the suppression time constants equal to the values given by Froemke et al . [6] , namely τpre = 28 ms , τpost = 88 ms . We also set the maximum potentiation and depression values equal ( A+ = A− = 0 . 005 mV and fixed the depression time constant ( τ− = 20 ms ) . We then varied the potentiation time constant τ+ to observe different behaviors of the model . Transitions to different behaviors can also be seen when changing other parameters ( for example the ratio A+/A− ) , but our simulations showed that changing the ratio between the potentiation and depression time constants ( τ+/τ− ) reveals these transitions most clearly . Calculating the drift of synapses in the suppression model is more complicated than in the models considered above . We leave the details to S2 Appendix and report the results here . When τ+/τ− < 1 . 2 , the average synaptic weight has a stable nontrivial fixed point ( Fig 5A–5C ) . For higher values of τ+/τ− , the nontrivial fixed point disappears and the average synaptic weigh has only the trivial zero fixed point ( Fig 5A and 5D ) . For low τ+/τ− values , the steady-state distribution of weights is partially stable and U-shaped , as in the case of the pair-based model ( Fig 5E ) . However , for τ+/τ− between 1 . 05 and 1 . 2 , the value of the average synaptic weight grows rapidly ( Fig 5A , gray area ) , and the steady-state distribution is stable and unimodal ( Fig 5F ) , implying that the w-dependent drift is negative in this range . Because of the complexity of spike interaction in the suppression model , a complete characterization of the w-dependent drift is beyond our analytical calculations ( S2 Appendix ) . However , features of the response of an integrate-and-fire neuron to a pair of presynaptic spikes in the context of the suppression model explain why the w-dependent drift becomes negative when the average synaptic weight is large . Suppose that two presynaptic spikes arrive at a neuron in quick succession , and we want to analyze the role of the second spike in inducing plasticity under the suppression model ( Fig 6 ) . The second presynaptic spike participates in plasticity twice: once by pairing with the previous postsynaptic spike , and again by pairing with the next postsynaptic spike . When the strength of the synapse is low , the first presynaptic spike is not very likely to induce a postsynaptic action potential after its arrival , so the pairing interval between the second presynaptic spike and the preceding postsynaptic spike is typically long , which induce weak depression ( Fig 6A ) . However , if the synapse is strong , the first presynaptic spike is likely to induce a postsynaptic action potential , and its pairing interval with the second presynaptic spike is then short , inducing strong depression ( Fig 6B ) . In addition , because of the high probability of postsynaptic firing in response to both presynaptic spikes , the interval between the induced postsynaptic spikes is short , which strongly suppresses the potentiation caused by pairing the second presynaptic spike with its following postsynaptic spike . Therefore , depression dominates over potentiation in the suppression model when synapses are strong . When this happens , deviations to even higher values lead to depression . This explains why w-dependent drift is negative when the average synaptic weight is large , which occurs when τ+/τ− approaches the critical value 1 . 2 ( Fig 5A , gray area ) . When half of the synapses receive correlated spike trains and the other half uncorrelated inputs , a distinctive features of the suppression model is that anti-Hebbian competition takes place: the uncorrelated synapses become strong and the correlated ones weak ( Fig 5H–5J ) . This is the result of postsynaptic suppression . When correlated presynaptic spikes arrive , they tend to induce a postsynaptic spike shortly after their arrival . This makes the interval between the induced postsynaptic action potential and the previous spike shorter than for the postsynaptic response to uncorrelated input . As a result , potentiation is suppressed for correlated synapses , and they eventually lose the competition with uncorrelated ones . In analogy with what was described in the previous paragraph , correlated inputs are similar to inputs with strong synapses and , in either case , the high probability of postsynaptic spiking makes the w-dependent drift negative . In summary , the characteristic properties of suppression model are anti-Hebbian competition and stability of the synaptic distribution when the mean synaptic strength is large . The NMDAR-based model [16] was proposed as an explanation for the original STDP experiments of Markram et al . [4] , and it predates both the triplet and suppression models and the data that inspired them . Nevertheless , as we will see below , it has features that resemble both of these models , and it is sensitive to spike interactions beyond pre-post pairings . The original version of the NMDAR-based model [16] includes the dynamics of the probability of presynaptic vesicle release . We focus on a simpler version that only models the modification of synaptic strengths by pre- and postsynaptic spikes [18] . In the NMDAR-based model , the NMDAR is assumed to have three states , rest , up and down . Each incoming presynaptic spike moves a portion of the NMDARs in the rest state into the up state , and each postsynaptic spike transitions a portion of the rest-state NMDARs into the down state . The NMDAR decays back to the rest state exponentially in the absence of spikes ( E 14 ) . In accord with the molecular kinetics of NMDARs [19 , 20] , the rest state can be interpreted as an NMDAR that is not bound to glutamate and is blocked by Mg2+ , the up state as an NMDAR that is bound to glutamate but blocked by Mg2+ , and the down state as an NMDAR that is not bound to glutamate but has had its Mg2+ block removed by a postsynaptic spike . The model also has two second messengers , called “up” and “down” messengers , which mediate potentiation and depression , respectively . These can be in either active or inactive states . When a presynaptic spike arrives , a fraction of the inactive down messengers transition to the active state . Likewise , when a postsynaptic spike reaches the synapse , it moves a portion of the inactive up messengers into their active state . The messengers decay back to their inactive states in the absence of spikes ( Eq 15 ) . Finally , upon arrival of a presynaptic spike , the synapse is depressed proportional to the amount of active down messenger , provided that this is larger than a threshold θdn . Similarly , each postsynaptic spike causes the synapse to potentiate proportional to the amount of active up messenger provided that it is larger than a threshold θup ( Eq 16 ) . Thus , the presynaptic spike plays three roles in this model: it moves resting NMDARs into the up state , it activates the down messenger , and it induces depression . The postsynaptic spike also has three roles: it transitions resting NMDARs into the down state , activates the up messenger , and induces potentiation ( Fig 7A ) . A key feature of the NMDAR based model is that preceding spikes decease the amount of available resting NMDARs available to upcoming spikes . This implements a mechanism akin to the suppression model , in which previous spikes suppress the effect of subsequent spikes . The roles of the second messengers are quite similar to those of the presynaptic depression and postsynaptic potentiation variables in the triplet model in that both integrate the effects of pre- and postsynaptic spiking to modify depression and potentiation . In fact , if we assume that the spikes have access to an unlimited pool of resting NMDARs and messengers , the NMDAR-based model is equivalent to the triplet model . Given the multi-spike interactions in the NMDAR-based model , it is not surprising that it responds asymmetrically to triplets of spikes . Our numerical simulations using the parameters provided by Senn et al . [16] ( Table 2 ) show that the synaptic modification in response to triplets in this model is qualitatively similar to that of the suppression model ( Fig 7B ) . The simulations also show that , with the parameters provided by Senn et al . [16] , the steady-state distribution is unstable and tightly clustered around the upper bound . When correlations are induced in half of the synaptic inputs , no competition takes place and all the weights are potentiated indiscriminately . To examine the spectrum of behaviors in the NMDAR-based model , we calculated the synaptic drift ( S3 Appendix ) . Interesting transitions into different regimes occur when the threshold of the up messenger is larger than that of the down messenger ( θdn = 0 . 2 , θup = 0 ) , and the ratio between maximum potentiation and maximum depression ( A+/A− ) is varied ( Fig 8 ) . All other parameters of the model are held constant at equal values for potentiation and depression components , and the time constants are set to the values provided by Senn et al . [16] ( Table 2 ) . When A+/A− is smaller than a critical value ( 0 . 042 ) , the average synaptic weight has both stable and unstable nontrivial fixed points . At the critical value , these two fixed points coalesce and disappear , and beyond the critical value the average synaptic weight has only the trivial fixed point at zero ( Fig 8A ) . The sign of w-dependent drift also changes as A+/A− varies . When A+/A− is smaller than 0 . 025 , the w-dependent drift is negative , and for larger ratios it is positive ( Fig 8B ) . Taken together , three different behaviors are observed in the NMDAR-based model: 1 ) When a stable mean synaptic weight exists and w-dependent drift is negative ( 0 < A+/A− < 0 . 025 , Fig 8A–8B , dark gray area ) , the steady-state distribution of synaptic weights is stable and unimodal ( Fig 8C and 8F ) . 2 ) When a stable mean synaptic weight exists and w-dependent drift is positive ( 0 < A+/A− < 0 . 042 , Fig 8A and 8B , light gray area ) , the steady-state distribution of synaptic weights is partially stable and U-shaped ( Fig 8D and 8G ) . 3 ) When the mean synaptic weight has no stable fixed point ( A+/A− > 0 . 042 ) , the steady state distribution is unstable , and it clusters near the upper bound ( Fig 8E and 8H ) . Synaptic competition is different in these three regions of the parameter space . When half of the input spike trains are correlated , the competition in the first region is anti-Hebbian because the w-dependent drift is negative and correlated synapses receive more depression ( Fig 8I ) . It is Hebbian in the second region because the w-dependent drift is positive ( Fig 8J ) . As in the triplet model , the closeness of the stable and unstable fixed points in this region makes synaptic competition elusive , such that when the correlation coefficient between the synapses is high , all the synapses tend to the upper bound and no competition takes place ( S1 Fig ) . There is no competition in the third region because the mean is not stable ( Fig 8K ) . In short , the distinguishing features of the NMDAR-based model compared to the pair-based model are the possibility of a stable synaptic distribution and anti-Hebbian competition when the maximum depression is significantly larger than the maximum potentiation . In previous sections , we imposed so-called hard bounds on the synaptic strengths to confine them between zero and a maximum allowed value ( wmax ) . It is also possible to confine the synapse by implementing soft bounds , that is , by making the maximum depression and potentiation weight-dependent so that when a synaptic strength approaches the bounds , its rate of change gradually decreases . This can be done by multiplying A+ and A− by 1 − w/wmax and w/wmax respectively . In the case of the triplet model , the presynaptic depression and postsynaptic potentiation variables should be also multiplied by 1 − w/wmax and w/wmax respectively , because they appear as potentiation and depression factors as well . The steady-state distribution of synaptic strengths is stable and unimodal for all three multi-spike STDP models with soft bounds ( Fig 9 ) . This behavior is robust and holds for a wide range of parameters ( we only show simulation results for the original parameters ( Table 2 ) in each model ) . The soft bounds weaken synaptic competition drastically , so than the distributions of correlated and uncorrelated synapses are close to each other ( Fig 9 , insets ) . As has been shown for pair-based STDP [10 , 11] , soft bounds turn STDP into a homeostatic plasticity mechanism with minimal sensitivity to the correlation structure of the external input .
The main focus of this study has been on synaptic stability and competition , two desirable but often conflicting features of activity-dependent plasticity rules [21] . Our analytical tool for assessing these properties was calculating the drift of a population of synapses under each multi-spike STDP model . This method has been applied to the pair-based STDP model in a number of previous studies . The pair-based model with hard bounds was shown to produce a partially stable U-shaped steady-state distribution of weights and Hebbian competition that favors correlated synapses over uncorrelated ones [9 , 22 , 23] . On the other hand , the pair-base model with soft bounds has been shown to have a stable steady-state distribution at the expense of losing synaptic competition and sensitivity to input correlations [10 , 11] . Our analysis can be viewed as a reconfirmation of these results of the pair-based models and an extension into the domain of multi-spike STDP models . Our goal has not been to identify a superior model among the different options . Rather , we have highlighted the largely overlooked consequences of implementing these models at the population level . Table 3 summarizes the results of our survey of stability and competition in multi-spike STDP models . Like the pair-based model , the triplet model produces a partially stable steady-state distribution of synaptic weights and Hebbian synaptic competition . However , competition is observed only for a limited range of its parameters . The suppression model shows predominantly anti-Hebbian competition and a stable steady-state distribution of synaptic weights when the average weight is high . The NMDAR-based model displays both stable and partially stable steady-state distributions depending on the parameters , with anti-Hebbian competition in the former case and Hebbian in the latter . Our results indicate that the dichotomy between stability and Hebbian competition , which is well characterized for pair-based STDP models , persists in multi-spike STDP models . However , anti-Hebbian competition can coexist with full synaptic stability for at least some parameter regimes in the suppression and NMDAR-based models . Conflict exists between stability and Hebbian competition because , for such competition to take place , correlated synapses , which induce a large transient increase in postsynaptic firing rate , should be strengthened . This property undermines full stability of the synaptic distribution because it creates a positive feedback loop in which strong synapses , which also induce large transient increases in postsynaptic firing , become even stronger . Thus , it is not surprising that this conflict persists in more elaborate multi-spike models . Anti-Hebbian competition , on the other hand , involves weakening of correlated synapses that induce large transients in the postsynaptic activity , and so should be compatible with full stability of the synaptic distribution . The dichotomy between synaptic competition and stability is a specific form of the general stability/plasticity dilemma [24] . Every form of plasticity faces the challenge of maintaining the balance between forming new memories through modification of synaptic strengths , and preserving old synaptic configurations to maintain old memories . A more specific aspect of this challenge is that Hebbian ( or anti-Hebbian ) competition among synapses in a network is a powerful mechanism for shaping and modifying neural activity based on the properties of the inputs to the network . However , unless changes in synaptic strength are stabilized appropriately , the level of activity in a neural circuit can grow or shrink in an uncontrolled way [25] . Therefore , it is highly desirable , from a computational point of view , to find a biologically plausible model that reconciles synaptic stability with competition . A number of solutions have been proposed for harmonizing stability and competition in pair-based STDP . One solution is interpolating between hard and soft bounds to obtain a middle ground that can harbor both synaptic competition and stability , which is obtained over a limited parameter range [12] . Another solution , based on a small temporal shift in the STDP window , can stabilize the distribution of synaptic weights while maintaining competitiveness [13] . This shift has a similar effect in the triplet model [13] . To search the parameter space of the models for different stability/plasticity interplays , we systematically varied the balance between potentiation and depression parameters in each multi-spike STDP model . However , for each model , a fixed set of parameters was originally proposed to match experimental results . Our parameter changes may cause the response profile of the model to deviate from its originally fitted form . This can be justified because both the temporal spread and the magnitude of potentiation and depression vary considerably as a function of the location of a synapse [14 , 26 , 27] . Therefore , each parameter set in our analyses and numerical simulations could coincide with the characteristics of the STDP window at a particular location on the dendritic tree . Although all of the models we considered were proposed on the basis of experimental observations of synaptic modification , their effect on a population of synapses onto a postsynaptic neuron can be quite different . As mentioned above , one useful computational aspect of STDP is its ability to implement Hebbian learning and to functionally organize neural circuits . None of the three multi-spike models generated Hebbian competition when the original fitted parameters were used . Moreover , using these parameters , all three models produced an unstable distribution of weights tightly clustered near the upper bound of their allowed range . Given the observed broad distribution of synaptic weights in vitro [28 , 29 , 30] this is implausible . As it is possible to construct several phenomenological models that explain a given experimental data set , it seems reasonable to use the effects of plasticity at the population level ( evaluated through simulations or analytical calculations ) as a criterion for selecting a model . This criterion works particularly against model such as triplet STDP , because of its limited capacity for inducing competition among synapses at the population level even with altered parameter values , even though the model accounts for isolated experimental results satisfactorily . Finally a natural question is whether the STDP models we have considered are interrelated in any way , or whether it is possible to unite them in a single framework . The triplet and suppression models were motivated by different experimental data sets that showed opposite synaptic modification in response to triplets ( ref . [6] vs . ref . [7] ) . However , the NMDAR-based model , which is phenomenologically closer to the molecular machinery involved in synaptic modification , can match the effects of either of these models , depending on the parameters used . Moreover , from the biophysical viewpoint , the other two models can be considered limiting versions of the NMDAR-based model through different simplifying assumptions about its components ( Fig 10 ) . If the second messengers activate instantaneously , the NMDAR-based model is qualitatively equivalent to the suppression model . The consumption of the limited pool of resting NMDARs by conversion into up or down states implements the suppressive effect of the preceding pre- or postsynaptic spike on the upcoming pre-post interaction . On the other hand , if there exists an infinite reservoir of resting NMDARs and inactive second messengers , the NMDAR-base model reduces to the triplet model . If both assumptions are fulfilled , the NMDAR-based model reduces to the simple pair-based model . This leads to the possibility that both the triplet and suppression models may arise from a single biophysical mechanism that involves NMDARs [31] , but under different conditions for the speed of conformational changes and abundance of second messengers .
We used a leaky integrate-and-fire ( LIF ) model neuron in our numerical simulations . The membrane potential of the LIF neuron obeys τmdVdt= ( Vr−V ) +Iex−Iin , ( 6 ) where τm is the membrane time constant , Vr is the resting potential , Iex is the excitatory input and Iin the inhibitory input . Although these inputs appear as currents , they are actually measured in units of the membrane potential ( mV ) because a factor of the membrane resistance has been absorbed into their definition . When the membrane potential V reaches the firing threshold Vth , the neuron fires an action potential and the membrane potential resets to the resting value Vr . The numerical values of all parameters are given Table 1 . Each presynaptic action potential arriving at an excitatory or inhibitory synapse induces an instantaneous jump in the corresponding synaptic input ( Iex or Iin ) , which decays exponentially between the input action potentials . The time course of the synaptic inputs can thus be expressed as Iex ( t ) =∑i=1Nexwi∑tik≤texp ( tik−tτs ) , Iin ( t ) =win∑i=1Nin∑tik≤texp ( tik−tτs ) , ( 7 ) where wi is the weight for excitatory synapse i , win is the common fixed weight for all Nin inhibitory synapses , and tik is the time of the k-th action potential at synapse i . The sums over presynaptic spike times are limited to spikes that arrive prior to the time t . The synaptic time constant τs = 5 ms is taken to be the same for excitatory and inhibitory synapses . The excitatory synaptic strengths , labeled collectively as w , are modified by STDP . If the rate of the excitatory and inhibitory inputs is rpre and rin respectively , the average firing rate of the LIF neuron can be approximated as [32] r¯post= ( τmπ∫Vr−μσ+αVth−μσ+αdx exp ( x2 ) ( 1+erf ( x ) ) ) −1 , ( 8 ) where μ= ( Nexrpre⟨w⟩−Ninrinwin ) τsandσ2= ( Nexrpre⟨w⟩2+Ninrinwin2 ) τs2τm , with ⟨w⟩ denoting the average value of the excitatory synaptic weights . The parameter α=|ζ ( 1/2 ) |τs/2τm , where ζ , is a correction to account for the nonzero synaptic decay constant . The arrival of a presynaptic spike increases the firing rate of the postsynaptic neuron transiently . For an LIF neuron in the case where the average excitatory input dominates over the inhibitory input , the firing rate after the arrival of a presynaptic spike at time t0 can be approximated as ( [33]; see S1 Appendix ) rpost ( t ) ≈r¯post+wexp ( −t−t0τs ) ( Vth−Vr ) τmΘ ( t−t0 ) , ( 9 ) where w is the strength of the synapse through which the presynaptic spike arrived , and Θ is the Heaviside step function . To study synaptic competition , we introduce correlations into half of the excitatory input spike trains . To generate Poisson spike trains with homogeneous pairwise ( zero-lag ) correlations , a “generating” spike train with rate r/c was first produced . The correlated spike trains were then obtained by trimming the generating spike train , that is , by randomly deleting spikes with probability 1 − c . The resulting spike trains all have rate r , and each pair is correlated with correlation coefficient c [34] . In pair-based STDP , a change of synaptic strength , Δw , is induced by a pair of pre- and postsynaptic action potentials with time difference ( pairing interval ) Δt = tpost − tpre . The functional relation between the synaptic modification and the pairing interval is Δw=F ( Δt ) ={A+exp ( −Δt/τ+ ) ifΔt≥0−A−exp ( Δt/τ− ) ifΔt<0 . ( 10 ) The positive parameters A+ and A− specify the maximum potentiation and depression , respectively . We express the synaptic strengths in units of the membrane potential ( mV ) , so A+ and A− have mV units . The time constants τ+ and τ− determine the temporal spread of the STDP window for potentiation and depression ( Fig 1A and 1E ) . In our analysis , we assume that the spike pairings are all-to-all , meaning that all possible pre-post pairs , not only the nearest neighbor pairs , contribute to plasticity . However , the results we derive apply qualitatively to a pair-based model with a nearest-neighbor restriction as well . In the triplet model , synapses are modified on the basis of pre-post pairing events in a manner similar to the pair-based model ( Eq 10 ) but , in addition , when a synapse is potentiated by a pre-post pairing ( Δt > 0 ) , the postsynaptic potentiation variable Mpost is added to the amount of the pair-based potentiation A+ . Similarly , when a synapse is depressed by a paring event ( Δt < 0 ) , the presynaptic depression variable Mpre is added to the pair-based depression A− . Thus , Δw=Ftrip ( Δt ) ={[A++Mpost ( t−ϵ ) ]exp ( −Δt/τ+ ) ifΔt≥0−[A−+Mpre ( t−ϵ ) ]exp ( Δt/τ− ) ifΔt<0 . ( 11 ) The small parameter ϵ ensures that the values of Mpre and Mpost just before their update by the pre- or postsynaptic spikes are used . The postsynaptic potentiation and presynaptic depression variables are governed by the equations dMpredt=−Mpreτpre+Apre∑iδ ( t−tpre ( i ) ) dMpostdt=−Mpostτpost+Apost∑iδ ( t−tpost ( i ) ) , ( 12 ) where δ ( t ) is the Dirac delta function , and tpre ( i ) and tpost ( i ) are the times of arrival of pre- and postsynaptic spikes respectively . This introduces four parameters into the model beyond those of the pair-based model: the time constants τpre and τpost and the increments Apre and Apost . In the suppression model with time constants τpre and τpost , the change in a synaptic weight is determined by Δw=Fsupp ( Δt ) =[1−exp ( −Δtpre/τpre ) ][1−exp ( −Δtpost/τpost ) ]×{A+exp ( −Δt/τ+ ) ifΔt≥0−A−exp ( Δt/τ− ) ifΔt<0 , ( 13 ) where Δtpre is the interval between the presynaptic spike in the pair and its preceding presynaptic spike , and Δtpost is the interval between the postsynaptic spike and its preceding spike . The suppression model introduces two new parameters beyond those of the pair-based model: the time constants τpre and τpost . The NMDAR-based model [16 , 18] is based on the assumption that NMDARs can be in one of three different states: “rest” , “up” and “down” . The variables frest , fup and fdn denote the fraction of NMDARs in each state respectively ( frest + fup + fdn = 1 ) . In the absence of pre- and postsynaptic spikes , the receptors in up and down states return to the rest state with time constants τfup and τfdn respectively . Each presynaptic spike up-regulates the receptors immediately after its arrival by an amount proportional to a parameter Afup , and each postsynaptic spike down-regulates the receptors proportional to a parameter Afdn . The dynamics of the NMDARs in the “up” and “down” states can be expressed as: dfupdt=−fupτfup+Afupfrest∑iδ ( t−tpre ( i ) ) dfdndt=−fdnτfdn+Afdnfrest∑iδ ( t−tpost ( i ) ) , ( 14 ) where the sums run over all pre- ( tpre ( i ) ) or postsynaptic ( tpost ( i ) ) spike times , indexed by i . In this and subsequent equations , we assume the convention that a quantity multiplying a δ function is evaluated immediately before the time when the argument of the δ function is zero . The fraction of active second messenger Mup is increased by postsynaptic spikes proportional to the amount of up-regulated NMDARs fup and the available inactive messengers 1 − Mup . Likewise , the fraction of active second messenger Mdn is increased by presynaptic spikes proportional to the amount of down-regulated NMDARs fdn and available inactive messenger 1 − Mdn . In the absence of spikes , these second messenger fractions decay with time constants τMup and τMdn , respectively . Thus , dMupdt=−MupτMup+AMupfup ( 1−Mup ) ∑iδ ( t−tpost ( i ) ) dMdndt=−MdnτMdn+AMdnfdn ( 1−Mdn ) ∑iδ ( t−tpre ( i ) ) , ( 15 ) where the sums run over all pre- ( tpre ( i ) ) or postsynaptic ( tpost ( i ) ) spike times . The parameters AMup and AMdn governing the magnitude of the changes in the messengers on spiking events . Finally , synaptic potentiation occurs in response to postsynaptic spikes and depends on the amount of Mup , and synaptic depression occurs in response to presynaptic spikes depending on the amount of Mdn , so that dwdt=A+[Mup−θup]+∑iδ ( t−tpost ( i ) −ϵ ) −A−[Mdn−θdn]+∑iδ ( t−tpre ( i ) −ϵ ) , ( 16 ) where θup and θdn are thresholds above which the corresponding messengers take part in plasticity , and [x]+ denotes the piece-wise linear threshold function [x]+ = x for x > 0 and zero otherwise . The small parameter ϵ is included because , in this case , we evaluate the factors multiplying the δ functions after the time of a spike , as required by the model . | Synaptic plasticity is believed to underlie learning and memory by competitive strengthening and weakening of synapses in neural networks . However , the ability to form new memories while maintaining the old ones involves an intricate balance between synaptic stability and competition . In one of the most widespread such mechanisms , spike-timing dependent plasticity ( STDP ) , the temporal order of pre- and postsynaptic spiking across a synapse determines whether it is strengthened or weakened . Early description of STDP only took into account pairs of pre- and postsynaptic spikes . However , more recent experimental results showed that the “pair-based” description is not sufficient to fully account for synaptic modifications under STDP , and motivated more complex “multi-spike” STDP models . While the conditions under which the pair-based STDP leads to synaptic stability and/or competition are well studied , it is not clear when and how multi-spike STDP models lead to synaptic stability and competition . Here , we address these questions through numerical simulation and analysis of a population of plastic excitatory synapses that converge to a neuron . We show that different multi-spike STDP models can induce synaptic stability and competition under radically different conditions , which have important implications in relating learning and memory to biophysical properties of synapses . | [
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] | 2016 | Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity |
Quantitative real time PCR ( qPCR ) offers rapid diagnosis of rickettsial infections . Thus , successful treatment could be initiated to avoid unfavorable outcome . Our aim was to compare two qPCR assays for Rickettsia detection and to evaluate their contribution in early diagnosis of rickettsial infection in Tunisian patients . Included patients were hospitalized in different hospitals in Tunisia from 2007 to 2012 . Serology was performed by microimmunofluorescence assay using R . conorii and R . typhi antigens . Two duplex qPCRs , previously reported , were performed on collected skin biopsies and whole blood samples . The first duplex amplified all Rickettsia species ( PanRick ) and Rickettsia typhi DNA ( Rtt ) . The second duplex detected spotted fever group Rickettsiae ( RC00338 ) and typhus group Rickettsiae DNA ( Rp278 ) . Diagnosis of rickettsiosis was confirmed in 82 cases ( 57 . 7% ) . Among 44 skin biopsies obtained from patients with confirmed diagnosis , the first duplex was positive in 24 samples ( 54 . 5% ) , with three patients positive by Rtt qPCR . Using the second duplex , positivity was noted in 21 samples ( 47 . 7% ) , with two patients positive by Rp278 qPCR . Among79 whole blood samples obtained from patients with confirmed diagnosis , panRick qPCR was positive in 5 cases ( 6 . 3% ) among which two were positive by Rtt qPCR . Using the second set of qPCRs , positivity was noted in four cases ( 5% ) with one sample positive by Rp278 qPCR . Positivity rates of the two duplex qPCRs were significantly higher among patients presenting with negative first serum than those with already detectable antibodies . Using qPCR offers a rapid diagnosis . The PanRick qPCR showed a higher sensitivity . Our study showed that this qPCR could offer a prompt diagnosis at the early stage of the disease . However , its implementation in routine needs cost/effectiveness evaluation .
Rickettsioses are vector borne diseases caused by Gram negative obligate intracellular rods belonging to the genus Rickettsia [1] . This genus contains 28 validated species , which are divided into four groups: typhus group ( TG ) , spotted fever group ( SFG ) , the ancestral group and the transitional group [2 , 3] . Typically , clinical features include eruptive fever associated or not with a unique or multiple inoculation eschar . However , spotless fever , absence or multiple inoculation eschars are frequently reported in endemic regions[4] . The diagnosis of Rickettsial infection remains a challenge because of these polymorphic and frequently non specific clinical presentations . In addition , due to its intracellular characteristics , the isolation of Rickettsia is difficult and limited to only reference laboratories . Thus , serology is the most widely used test in routine laboratory for the diagnosis of Rickettsial infections . However , serology has low sensitivity essentially in the early stage of the infection . Raoult et al reported that a combination of three serological methods had a sensitivity of only 56% to detect R . africae antibodies [5] . Besides , the mean delay of IgM seroconversion is 16 days in Mediterranean spotted fever and it could reach 25 days in African tick fever , for IgG seroconversion delays are 22 and 28 days respectively [6] . When patients received doxycycline , antibodies may not appear or appear late . Thus , late follow-up serological tests are needed and diagnosis is only retrospective . Since delayed diagnosis is a factor of poor prognosis , a rapid diagnostic method is needed . In an Algerian series , the authors reported a high prevalence of severe forms ( 49 . 1% ) with 50% mortality rate[7] . The majority of these patients received ineffective antibiotic therapy before being hospitalized[7] . Thus , rapid methods for diagnosis , such as PCR , are necessary for successful treatment . Many PCRs were reported to be sensitive and specific to detect Rickettsia DNA either in skin biopsies or in whole blood of infected patients . Four genes were mainly used for molecular detection and diagnosis of Rickettsia: citrate synthase encoding gene ( gltA ) [8] , genes for outer membrane proteins A and B ( ompA and ompB ) [9 , 10] and the 17kDa lipoprotein precursor antigen gene ( 17 kDa ) [11] . To improve the sensitivity of conventional PCR , nested PCR was proposed , but this technique generates many contaminations and is no more recommended for diagnosis . Diagnosis of Rickettsia infection would benefit from the use of the more rapid and sensitive method of quantitative real time PCR ( qPCR ) . In fact , qPCR was reported to be less expensive and reduces the delay of the diagnosis . Since the first report of the development of qPCR in the diagnosis of rickettsioses [12] , many authors proposed several targets which were either species specific or detecting whole Rickettsia genus . Currently , molecular diagnosis of rickettsial diseases remains unstandardized with no available commercialized kits . Renvoisé et al reported a high sensitive qPCR for rickettsial diagnosis . The assay was used widely during 2 years in the French national reference centre and allowed to reduce the diagnosis delay [13] . Recently , Giulieri et al . proposed a q-PCR targeting 16srDNA showing high analytical sensitivity and specificity . However , this PCR assay was evaluated on a small number of samples[14] . In this study , we proposed to compare the two qPCRs for Rickettsia detection and to evaluate their contribution to the early diagnosis of rickettsial infection in Tunisian patients .
Skin biopsies . Patients included in this study were hospitalized at different infectious disease departments from June 2007 to July 2012 . They were suspected to have rickettsial infection on the basis of clinical presentation ( acute fever with cutaneous rash ) and epidemiologic feature ( hot season , increased exposure to ticks and/ or fleas ) . Three Tunisian hospitals participated to the study . The Hedi Chaker University of Sfax is the unique tertiary care hospital in the south of Tunisia and patients enrolled are from different regions . Patients with eruptive fever hospitalized during 2011 at The Infectious Diseases Department Farhat Hached University Hospital of Sousse and at The Regional Hospital of Zarzis were also included . For each patient , a skin biopsy of the inoculation eschar or the cutaneous rash , a whole blood sample in EDTA and a serum sample were requested . The skin biopsies and whole blood samples were stored at -80°C until their use . This study was approved by our institutional review board “Habib Bourguiba University hospital ethics committee” with the given number 11–13 . All the subjects provided informed written consent ( all our patients were adults and children were excluded ) . Serology was performed in a microimmunofluorescence assay using R . conorii and R . typhi antigens provided by the “Unité des Rickettsies , Marseille France” as described previously [15] . Titers equal to or higher than 1: 32 for IgM and 1: 128 for IgG were considered positive . Total DNA from skin biopsies and 200 μl of whole blood were extracted using QIAamp DNA tissue extraction kit ( Qiagen , Hilden , Germany ) according to manufacturer’s instructions ( protocols used were DNA purification from tissue and DNA purification from blood ) . Since all skin biopsies were performed using punch , weight of used tissues ranged between 18 and 20 mg . DNA was eluted in a final volume of 100μl . DNA extracts were stored at -20°C until their use . Two duplex qPCR assays were used . The first set of primers and probes consisted of a qPCR targeting all Rickettsiae named PanRick and a qPCR targeting R . typhi named Rtt[14] . The second set consisted of two qPCR named RC0338 and Rp 278 detecting SFG and TG Rickettsiae , respectively[13] . QPCR amplifications and products detections were carried out in the CFX96 Touch Real-Time PCR Detection System ( Biorad , USA ) . The reaction mixture included a final volume of 20 μL with 0 . 2 μM of each primer , 0 . 2 μM of probe and 12 . 5 μL of Premix ExTaq ( Takara , Japan ) and 5 μL of DNA sample . After a hot-start cycle at 95°C for 2 min , reactions were cycled 40 times as follows: 95°C for 15 s and 60°C for 1 min . Rickettsia montanensis DNA and R . typhi DNA ( extracted from strains grown on cell culture and kindly provided by Prof Didier Raoult , Unité des Rickettsies , Marseille , France ) was used as positive controls for SFG and TG Rickettsia detection respectively . To allow quantification , four plasmids containing the different targets were constructed . For each couple of primers , the genomic DNA was amplified using the polymerase Flexi ( Promega , USA ) . PCR products were purified using Quick-PCR Purification Kit ( Qiagen , Hilden , Germany ) and then cloned into the pGEM-T Easy vector using the Kit ( Promega , USA ) . The ligation mixtures were transferred into E . coli Top10 competent cells using the CaCl2 method . The presence of the appropriate insert was verified by PCR followed by sequencing . Thus , DNA products were analyzed on a standard 2% agarose gel containing ethidium bromide ( Sigma ) . DNA sequences were elucidated by the dideoxynucleotide chain termination method according to a cycle sequencing protocol using thermosequenase ( Amersham Pharmacia Biotech ) with the DNA sequencer ABI PRISM 3100/3100-Avant Genetic Analyser . The identified positive colonies were grown in LB medium containing ampicillin ( 100 μg/ml ) , and the recombinant plasmids were isolated from bacteria cells using a QIAprep Spin Miniprep Kit ( Qiagen , Hilden , Germany ) . Quantification was performed on a Nanodrop ND-1000 ( Witech , Littau , Switzerland ) . Patients were considered to have rickettsial infection when at least one of the following conditions was fulfilled; the occurrence of the characteristic triad of rickettsiosis ( fever , eschar and cutaneous rush ) during hot season ( from May to October ) , or positive serology ( positivity of both IgM and IgG on a single serum , seroconversion or significant elevation of IgG titers on two sera ) To assess analytical sensitivity of the four qPCRs , seven serial 10-fold dilutions ( from 106 copies/μl to 1 copy/μl ) of the four positive control plasmids were tested in triplicate . To determine the quantity of DNA in samples , five standards ( 104 , 103 , 102 , 10 , 1 copies / μl ) were tested in each run . Quantity of DNA was determined using the Bio-Rad CFX Manager software . Frequencies were compared using the Student test ( χ2 ) on EpiInfo software ( version 6 . 0 ) . Fisher exact test was used if numbers of subgroup were less than 5 . Means were compared using t-test . For all used tests , the p value was considered significant when < 0 . 05 .
To determine the sensitivities of the two duplex qPCRs , serial dilutions from 106 to 1 copies/μl were produced for each target . All qPCRs were able to detect rickettsial DNA at a concentration of 2 copies/reaction . Constructed standard curves are shown in Fig . 1 . All qPCRs showed good reproducibility when standards were tested in triplicates . A total of 180 patients were included in the study . Serology was performed for all patients . Skin biopsy and whole blood samples were obtained from 77 and 174 patients respectively . Among the 180 tested patients , diagnosis of rickettsial infection was confirmed in 82 cases ( 45 . 5% ) . Serology was positive in 73 cases ( 89% ) and qPCR in 46 cases ( 56% ) . The geographic distribution of patients with confirmed diagnosis using serological tests or molecular methods is shown in Fig . 2 . For skin biopsies PanRick qPCR was positive in 24 samples ( 54 . 5% ) among 44 obtained from patients with confirmed diagnosis . When subjected to Rtt qPCR , three samples were positive . Using the second set of qPCRs , among 21 positive samples ( 47 . 7% ) , 19 were detected by RC0338 qPCR and 2 by Rp278 qPCR . For whole blood samples , PanRick qPCR was positive in 5 cases ( 6 . 3% ) among 79 specimens obtained from patients with confirmed diagnosis . When subjected to Rtt qPCR , two samples were positive . Using the second set of qPCRs , the positivity rate was of 5% with three samples positive by RC0338 qPCR and one sample positive by Rp278 qPCR . Table 1 showed positivity rates in patients with negative serology at admission and patient with already detected antibodies at diagnosis . The positivity rates of qPCRs are higher in patients with negative serology at admission . Comparison of means of DNA quantities detected in skin biopsies obtained from patients with first positive serum versus patients with negative first serum did not show any statistical difference for the PanRick qPCR ( p = 0 . 74 ) and RC00338 qPCR ( p = 0 . 48 ) . For the other qPCRs and whole blood samples , comparisons were not performed because of the limited numbers in each group .
Our data confirmed that rickettsial infections , especially SFG rickettsioses , are endemic in our region . Diagnosis of these infections remains challenging , since laboratory conformation of rickettsial infection by serology ( the most available method in routine use ) is a retrospective process and could not be used to guide patient treatment . In addition , MIF assay requires well experienced technicians and lacks standardization . Alternatively , PCR was proposed . It permits a rapid diagnosis and could improve the outcome . First , many conventional PCRs targeting many genes ( gltA , ompB , ompA ) were used but their sensitivity is diminished . Recently , real time PCR , largely used in rickettsiology , is considered as a rapid and very sensitive molecular tool[16] . Initially , it was used to study the susceptibility of Rickettsia species to antibiotics since it allows quantification of DNA[17] . Currently , many studies proposed qPCRs to detect rickettsial DNA in clinical samples . Some of these qPCRs are species specific and others are genus specific allowing detection of a wide range of rickettsial pathogens [12 , 18 , 19] . Compared to conventional PCR , qPCR is more sensitive and is less time consuming , but the higher cost of this technique limits its wide use [20] . Of note , real time thermal cyclers cost is high . However , if the instrument is available , the qPCR reaction cost could be diminished lower than that of conventional PCR . In fact , many efficient and less expensive reagents , such as that used in our study ( Takara ) , are currently available . Besides , conventional PCR uses ethidium bromide and UV light to visualize amplification products in the agarose gel medium while qPCR uses fluorescent dye system which is safer . Another limitation of qPCR is the high risk of contamination , but it remains lower than that of nested PCR[20] . In this study , we compared two previously reported duplex qPCRs to detect rickettsial DNA in clinical samples . The duplex qPCRs compared were able to detect all rickettsial DNA and to differentiate between SFG and TG using different approaches . The duplex developed by Giulieri et al [14] is composed of a first qPCR targeting the 16s rDNA gene that detect all Rickettsia species and a second qPCR detecting R . typhi , which is performed only if the first qPCR is positive . The duplex used by Renvoisé et al [13] consisted of two qPCRs that should be performed in parallel since the first qPCR detects SFG Rickettsia species and the second qPCR detects TG rickettsial DNA . The analysis of standard curves showed that the two duplex qPCRs had comparable sensitivities ( up to two copies of DNA were detected ) . When applied to clinical samples , panRick qPCR was slightly more sensitive to detect rickettsial DNA essentially for SFG Rickettsia . When previously described , this qPCR was not evaluated on a large clinical sample . In our study , PanRick qPCR confirmed diagnosis of rickettsioses for 70 . 6% of patients with skin biopsies . Finally , the PanRick qPCR would be less expensive since the PCR targeting TG is performed only when the first qPCR is positive . To have identification at species level , more specific qPCRs or standard PCRs could be further performed . Both qPCRs used showed higher rates of positivity when performed on skin biopsies than on whole blood . Previously , Renvoisé et al [13] found that among 45 positive clinical samples , 68 . 9% were skin biopsies and 4 . 4% were whole blood . In fact , a rickettsemia has been demonstrated to occur on the first stage of the disease . In our study , since institutions included were either tertiary care ( Sfax and Sousse ) or secondary care ( Zarzis ) hospitals , the majority of our patients had taken antibiotics before hospitalization with a delay exceeding frequently three days . Angelakis et al [21] , showed in a recent study that the positivity is affected by the quantity of bacterium in the sample and that previous antibiotic treatment reduces the number of Rickettsia spp . in the skin biopsy . QPCRs used in our study showed higher positivity in patients with negative serology with a statistically significant difference . In fact , when antibodies are detectable , the number of bacteria is decreased both in blood and in the inoculation site . TG Rickettsia species detected in our study has been previously reported in our country[22] . However , all reported cases were diagnosed using serology . In this report we confirmed diagnosis of murine typhus using molecular methods . Previously , we reported 43 cases of murine typhus diagnosed using MIF assay and we speculated that the disease is still endemic in our country[23] . Using qPCR , TG rickettsial DNA was detected both in whole blood samples and in skin biopsies taken from patients with eruptive fever , confirming murine typhus onset in our country . Effectively , Giulieri et al [14] reported in their study that the patient diagnosed with TG rickettsioses had traveled to Tunisia . Murine typhus was reported also in two French travelers from Tunisia [24] . In our study , many cases occurred during the summer of 2011 in the western south of Tunisia ( Zarzis ) and they were generally severe . In fact , in this region many refugees from Libya were living during this period and many of them had brought their animals . Unfortunately , no collected arthropods were obtained from these animals . It should be noted that we previously reported severe form of Israeli spotted fever rickettsioses in a patient suspected to be infected in Libya [25] . Thus , the circulation of virulent strains has to be confirmed by larger studies comparing strains in Southern East of Tunisia and Libya essentially . In conclusion , qPCR is a very sensitive tool . The main advantage of the technique is that it offers a rapid diagnosis . Optimal use of the qPCR includes its application in patients with clinical features and epidemiological characteristics compatible with rickettsioses . Of note , the assays used in our study are not sensitive enough to allow ruling out diagnosis if negative results are obtained . However , diagnosis is more effective in patients with negative serology . Thus , the assay could be proposed as an alternative method for laboratories with limited budgets . Other qPCRs , essentially multiplex qPCRs , using most frequent species specific probes could be developed so as the diagnosis of rickettsial infections could be made more easily . | Rickettsial diagnosis is challenging in routine laboratory . Serology offers only retrospective diagnosis . We aimed to introduce molecular methods in routine diagnosis of these infections . The lack of standardized methods led us to compare real time PCR assays previously reported in order to implement a clear strategies for diagnosis of these infections in our laboratory . Real time PCR proposed by Renvoisé et al includes two PCRs , one to detect spotted fever group and another to detect typhus group Rickettsiae . The real time PCR proposed by Giullieri et al includes a first PCR detecting 16rDNA of all Rickettsiae and if it is positive a second PCR detecting R . typhi should be performed . This second Real time PCR was shown to offer a slight higher sensitivity with a lower cost in our study . Skin biopsy specimens were more likely to show positive results than whole blood samples . Finally , positivity rates were higher among patients presenting at the first stage of the disease , essentially with negative serology . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Comparison of Two Quantitative Real Time PCR Assays for Rickettsia Detection in Patients from Tunisia |
We propose a new computational method for exploring chromatin structural organization based on Markov State Modelling of Hi-C data represented as an interaction network between genomic loci . A Markov process describes the random walk of a traveling probe in the corresponding energy landscape , mimicking the motion of a biomolecule involved in chromatin function . By studying the metastability of the associated Markov State Model upon annealing , the hierarchical structure of individual chromosomes is observed , and corresponding set of structural partitions is identified at each level of hierarchy . Then , the notion of effective interaction between partitions is derived , delineating the overall topology and architecture of chromosomes . Mapping epigenetic data on the graphs of intra-chromosomal effective interactions helps in understanding how chromosome organization facilitates its function . A sketch of whole-genome interactions obtained from the analysis of 539 partitions from all 23 chromosomes , complemented by distributions of gene expression regulators and epigenetic factors , sheds light on the structure-function relationships in chromatin , delineating chromosomal territories , as well as structural partitions analogous to topologically associating domains and active / passive epigenomic compartments . In addition to the overall genome architecture shown by effective interactions , the affinity between partitions of different chromosomes was analyzed as an indicator of the degree of association between partitions in functionally relevant genomic interactions . The overall static picture of whole-genome interactions obtained with the method presented in this work provides a foundation for chromatin structural reconstruction , for the modelling of chromatin dynamics , and for exploring the regulation of genome function . The algorithms used in this study are implemented in a freely available Python package ChromaWalker ( https://bitbucket . org/ZhenWahTan/chromawalker ) .
The packing of two meters of DNA in the few-micrometer nucleus results in a structure that performs multiple roles , from forming a structural scaffold of chromatin to facilitating active expression and silencing of genetic material [1 , 2] . The beginning of interest in the biophysical characterization of chromatin dates to about 50 years ago , spanning from experimental measurements of DNA persistence length [3–5] and thermal stability [4] to pulling individual DNA-protein ( DNP ) fibrils by convection flows in solution [6] , exploring fibril morphology and stability under different media conditions [7] , and exposure to ionizing radiation [5] . Before the chromosome conformation capture ( 3C ) [8] era , the classical view of chromatin organization included several successive levels of packing with archetypal structural patterns , ranging from the compaction of nucleosome-bound 10nm fibers [9] with a roughly 200 base-pair periodicity , to the transient 30nm solenoid ( hard to detect in vivo ) presumably working in the regulation of gene expression [10 , 11] , then to the 30-100kbp loops/domains that are apparently instrumental in shaping large-scale chromatin organization and gene expression [1 , 12–19] . With the development of the chromosome conformation capture ( 3C ) protocol [8] , it has become possible to study chromatin interactions between distant genomic loci . In less than a decade , the original 3C protocol evolved from the analysis of selected pairs of genomic loci to the detection of pairwise interactions between loci and the rest of the genome using chromosome conformation capture on-chip ( one-to-all , 4C , [20] ) , carbon copy ( many-to-many , 5C , [21] ) , and high-throughput 3C ( all-to-all , Hi-C , [22] ) . Finally , improvement of the signal-noise ratio was achieved by performing DNA proximity ligation before nuclear lysis , implemented in in-situ Hi-C [23] . Computational approaches for the analysis of chromatin interaction data developed in recent years can be classified as model-driven or data-driven [24] . Generally , the goal of model-driven studies is to validate physical polymer simulations using Hi-C data . Among them are models of chromatin as a crumpled ( fractal ) globule [22 , 25–27] , scenarios of loop formation [28 , 29] , analyses of the role of epigenetic factors in driving the chromatin organization [30–34] , to name a few . In data-driven studies , on the other hand , experimental Hi-C interaction maps are used for extracting information on statistically significant chromatin interactions , for defining topologically associating domains ( TADs ) and A/B compartments [35 , 36] , and for the 3D reconstruction of chromatin . Several algorithms have been introduced to study the hierarchical organization of chromatin and its correlation with the distribution of various epigenetic features [37–40] , including graph-based approaches for exploring sparse networks of Hi-C interaction peaks , as well as ChIA-PET and HiChIP interaction pairs [41–43] . A recent work by Pancaldi et al . defined chromatin assortativity as a metric for the analysis of correlation between distributions of epigenetic marks and chromatin structure [44] . To date , many methods developed for domain detection [23 , 45–47] essentially adopt an image segmentation approach aimed at identifying domain regions as a function of short-range interactions along the chromosome , and domain boundary positions are often highly sensitive to the choice of heuristic tuning parameters [48] . Recent network-based methods incorporate effects of long-range interactions in characterizing structural organization [37 , 39 , 49] and observe spatial couplings at multiple scales associated with the regulation of gene expression [50] . Spatial reconstructions of chromatin using Hi-C interaction data yield consensus structures [51 , 52] or ensembles of possible chromosomal conformations [53 , 54] , providing an overall picture of chromatin organization [55] . In this work , we propose a new approach for extracting robust genomic partitions from Hi-C data , seeking to capture the footprints of chromatin structure and organization by considering the entire interaction landscape of this complex system . Specifically , our objectives here are to identify and study structural features of chromatin from Hi-C interaction data and to find a connection between these features and data on epigenetic regulation . Introducing a Markov State Model ( MSM ) with minimal assumptions and parameters on the chromatin interaction network , we aim to identify structural partitions and interactions between them . By analogy with a biomolecule moving and interacting in condensed chromatin , the MSM allows one to explore chromatin structure using a “probe” randomly walking in the contact energy landscape derived from Hi-C data . Given the multiscale nature of the data-derived contact energy landscape and the metastability of the corresponding MSM , we can identify regions of dense intra- and inter-chromosomal interactions , linkers between these regions , as well as the overall topology of individual chromosomes and the complex structures that chromosomes form by interacting with each other . We found that multiple levels of hierarchy exist in the structure of each chromosome with a layer-by-layer splitting of partitions into subunits with distinct structural and epigenetic features , and presumably , distinct roles . The notion of effective interaction between partitions is introduced and shown to be instrumental in uncovering the hierarchical organization , as well as functional dynamics and epigenetic modulation , of individual chromosomes . Looking at the whole-genome picture , the matrix of effective interactions delineates how chromosomal partitions form a major cluster—with several chromosomes linked by significant inter-chromosomal interactions—as a structural scaffold for genome architecture . The notion of affinity between partitions complements the picture of effective interactions by evaluating the degree of association between partitions , which may contribute to the formation of topologically associated domains , transcription factories and other functional elements , thereby organizing the regulation of genome expression .
A common strategy to study complex network data structures is to combine them with a discrete state Markov process , commonly called a Markov State Model ( MSM ) , with the goal of characterizing hidden network properties [56–58] . MSMs enable one to systematically explore network structure via random walks , where traveling probes form virtual trajectories through the whole network by connecting pairs of nodes . It has been shown that studying the spectral and metastability properties of the network-associated MSM allows one to obtain a reduced description of the underlying complex data . In order to illustrate how MSMs can be used for studying Hi-C data , we introduce here a toy model of a chromosome . Let us consider a linear system characterized by a discrete set of loci S = {1 , … , N} , with N = 500 . We assume that the number of loci N determines the maximal resolution of this relatively large system . Each locus of the system is associated with an energy Ei , which is linked to the intrinsic stability of the locus i at the given resolution . For the sake of argument , we assume the intrinsic stabilities Ei to follow a hierarchically shaped energy profile ( Fig 1A ) . The energy profile considered here contains 18 wells separated by barriers ranging from 0 . 5 to 2 energy units . On the first level of hierarchy , there are two basins separated by a barrier of 2 energy units ( black diamonds in Fig 1A ) , each divided into three sub-basins ( indicated as red circles in Fig 1A ) , which in turn are split into three basins on the third level of hierarchy ( black circles in Fig 1A ) . A traveling probe in such an energy profile is assumed to make two types of moves: sliding between adjacent loci and hopping between non-adjacent ones . We do not make any assumption about the three-dimensional structure of the system and assume a power law contact probability between non-adjacent loci , namely ( d0/dij ) α , where d0 and dij are the distance between adjacent and any non-adjacent loci respectively , which is equivalent to the genomic distance between loci i and j and such that dij = d0 for adjacent loci . Thus , for each pair of loci i and j , we define the contact energy landscape Eij = ( Ei + Ej ) /2 − αln d0/dij with α = 1 . 5 . Assumptions on the power law dependence and the value of exponent α are made on the basis of empirical observations on Hi-C data and polymer models of chromosomes [22] . The contact energy landscape is represented in Fig 1B . To construct the MSM describing the motion of a probe , we define the corresponding Markov generator L for transitions between loci i → j by the Laplacian Lij=e−β ( Ej−Ei ) /2eβln ( d0/dij ) α and Lii = −∑j≠iLij , with transition matrix pij = Lij/∑j≠iLij , pii = 0 ( ∑jpij = 1 ) , flux πij = Lijμj , where steady state probabilities are given by μi=e−βEi/∑je−βEj , and β is an inverse temperature parameter . A network of nodes ( loci ) and edges ( contacts ) is obtained from the matrix of fluxes πij , which represents the symmetric probability of contact between a pair of loci . With the set of rules given by the above Markov generator , a probe will tend to explore regions of the network in the neighborhood of the loci that are more stable , i . e . , within an energy well , and will rarely connect loci in different energy wells . This property relates to the “metastability” of the corresponding Markov process . Specifically , in a metastable MSM only a few nodes function as “hubs” of the network , which means that the probe tends to spend most of the time in the neighborhood of these hub nodes , instead of anywhere else . In other words , a probe departing from a generic node in the network is likely to hit the closest hub node in the set hub-nodes M . Additionally , the probability for a probe departing from a hub-node in M to return to itself is larger than that for the probe to reach another hub-node in M . As a result , nodes in the neighborhood of hubs tend to cluster together in a modular manner . This is a condition that allows one to find a reduced size MSM that approximates the original Markov process associated with the initial network . One can quantify how well the probe motion satisfies this condition by defining a metastability index ρM . A metastability index is the ratio of two probabilities ( see Eq 6 in Methods for a precise definition ) : the probability Pout for a probe to connect two different hubs in the set M ( as small as possible ) over the probability Pin for a walker to hit any hub in the set M irrespective of the starting point ( as large as possible ) [59] . In a metastable MSM the metastability index is expected to be a small number ( ρM=Pout/Pin<1 ) characteristic of the hub set M . To understand how metastability works , it is instructive to consider the large changes in kinetic properties of the MSM upon increase of the inverse temperature parameter β ( annealing condition ) . These changes are clearly illustrated by the mean first passage time MFPT τij , which is the average time ( number of steps ) a probe takes to connect the pair of states i and j ( where states represent loci of the toy chromosome , see Eq 4 in Methods ) . Fig 1C shows the MFPT matrices in the case of low β = 1 and high β = 10 , respectively . A clear separation of time scales emerges upon increasing 𝛽 , as reflected in the partitioning of the MFPT matrices . The nested squares emerging in the MFPT matrix ( Fig 1C ) at high β identify pairs of states/loci ( i , j ) with comparable values of the MFPT τij , which is a result of the hierarchically shaped energy profile . As β is increased , the emerging separation of time scales in the MFPT matrix is the result of the dominant barrier that separates a given pair of loci in the energy landscape ( see Fig 1A ) : each of the separated regions contains one or more hubs that cause probes to stay within its vicinity . As a result , the effect of high β on the MFPTs of the MSM elucidates how dominant interactions in the system can be captured using just a subset of loci , the hub set M . To quantitatively identify the hub set , an optimization procedure is performed in order to find the sets M that minimize the metastability index ρM ( see details in Materials and Methods ) as a function of increasing β . Fig 1D shows the optimized profile of the index ρM as a function of the hub set sizes , and at different values of β . All the profiles of ρM clearly show three minima corresponding to the hub sets M ( 2 ) , M ( 6 ) , and M ( 18 ) ( of sizes 2 , 6 and 18 , respectively ) , which correctly identify locations of the energy wells in the hierarchically shaped energy landscape in Fig 1A . The hub sets obtained by optimizing the index ρM are suitable as cores of partitions , which characterize the coarse-grained state space of an approximated MSM . Fig 1E ( top ) depicts the toy network associated with the contact energy landscape shown in Fig 1B . Nodes are colored according to the partitions constructed around nodes in the hub set M ( 6 ) . A reduced network corresponding to the hub set M ( 6 ) is also shown in Fig 1E ( bottom ) . The nodes in this network are defined as soft partitions of the initial set of loci S , whereas the links characterize the “effective interactions” between nodes with values Fab = ∑i∈Sqa ( i ) πib ( see Eq 11 in Materials and Methods ) . The quantity qa ( i ) is a committor probability , which is the probability for a probe departing from a locus i to hit the locus a∈M before any other locus in the hub set M ( see Eq 8 in Materials and Methods ) . Using the intuition acquired with the help of this toy model , we describe in the following section how a MSM can be constructed from the Hi-C dataset of a single chromosome and how metastability analysis can be performed in order to infer chromosomal architecture and effective interactions between partitions . We now consider the random walk through the interaction network of a single chromosome , using the example of Hi-C data on human chromosome 17 in the human B lymphoblastoid cell line GM12878 at 50kbp resolution [23] and describing it via a Markov process . To do that , we start from the number of times fij a pair of genomic loci i and j is found in a contact . After applying a Gaussian smoothing filter on the raw data ( see Hi-C data preprocessing in Materials and Methods ) , a pairwise contact energy Eij = −lnfij is defined for each pair of genomic loci . With this interpretation , the larger the contact frequency the more stable ( lower contact energy ) pair of genomic loci is involved . The representation of this two-dimensional contact energy landscape is shown in Fig 2A . A probe moving in such a landscape is expected to spend most of the time in pairs characterized with low contact energy and rarely connecting across high contact energy pairs . In the toy model presented in the previous section , a pairwise contact energy landscape ( Fig 1B ) was constructed from the one-dimensional energy landscape ( Fig 1A ) . Here , we use a reverse logic and consider the one-dimensional projection ( Fig 2B ) of the two-dimensional contact energy landscape ( Fig 2A ) . To do that we define the contact energy of a genomic locus i as Ei = −lnfi , where fi = ∑jfij is the total number of times a genomic locus i is found in any contact , hence loci involved in more contacts are more stable as they exhibit lower contact energy . Fig 2B shows the 1D projection of the pairwise contact energy landscape ( for both raw and Gaussian-smoothed data ) , which presents multiple features—minima , maxima , and barriers—characterizing the architecture of the chromosome . Here , we briefly describe the metastability analysis applied to chromosome 17 ( steps 1–4 ) and consider whole-genome interactions ( step 5 ) using a coarse-grained approximation . Step 1 . In order to explore chromosomal architecture , the MSM describing the motion of a probe in the contact energy landscape Eij is implemented by introducing the Maxwell-Boltzmann probability πij ( β ) =e−βEij/Z ( β ) , where Z ( β ) =∑ ( i , j ) e−βEij is the partition function , β is the inverse temperature parameter , and πij is the symmetric flux of probes between pairs of loci . Using a 50kbp resolution for the Hi-C dataset , a total of N = 1625 genomic loci comprise the state space S of the MSM for chromosome 17 . The transition matrix associated with the MSM is defined as pij = πij/μi , where μi ( β ) = ∑jπij ( β ) is the Boltzmann weighted probability ( steady state probability distribution ) of observing locus i involved in any contact . The effect of annealing ( increasing the inverse temperature β ) on the kinetics of a random walker is clearly reflected in the MFPT matrices ( Fig 2C ) , obtained at low and high β , respectively . While at low β ( β = 1 ) MFPTs show no partitioning , a separation of time scales becomes evident at high β ( β = 9 ) . Indeed , the 1D projection of the pairwise pseudo-energy landscape ( Fig 2B ) shows that , apart from the centromere that naturally separates the two chromosome arms , the highest barrier in the 1D projection is about 1 . 5 in β−1 units ( see Fig 2B ) . Therefore , partitioning of MFPTs scales is observed only for significantly higher values of β . Step 2 . Optimization of the metastability index ρM ( see details in Materials and Methods ) over the hub set M of different sizes was performed as a function of the inverse temperature parameter β , revealing the levels of structural hierarchy of chromosome 17 . The ρM profiles upon increasing β ( Fig 2D ) converge towards five minima , which correspond to the hub sets M ( 2 ) , M ( 5 ) , M ( 8 ) , M ( 12 ) , and M ( 27 ) , of sizes 2 , 5 , 8 , 12 , and 27 , respectively . The M ( 2 ) hub set is not considered as it trivially identifies the chromosome arms separated by the centromere . It should be noted that the locations of the obtained hub sets correspond to the locations of the multiple wells present in the projected contact energy landscape , as shown in Fig 2B . Step 3 . Given the hub sets obtained at different levels of structural hierarchy , one can identify chromosomal partitions , namely regions of the chromosome compacted around corresponding hubs and , at the same time , separated from one another . Soft partitions are defined around corresponding hubs using the committor probability qa ( i ) [59] , which in this case is interpreted as the probability for a locus i to belong to the partition defined by the hub a∈M . To identify physical partitions of the chromosome in relation to other chromosomes , a coarse-grained description is adopted here by considering hard partitions . In this case , a step function θA ( i ) characterizes whether a locus i belongs to a partition A , specifically θA ( i ) = 1 if i ∈ A , θA ( i ) = 0 otherwise , and ∑AθA ( i ) = 1 for any locus i ( see Eq 9 in Materials and Methods ) . Fig 2E illustrates the partitioning of the network for human chromosome 17 that is obtained from the hub set M ( 12 ) . Step 4 . To complete the description of chromosome structure , one needs also to characterize the strength of interactions between the partitions obtained at different levels of hierarchy . As in the example illustrated in the toy model , we consider the effective interaction between two soft partitions located around the hub loci a and b of a chromosome as the mean contact energy acting between them , which corresponds to the weighted flux connecting loci a and b via the committor probability qa ( i ) , namely Fab = ∑i∈cqa ( i ) πib ( see Eq 11 in Methods ) . Step 5 . In the context of whole-genome interactions , a coarse-grained description is adopted ( see Step 3 ) for estimating the mean contact energy between pairs of partitions in the 23 chromosomes: FAB = ∑i∈gθA ( i ) ∑j∈gπijθB ( j ) , where θA ( i ) and θB ( i ) are step functions and πij is the flux of probes between corresponding loci ( see Materials and Methods for details ) . Fig 3A–3C show the partitioning of chromosome 17 at three levels of hierarchy with corresponding effective interaction matrices ( Fig 3D–3F ) , and the band representation of partitions at all three levels ( M ( 5 ) , M ( 12 ) , M ( 27 ) ; Fig 3G ) . The major partition boundaries that emerge at the first level of hierarchy persist through the other levels ( Fig 3G; similar for all chromosomes , see S1 Fig ) and show a qualitative agreement with the borders of euchromatic and heterochromatic bands obtained from Giemsa staining ( Fig 3G and S1 Fig ) . Unfortunately , as Giemsa staining is a very basic and crude cytological method for identifying densely-packed ( heterochromatic , dark stain ) and low-density ( euchromatic , light stain ) genomic regions , it is not possible to perform an accurate quantitative analysis on staining bands [61 , 62] . At the lowest level of hierarchy ( Fig 3A ) , we observed the bulk topology of the chromosome where two chromosomal arms are brought together via strong interactions between partitions 1 , 4 , and 5 . Most partitions at the lowest levels of hierarchy are found to contain both euchromatic and heterochromatic bands , and they have highly distinct structural and/or functional characteristics . At the second level of hierarchy ( Fig 3B ) , partitions 1 . 2 , 4 . 1 , 4 . 2 , 5 . 1 , and 5 . 4 form several non-adjacent contacts , working as hubs responsible for most of the network structure . The third level of hierarchy ( Fig 3C ) yields further details of chromosomal architecture: the p-arm is loosely connected and is weakly centered on 1 . 2 . 2 and 1 . 2 . 4 , while the q-arm is densely connected by multiple hubs ( 4 . 1 . 1 , 4 . 2 . 2 , 4 . 3 . 1 , 5 . 1 . 2 , and 5 . 4 . 1 ) . At this level many partitions are homogeneous , either eu- or heterochromatic , interacting more strongly with partitions with similar packing densities , resembling the phenomenology of the so-called A/B ( active/inactive ) chromatin compartments [22] . For instance , partition 1 . 2 is split into mostly euchromatic ( 1 . 2 . 2 , 1 . 2 . 3 ) and heterochromatic ( 1 . 2 . 1 , 1 . 2 . 4 ) partitions , while partition 4 . 1 is split into predominantly euchromatic ( 4 . 1 . 1 , 4 . 1 . 2 , 4 . 1 . 3 ) and heterochromatic ( 4 . 1 . 4 , 4 . 1 . 5 ) ones . Partition 4 . 1 . 1 is the largest among these , forming significant interactions with the p-arm through partition 1 . 2 . 2 . Another noticeable interaction between chromosomal arms occurs via the partition 4 . 3 . 1 , which links heterochromatic partitions 5 . 1 . 1–2 and 1 . 2 . 4 . Interestingly , the mostly euchromatic partition 1 . 2 . 2 is responsible for many non-adjacent contacts with the q-arm , whereas heterochromatic 1 . 2 . 4 forms non-adjacent contacts only with 4 . 3 . 1 and 3 . 1 . 1 . With these observations , one may conclude that heterochromatic partition 1 . 2 . 4 acts as a structural foundation that link the mostly euchromatic partitions 1 . 2 . 2 , 1 . 2 . 3 , 2 . 1 . 1 , 2 . 1 . 2 , and 3 . 1 . 1 . To investigate how the hierarchical organization of chromosomes facilitates their function , we first analyzed the average density of various epigenetic factors in partitions ( Fig 4 and S3 Fig ) , using chromosome 17 as an illustration for this analysis and operating at the third level of structural hierarchy . Fig 4A , in which node sizes depict partition sizes , shows that heterochromatic partitions 5 . 1 . 1 and 5 . 1 . 2 apparently form a structural foundation of chromosome 17 architecture , linking the p- and q-arms through the large mixed partition 4 . 3 . 1 and the heterochromatic partition 1 . 2 . 4 . Next , we consider two transcription factors commonly associated with chromatin structure studies , namely CTCF ( transcriptional repressor , Fig 4B ) and RAD21 ( cohesin , S3H Fig ) . The CTCF graph ( Fig 4B ) shows that the heterochromatic partitions ( 1 . 2 . 4 , 5 . 1 . 1 , 5 . 1 . 3 , and 5 . 2 . 2 ) and the pericentromeric partition 3 . 1 . 1 have the lowest CTCF levels , while the highest CTCF levels were found on 4 . 2 . 2 , 4 . 2 . 1 , and 5 . 4 . 3 . The euchromatic or mostly euchromatic partitions 1 . 1 . 1 , 1 . 2 . 2 , 2 . 1 . 2 , 2 . 2 . 1 , 4 . 2 . 3 , 5 . 4 . 1 , and 5 . 4 . 2 show average levels of CTCF in the overall eight-fold variation in the density of this transcription factor across partitions . Among the hub partitions , namely those that form extensive non-adjacent contacts , only 4 . 2 . 2 shows high CTCF levels . Unlike CTCF , RAD21 ( a component of cohesin ) exhibits only a two-fold variation in densities across partitions at this level of hierarchy . The correlation between CTCF and cohesin binding sites has been noted previously [63 , 64] , and indeed the distribution of RAD21 ( S3H Fig ) chiefly follows the same general trends as that of CTCF . The strongest among the few exceptions are the increased density of RAD21 in 4 . 2 . 3 and decreased density in 5 . 4 . 3 . Turning to histone modifications , we note that H3K9ac ( Fig 4C ) and H3K9me3 ( S3A Fig ) are associated with activation and silencing of transcription in corresponding promoter regions and , therefore , are expected to show opposite density trends . Indeed , densely packed heterochromatic partitions ( 1 . 2 . 4 , 5 . 1 . 1 , 5 . 1 . 3 ) and pericentromeric 3 . 1 . 1 show very low levels of the activating H3K9ac histone modification , while the silencing H3K9me3 modification shows increased density in these partitions ( highest in the case of 3 . 1 . 1 ) . At the same time , euchromatic and mostly euchromatic partitions 1 . 1 . 1 , 1 . 2 . 2 , 2 . 1 . 2 , 4 . 1 . 2 , 4 . 2 . 2 , 5 . 4 . 2 , and 5 . 4 . 3 show an increased density of both epigenetic factors , with some slight variations . The opposing trends are observed in heterochromatic partitions for the activating H3K27ac ( decreased density , S3B Fig ) and inhibiting H3K27me3 ( increased density , S3C Fig ) modifications , with the most pronounced effects being on 1 . 2 . 1 , 4 . 1 . 4 , 4 . 1 . 5 , 5 . 1 . 1 , and 5 . 1 . 3 . Distributions of the H3K4me1 ( S3D Fig ) and H3K4me3 ( S3E Fig ) modifications—both activators—show higher densities in most euchromatic partitions , and in few heterochromatic ones—1 . 2 . 1 , 4 . 2 . 1 , and 5 . 1 . 2 . Interestingly , the heterochromatic partitions 1 . 2 . 1 , 4 . 2 . 1 , and 5 . 1 . 2 are enriched in all activating histone modifications considered here ( H3K4me1 , H3K4me3 , H3K9ac , H3K27ac ) , and , at the same time , are depleted in the inhibiting modifications H3K9me3 , H3K27me3 . These trends suggest that the above partitions may contain facultative heterochromatin that switches between active and repressed states . Overall , the DNA accessibility graph , indicating the DNase-Seq signal ( Fig 4D ) , shows that most of the euchromatic partitions ( 1 . 1 . 1 , 1 . 2 . 2 , 2 . 1 . 2 , 4 . 2 . 2 , 5 . 4 . 2 , and 5 . 4 . 3 ) are rather open and accessible for contacts or interactions . Increased accessibility observed for partitions 4 . 2 . 1 and 1 . 2 . 1 is consistent with the conclusion that these partitions may contain facultative heterochromatin , which was inferred from the distribution of activating and inhibiting histone modifications . The partition 5 . 1 . 2 , on the contrary , is less accessible , suggesting that it contributes mostly to the structure formation . Finally , the distributions of RNA polymerases II and III ( S3F and S3G Fig ) complement the picture of the potential functional involvement of different partitions in chromosome 17 . RNA polymerase II ( POL2 ) , crucial component of mRNA synthesis , is distributed quite evenly in both euchromatic and heterochromatic partitions ( except the high level in 2 . 2 . 1 ) . The synthesis of tRNA , 5S rRNA , and small RNAs through the action of RNA Polymerase III ( POL3 ) is distributed in a more specific way across different partitions . The POL3 signal is high in euchromatic partitions 1 . 2 . 2 , 4 . 1 . 2 , 4 . 2 . 3 , in mixed 4 . 2 . 1 and 5 . 3 . 1 , as well as in some heterochromatic ones ( 4 . 1 . 4 , 4 . 1 . 5 , 5 . 1 . 2 , 5 . 1 . 3 , and 5 . 2 . 2 ) . Peculiarities in distributions of epigenetic factors , DNA accessibility , and RNA polymerases revealed in the analysis of individual chromosomes should be further considered in the framework of whole-genome organization , exploring the interplay between intra- and inter-chromosomal interactions in the regulation of gene expression . To this end , we moved from single-chromosome analysis to studying the whole-genome effective interaction matrix . Given that chromosomes are spatially segregated into chromosomal territories ( CTs ) , one can approximate the whole-genome organization by merging single-chromosome partitioning schemes at appropriate levels . Using a selected representative level from each chromosome ( see Materials and Methods: Chromosome partitioning ) , we formed a whole-genome description with 539 partitions , with an average partition size of about 5Mbp ( S2 Table ) . The matrix of effective interactions between chromosomal partitions ( Fig 5 ) provides a general view of the overall physical interactions in chromatin . It shows that chromosome 1 and small chromosomes ( 14–20 and 22 ) massively interact with others , while chromosomes 4 , 5 , 9 , 21 , and X appear to be relatively isolated from the rest of the genome ( Figs 5 and 6 ) . Several partitions form consistently stronger intra- and inter-chromosomal interactions with other partitions . We classified interaction strengths into 5 layers with equally-spaced threshold values: the scaffold layer is the strongest , followed by layers 1 , 2 , etc . ( see also Materials and Methods for the definition of the interaction strength at different layers ) . Fig 6A shows the major cluster in the whole-genome partition set: partitions from different chromosomes form tight sub-clusters highlighted by color and marked by chromosome labels . All displayed partitions are linked by the two strongest layers of interactions ( scaffold interactions are represented by black edges , and layer 1 by grey edges ) . It is easy to see that most of the intra-chromosomal contacts and some inter-chromosomal interactions are established on the scaffold layer , giving rise to a structural foundation for genome-wide architecture ( Fig 6 ) . Specifically , chromosomes 1 , 14 , 16 , 17 , 19 , 20 , and 22 are densely interconnected , while other chromosomes in the major cluster are linked to them via only a few interactions . Partitions 1–2 . 1 . 2 , 14–3 . 4 , and 22–6 , for example , act as contact hubs between these massively interacting chromosomes and others . On the other hand , partitions such as 3–2 . 2 . 2 , 8–6 . 2 . 1 , 10–3 . 4 . 1 , connect less-strongly interacting chromosomes to the strongly interacting ones ( see S3 Table for interaction strength layers for these interactions between chromosomes ) . Notably , chromosomes 1 and 2 , the two largest ones ( about 250Mbp each ) , behave differently in the context of the whole-genome interactions . While chromosome 1 serves as a hub in the interactions between the highly- and less-interacting chromosomes , chromosome 2 does not show many interactions with other chromosomes ( Fig 6A ) . Turning to functional regulation , most of the partitions involved in significant inter-chromosomal interactions exhibit higher densities of several epigenetic factors , such as CTCF ( Fig 6B ) , H3K9ac ( S5A Fig ) , and DNase accessibility ( S5B Fig ) . These partitions may participate in the formation of active epigenetic compartments facilitated by the structural role of CTCF [65] . Active processing of genomic information taking place in these structures is regulated by the activating histone modifications ( H3K9ac ) and transcriptional repressors ( CTCF ) . The opposite trend is observed for partition 14–3 . 4 , which is coupled with a higher density of the silencing H3K9me3 histone modification . Therefore , partition 14–3 . 4 and its interactions with partitions in other chromosomes , for instance 10–3 . 4 . 1 , with low activating factor densities may indicate the formation of dense structural heterochromatin and/or silencing facilitated by Polycomb bodies [1 , 66] . To evaluate how the distribution of epigenetic signals may be associated with interaction between partitions , we calculated correlations between effective interaction strengths and the expected enrichment of factor densities across partition pairs that are mostly euchromatic ( EC ) or heterochromatic ( HC ) . The enrichment of factor densities is estimated here as the product of factor densities per partition ( S12 Fig ) . To obtain the strongest signals , we limited our consideration to interactions between partitions that are dominated by either eu- or heterochromatin ( see legend for S12 Fig for the definition of EC and HC partitions ) : EC-EC pairs ( S12A Fig ) , HC-HC pairs ( S12B Fig ) , and EC-HC pairs ( S12C Fig ) . Despite the relatively weak correlations , general trends appear to be quite clear , with the strongest ones seen between euchromatic ( EC-EC ) partitions ( S12A Fig ) . Transcription factors CTCF and RAD21 are always positively correlated , as well as POL2 in EC-EC ( S12A Fig ) and EC-HC ( S12C Fig ) pairs , whereas POL3 shows no correlation . The positive correlation for CTCF and RAD21 with effective interaction strength agrees with current literature on the role of CTCF and cohesin in mediating chromatin structure through looping interactions [65 , 67 , 68] . Stronger interactions between EC partitions appear to be linked to higher transcriptional activity , as suggested by the positive correlation with active histone modifications and POL2 . Absence of correlation for HC-HC pairs in the case of POL2 can be related to the fact that transcriptional activity is suppressed in heterochromatin . Potential active involvement of interacting euchromatic partitions in the formation of transcription factories is corroborated by the most pronounced correlation observed for DNA accessibility in pairs of euchromatic partitions ( S12A Fig ) . Activating histone modifications , except for H3K4me3 , show positive correlations in all types of interacting partition pairs . Interestingly , silencing histone modifications appear also to be weakly correlated with effective interactions between partitions . The original partitioning analysis was performed on the GM12878_primary ( B lymphoblastoid ) Hi-C dataset by Rao et al . [23] ( GEO accession GSE63525 ) . We also applied our model to four other datasets: GM12878_replicate ( a biological replicate of GM12878_primary dataset ) , IMR90 ( lung fibroblast ) , HUVEC ( umbilical vein endothelial cells ) , and HMEC ( mammary epithelial cells ) . Our goal in this analysis was two-fold: ( i ) to benchmark robustness and reproducibility of the method using the replicate dataset; ( ii ) to examine the sensitivity of the method in detecting alterations in chromatin organization in different cell lines , associated with corresponding genome functional states and gene expression levels . S13 Fig shows side-by-side comparisons of the partitioning schemes for GM12878_primary and the other datasets , and S6 Table shows some indicative statistics comparing the results from each case . First , we observed a high consistency between the biological replicates of GM12878: S13A Fig shows that the partitioning was highly consistent between the two sets of Hi-C data , with partition boundaries being identical in most cases , resulting in the high Rescaled Mutual Information ( RMI ) of 0 . 70 ( see Chromosome partitioning in Materials and Methods for definition of RMI ) . The composition of the major cluster was also largely identical . Comparing the results from other cell lines , we observed significant differences: IMR90 , HUVEC and HMEC cells each had significantly shifted partition boundaries compared to GM12878_primary , leading to lower RMI values of 0 . 39 to 0 . 48 . The major-cluster structures in these cell lines are also significantly different ( see S16 Fig for IMR90 and HUVEC ) , especially that of HMEC , where no strong inter-chromosomal interactions were observed between partitions , and the chromosomes remained isolated in the whole-genome network . Notably , in both IMR90 and HUVEC , a large partition on chromosome 9 forms extensive inter-chromosomal interactions: the overlapping region ( chr9:1268000000–1412500000 ) contains two genes ( OLFM1 and MVB12B ) with the RNA-expression profiles different from that of GM12878 . The MVB12B ( a component of endocytic protein system [69] ) gene is activated in both IMR90 ( lung fibroblast ) and HUVEC ( umbilical vein endothelial cells ) cell lines , and OLFM1 ( lung cancer marker [70] ) in IMR90 , while both genes are silenced in GM12878 . These preliminary observations call for future in-depth investigation of the structural basis , functional mechanisms , and specifics of epigenetic regulation behind the observed differences between cell types . While effective interactions between partitions characterize the overall architecture of genome organization , it may not fully discriminate functionally relevant interactions between chromosomes and their parts . Indeed , most partitions are presumably in constant motion within the nucleus , and as Hi-C experiments are typically conducted on unsynchronized cell populations , effective interactions capture the average contact probability arising from both random diffusion and specific transient interactions . Therefore , in addition to effective interactions , the affinity between partitions was also calculated , which reflects how the observed interaction frequency differs from the expected frequency ( from random diffusion ) , because of possible associations between partitions . Defined as the ratio between observed and expected contact probabilities between pairs of partitions ( see Eq 15 in Methods ) , the affinity is indicative of the degree of association between partitions , and high affinity values may serve as a manifestation of biologically-relevant contacts . Fig 7 contains the whole-genome matrix of pairwise affinities ( blue: high affinity , white: low affinity ) between corresponding partitions . Like the observations in the whole-genome effective interaction matrix ( Fig 5 ) , the largest chromosomes 1 and 2 exhibit different behavior , with chromosome 1 containing partitions with high affinity to those in several other chromosomes ( especially with chromosomes 14–22 ) and chromosome 2 generally showing low affinity to partitions in other chromosomes . Smaller chromosomes 14–22 form more , presumably functional , contacts with each other , compared to other chromosomes . At the same time , the number of partition pairs with high affinity is much lower than number of pairs with significant effective interactions ( compare Fig 5 and Fig 7 ) . In total , we observed 687 high-affinity pairs ( S4 Table ) , which are seemingly crucial for whole-genome structural organization and function . Interestingly , several large partition pairs ( >2Mb ) with high effective interactions and affinity are located in the telomeric regions of corresponding chromosomes ( yellow cells in S5A Table ) , having moderately high densities of epigenetic/transcription factors and increased DNA accessibility ( S5A Table ) . Two other groups of partitions with high affinities are characterized by smaller partition sizes and highly elevated concentrations of various transcription factors and epigenetic modifications ( S5B Table ) : ( i ) pericentromeric partitions ( red cells in Table ) show high concentrations of activating ( H3K4me3 ) and silencing ( H3K27me3 and H3K9me3 ) histone modifications and high levels of POL3 and RAD21; ( ii ) telomeric partitions ( yellow cells in Table ) show strongly increased concentrations of all activating histone modifications , POL2 , and CTCF , as well as high DNA accessibility . This separation between types of activating histone modifications , transcription factors , and DNA accessibility in centromeric and telomeric regions signals a specificity of functional interactions between partitions with high affinities to each other . Examples of partitions involved in high-affinity interactions and characterized by the over-representation of different epigenetic factors and modifications are collected in Fig 8 and S6 Fig , where high-affinity clusters of partitions enriched in these epigenetic marks are plotted . A comparison of the inter-chromosomal interactions in the major cluster of effective interactions ( Fig 6 ) with interactions in affinity clusters ( Fig 8 and S6 Fig ) highlights several relatively-small partitions , e . g . 9–5 . 6 . 3 , 9–5 . 6 . 5 , and 11–5 . 1 . 3 , that act as junctures between different chromosomes . These partitions yield increased density of CTCF along with other juncture-partitions ( 3–2 . 2 . 2 , 6–1 . 4 . 3 , 8–6 . 2 . 1 to name a few ) , pointing to the potential importance of these partitions in whole-genome structural organization . This inference is further supported by multiple interactions detected for partitions 8–6 . 2 . 1 , 9–5 . 6 . 5 , and 11–5 . 1 . 3 in the CTCF affinity graph ( S6D Fig ) . Additionally , H3K9ac ( Fig 8A ) and H3K27ac ( Fig 8C ) affinity graphs hint at the functional importance of some of these partitions: the central part of the H3K9ac graph is formed by partitions 9–5 . 6 . 3 , 9–5 . 6 . 5 , and 11–5 . 1 . 3 , while 9–5 . 6 . 3 and 11–5 . 1 . 3 are also present in the H3K27ac graph . Focusing on individual epigenetic factors , the activating H3K4me3 mark links more partitions than the activating H3K4me1 histone modification . The silencing H3K9me3 histone modification functionally links many centromeric partitions , whereas the activating H3K9ac modification works in both mostly euchromatic and mixed euchromatic/weakly-heterochromatic non-centromeric regions . Similarly , the activating H3K27ac modification affects mostly non-centromeric partitions , unlike the very active silencing H3K27me3 , for example , in partitions 1–4 . 11 . 1 , 2–3 . 1 . 1 , and 10–2 . 1 . 1 ( Fig 8 ) . These partitions are also characterized by the high levels of POL3 ( S6B Fig ) and RAD21 ( cohesin , S6C Fig ) , whereas the insulator CTCF links several euchromatic partitions across different chromosomes ( S6D Fig ) . It is evident that centromeric partitions 1–4 . 11 . 1 , 2–3 . 1 . 1 , and 10–2 . 1 . 1 are enriched with almost all regulatory factors ( see Fig 8 and S6 Fig ) , yielding high affinities to other partitions and pointing to important functional interactions and intense regulation taking place in these partitions . Interestingly , while activating histone marks ( Fig 8A , 8C , 8E and 8F ) are dominant in several euchromatic partitions , these marks are also present in partitions containing large sections of heterochromatin and centromeres , which are commonly associated with dense packing and transcriptional repression . Similarly , silencing histone marks ( Fig 8B and 8D ) are dominant not only in heterochromatic and centromeric partitions , but also in some partitions that are mostly euchromatic . Furthermore , dominating regions for the transcription factors CTCF and cohesin ( S6C and S6D Fig ) appear to have significant overlap with activating and silencing histone marks , respectively . These overlaps show the complexity of functional interactions in chromatin , even at the coarse-grained level of partitions: opposing factors are found acting in the same regions , allowing for switching between transcriptional states in response to other biochemical cues .
We proposed here a computational framework for exploring chromatin organization based on Markov State Modelling of chromatin interactions . Given the multilevel hierarchical packing of chromatin , we introduced a reduced description of the complex network of chromatin interactions and its organization via interactions between structural units at different levels of hierarchy . By interpreting Hi-C data as a pairwise contact energy landscape , a Markov State Model approach was used to explore the chromatin interaction network through the random walk of a probe . While steady-state distributions obtained from the Markov process of randomly-moving molecules can serve as a measure of the chromatin accessibility for epigenetic factors [71] , taken alone they describe neither the genome architecture , nor structural and functional interactions between genome partitions and regulatory factors . In this work , analysis of the Markov State Model under thermal annealing shows the key role played by the ruggedness of the contact energy landscape in shaping chromosome structural organization . Specifically , metastability analysis of the Markov State Model associated with the chromatin interaction network allowed us to identify levels of structural hierarchy and to observe structural units—partitions of different scales . These structural partitions serve as a coarse-grained description of chromosomes , which form the basis for introducing the notion of intra- and inter-chromosomal network of effective interactions . The analysis of effective interaction networks across levels of hierarchy in individual chromosomes shows that chromosomes adopt highly varied topologies . While the lower levels reveal an overall architecture of the folded chromosome , the higher levels can provide structural details in relation to functional organization and regulation of gene expression . Biological insight on the structural organization of chromosomes can be obtained with our method by considering peculiarities in the distributions of transcription and epigenetic factors in eu- and heterochromatic partitions in relation to interactions between them . Partitions at the highest levels of hierarchy may be seen as analogous to TADs , or to the so-called A/B ( active/inactive ) epigenomic compartments [72] . In the future , with the development of common standards and benchmarks by the community , it would be important to compare results and insight obtained from various genomic segmentation approaches . In this work , however , we based our analysis on the obtained sets of partitions , showing how studying distributions of activating and silencing histone modifications in these partitions can help to understand the role of structural organization of chromosomes in the regulation of gene expression . Shifting our focus from structural analysis of individual chromosomes and the functional involvement of partitions to whole-genome architecture , we considered the set of 539 partitions obtained at high levels of hierarchy in corresponding chromosomes with effective interactions between them , which were obtained by adopting a fast “mean field” approximation . In the context of the “partition space” , an analysis of genome-wide effective interactions provides a blueprint of inter-chromosomal contacts , showing that despite the strong crowding of partitions in chromosome territories , most chromosomes are significantly connected with each other , giving rise to a bulky cluster in the core of the effective interaction network . The strongest interactions were observed between chromosomes 14–22 , which are characterized by small chromosome sizes . Heterochromatic partitions are apparently mostly involved in the formation of chromosomal territories , interacting within the corresponding chromosomes and providing structural integrity , and showing only low levels of activating factors . On the other hand , most of the inter-chromosomal juncture partitions , while relatively small in size , are enriched with CTCF , H3K9ac , and DNase-Seq , which may lead one to conclude that these partitions are involved in the formation of inter-chromosomal active epigenomic compartments [72] . Correlations of effective interactions between partitions with distributions of epigenetic factors in these partitions show that: ( i ) most active regulation apparently takes place in pairs of interacting euchromatic partitions; ( ii ) DNA accessibility , CTCF and activating histone modifications H3K4me1 , H3K9ac , and H3K27ac are major potential contributors in the regulation of genome function . Additional biological insight was obtained by determining partitions that may form transient function-related contacts , thereby triggering alternate chromatin states . To this end , the affinity measure was introduced here to evaluate the level of association between partitions , so as to identify partitions with functionally-related interactions . Irrespective of the effective interaction strength , high affinity between partitions point to the mutual functional involvement of corresponding partitions . Since different factors are likely to play dominant roles in different genomic regions , our affinity analysis is complementary to the concept of chromatin assortativity introduced by Pancaldi et al . [44] , which may identify epigenetic factors associated with multiple high-affinity communities across the whole genome . There are different challenges in extending the original analyses of the Hi-C data to exploring the structure-function relationships in the genome . Several previous studies using a hierarchical clustering approach for the analysis of Hi-C data are based on the a priori assumption of the existence of structural hierarchy in chromatin [38–40] . While the work by Boulos et al . is free from such an assumption [37] , it employs a tunable scale parameter in establishing the hierarchy . Our approach is based on an energy landscape representation of the chromatin interaction network , which is formalized and explored via a Markov State Model . Metastability analysis of the Markov State Model allows one to detect natural levels of hierarchy in chromatin structure . The novelty of our approach does not free it , however , from certain limitations . For example , the “mean-field” approximation for coarse-graining of whole-genome interactions , rooted in the computational challenge in calculating committor probabilities on very large networks , is currently a necessary step for processing massive whole-genome datasets . On the other hand , because of its rapid computational time , this approximation allows us to explore higher resolution Hi-C datasets and to obtain partitions of smaller sizes and , ultimately , uncovering higher levels of hierarchy . Further , as the rugged morphology of the energy landscape is key to determining chromosomal partitions , the effect of noise associated with the Hi-C data is of critical importance . For example , all contact frequencies and derived quantities are affected by the characteristic noise , since most Hi-C experiments are conducted on unsynchronized cell populations . While a possible solution would be to study single-cell Hi-C [73] data , which shows great promise in capturing differences between transient states in chromatin organization , the current protocol yields too few interaction pairs for a meaningful analysis of the interaction network . Although specialized variants of the Hi-C protocol , such as capture Hi-C ( cHi-C ) [74] , do not provide a full view of the physical organization of chromatin , they can nonetheless be useful in targeting specific subsets of genomic loci , such as promoters . The biological implications of our analysis , particularly on the relationship between factor enrichment and effective interaction strength and affinity , may also be strengthened by incorporating additional experimental approaches , such as ChIA-PET [75] and HiChIP [76] , which identify interacting genomic elements that are concurrently associated with specific binding proteins . Genome architecture mapping ( GAM ) , a newly devised experimental protocol that determines the frequency at which genomic loci lie on the same spatial plane by sequencing fragments isolated in cryosections of the nucleus [77] , can be a great source of constraints for future 3D whole-genome reconstruction . To conclude , there is no doubt that scientific interest in chromatin structure will continue to drive the development of a variety of specialized experiments and computational approaches in the field of 3D genomics . The method presented here , aimed at detecting and characterizing the hierarchical organization of chromatin , is a step towards unravelling causal relationships in chromatin structure and dynamics of function-related transient molecular phenotypes . The great potential of new experimental data combined with constant methodological improvement are critical in the quest for a more detailed understanding of chromatin architecture , 3D reconstruction , dynamics , and epigenetic regulation .
No human or animal subjects and/or tissue were used in the work . Ethics rules of the Bioinformatics Institute , A*STAR were followed during the work on the project and preparation of the paper . In the following , a Markov jump process is introduced to describe a random walk in the chromatin interaction network , where a probe connects pairs of interacting genomic loci , which represent the states of the Markov State Model ( MSM ) . We first focus on a single chromosome c and denote the corresponding matrix element of Hi-C counts fij for a pair of loci ( i , j ) . We define a pairwise interaction pseudo-energy Eij = −lnfij , which characterizes a strength of interaction between a pair of loci ( i , j ) : the higher the observed counts the more stable the corresponding interactions ( lower pseudo-energy ) are . Therefore , a Maxwell-Boltzmann probability distribution of counts is defined as πij ( β ) =1Z ( β ) exp ( −βEij ) ( 1 ) where Z ( β ) = ∑ ( i , j ) ∈c exp ( −BEij ) is the partition function and β is the thermal parameter ( inverse of a temperature ) . Eq 1 evaluates the joint interaction probability of a pair of loci ( i , j ) , which can be modulated by the thermal parameter β . For low values of β ( high temperature ) , the interaction energies tend to contribute equally in the exponential , whereas for high values β ( annealing , low temperature ) only the highly interacting loci contribute significantly in the exponential . The transition probability associated with the Markov jump process is defined by the following conditional probability pij=πijμi ( 2 ) where μi=∑j∈cπij ( 3 ) is the probability for the genomic locus i to form any interaction in chromosome c . Additionally , μi is the steady state distribution of the transition matrix pij . The transition probability matrix in Eq 2 uniquely identifies a discrete time Markov jump process that governs the trajectories of a random walker across the state space . A random walker is interpreted as a probe particle traveling between genomic loci , for instance a protein such as a transcription factor . Accordingly , the steady state distribution μi can be interpreted as a distribution of probes in a locus i , whereas the distribution πijc in Eq 1 describes an undirected flux of probes connecting the loci i and j . The kinetic distance between pairs of loci is the mean first passage time ( MFPT ) , the mean number of discrete steps τij between two different genomic loci ( i , j ) in chromosome c , which is obtained by solving the system of equations [78] τij=pij+∑k≠i , jpik ( 1+τkj ) ( 4 ) If the departure and arrival states coincide , the MFPT is called mean recurrence time MRT τi , which gives the mean time for a walker to return to its initial state i . The MRT is obtained from the MFPTs in Eq 4 via the formula τi=pii+∑k≠ipik ( 1+τki ) ( 5 ) S7 Fig shows the MFPT matrices ( with the MRTs in diagonal ) for chromosomes 1 , 17 , and 20 at β = 1 ( left ) compared to annealing condition at high β ( right ) . A separation of time scales emerges upon increasing the β parameter , which is reflected in the partitioning of the MFPT matrices . The squares depicted in the annealed MFPT matrices identify sets of pairs ( i , j ) with a similar τij , which are the results of the partitioning in the network of interactions . S8 Fig shows the MFPT matrices for all chromosomes at corresponding high β . For each chromosome , the value of β was chosen as large as possible to observe the fine partitioning structure in the chromosome , while at the same time avoiding singular values in the calculations of the MFPTs and MRTs . The values of β used are listed in S1 Table . The optimization of the metastability index upon annealing conditions allows one to obtain an optimal hub set M . It has been shown that in a MSM with a large state space , an optimal hub set and its corresponding partitions can be used for reducing the state space and obtaining a smaller MSM [59] . In the context of our model of chromosome interactions , we introduce a scheme for coarse-graining chromatin structure and quantifying effective interactions between the obtained partitions . In the previous section , we have described the interaction network of a chromosome c in terms of a MSM with transition matrix pij ( Eq 2 ) , representing the probability for a probe to reach locus j from locus i , with the steady-state distribution of probes μi in locus i ( Eq 3 ) , and with the undirected flux of probes πij between loci i and j defined in Eq 1 . Considering the optimal hub set M with its associated committor probability qa ( i ) in the chromosome c , the effective flux of probes between any two soft-partitions a , b is given by Fab=∑i∈cqa ( i ) πib ( 11 ) where πib is the undirected flux of probes between the loci i and b ( Eq 1 ) in chromosome c . In other words , Eq 11 measures the portion of flux between any locus i and hub b passing through hub a ( see S2 Fig for illustration of the notion of effective interactions ) . Eq 11 is an exact calculation on a single chromosome . We are also interested in evaluating the effective fluxes between hub loci in different chromosomes . However , Eq 11 cannot be simply extended to the whole genome as the committor is by construction qa ( i ) = 0 for any locus i ∉ c . As computational limitations do not allow us to calculate the exact committor for the entire genome , a mean field formulation of Eq 11 was used to estimate the effective flux between any two partitions A , B in the genome . To this end , the effective flux between partitions , irrespective of the chromosomes to which they belong is calculated as FAB=∑i∈gθA ( i ) ∑j∈gπijθB ( j ) , ( 12 ) where the summations are carried over the entire genome g , πij is the flux of probes between any pair of i and j in the genome ( Eq 1 with fij the Hi-C matrix of paired-end reads of counts is now extended to the entire genome ) , and θA ( i ) the hard-partitioning committor defined in Eq 8 . The rationale of Eq 12 is to efficiently , though indirectly , estimate the flux between any two partitions A , B in terms of all the intermediate pairwise fluxes πij . Within the logic of a MSM , the effective fluxes in Eq 12 serve as a measure of chromatin effective interactions . Chromosome partitions are obtained from the optimal hub set as a result of the metastability analysis upon annealing conditions . They offer a coarse-grained description of the genome as the interactions between partitions are characterized via effective interaction strengths ( Eq 12 ) . Given a genome-wide set of partitions obtained above , a putative reduced model of the major partition interactions can be constructed by directly coarse-graining the matrix of counts fij for the entire genome . The observed joint probability of interaction between two partitions A and B is P ( A∩B ) =∑i∈A∑j∈Bfij∑ ( X , Y ) , X≠Y∑i∈X∑j∈Yfij , A≠B ( 13 ) where the summation in the denominator is carried out on the pairs ( X , Y ) of distinct partitions to ensure proper normalization . Because of the law of total probability , the probability for a partition A to be involved in any interaction other than itself is P ( A ) =∑Y≠AP ( A∩Y ) ( 14 ) which by construction adds up to one over all possible partitions A . In general , in the case of independent partitions , namely with no association between them , the relation P ( A ∩ B ) = P ( A ) P ( B ) would hold for the interaction probability . Therefore , to provide a measure of the degree of association between partitions , we define the following affinity as CAB=P ( A∩B ) P ( A ) P ( B ) ( 15 ) which is a positively defined quantity . This quantity is also known as the observed to expected ratio o/e where P ( A ∩ B ) and P ( A ) P ( B ) are the observed and expected probabilities , respectively . In the case of CAB > 1 where the observed probability exceeds the expected , this is interpreted as a degree of association between partitions , either a contact or functional relationship . On the contrary , if CAB ≤ 1 observed and expected probabilities either coincide or the expected probability exceeds the observed one . These situations are interpreted as either no association ( between partitions CAB = 1 ) or dissociation ( partitions repel each other for CAB < 1 ) . Thus , high values of affinity indicate a high degree of association between partitions , suggesting the presence of active binding and/or co-localization mechanisms . Intra-chromosomal pairs show very high affinities , typically with CAB > 10 , while inter-chromosomal pairs have affinities CAB < 4 . In this work , we analyzed 50kbp in-situ Hi-C interaction maps obtained by Rao et al . [23] for human B lymphocyte cells ( GM12878 , two replicates ) at both single-chromosome and whole-genome levels ( GEO accession GSE63525 ) . Three other datasets listed under the same GEO accession were also analyzed: IMR90 ( lung fibroblast ) , HUVEC ( umbilical vein endothelial cells ) , and HMEC ( mammary epithelial cells ) . Epigenomic data tracks for GM12878 were obtained from the ENCODE Consortium web portal , with signal tracks for transcription factor ChIP-Seq from ENCODE/Stanford/Yale/USC/Harvard , histone ChIP-Seq from ENCODE/Broad Institute , DNase-Seq from ENCODE/OpenChrom ( Duke ) . Z-scored fractions of epigenetic factors were calculated in order to investigate their distributions within partitions . In the single-chromosome case , for a given signal track density xf ( A ) of factor f in a partition A of chromosome c , the Z-scored density of factor f is: Zf ( A ) =xf ( A ) −μfσf ( 16 ) where μf and σf are the weighted mean and standard deviation of densities of factor f across partitions in the chromosome c . For the Z-score calculations on the whole-genome , the weighted mean and standard deviation across all 539 partitions were used . For the network representation of the effective interactions , the force-directed layout in Cytoscape was used [79] with the force constants parametrized as logFAB , where FAB is the effective interaction between partitions ( Eqs 11 and 12 ) . The node sizes are proportional to the partition size or Z-scored epigenetic factor density , respectively . Only partitions of size larger than 2Mbp are shown . Edge width scales with logFAB and only interactions above a certain threshold are shown . For intra-chromosomal networks , width of edges is defined according to fixed thresholds of the interaction strength at each level of hierarchy . In the whole-genome network of effective interactions , given the large number of partition pairs with a wide spread of effective interaction strengths , we classify interaction strengths into discrete levels and ignore weaker interactions . Histograms of the distribution of effective interaction strengths are plotted in S4 Fig , with intra-chromosomal ( red ) , inter-chromosomal ( green ) , and all ( blue ) interactions shown on the same axis . Layers of successively weaker interactions provide finer details to the interaction network structure ( see S4 Fig ) : ( i ) Scaffold-Layer interactions are the strongest 2000 interactions , or the top 1 . 35% of all interactions; ( ii ) Layer 1 interactions comprise the top 1 . 35% to 1 . 5% of all interactions , compared with the Scaffold Layer; ( iii ) Layer 2 interactions represent the top 1 . 5% to 1 . 7% of interactions; ( iv ) Layer 3 interactions represent the top 1 . 7% to 2 . 0% of interactions . In our analysis for the GM12878_primary network , we considered only the scaffold and Layer 1 interactions ( the top 1 . 5% of all interactions ) to be significant . Before performing the MSM analysis for single chromosomes , a Gaussian Filter ( GF ) was employed to reduce the effects of sampling noise and systematic errors in Hi-C data: the matrices of raw interaction counts were convolved with a Gaussian kernel . With the interaction matrix at 50kbp resolution , a width parameter in the Gaussian kernel σ = 200kbp was used , truncated at 4σ . S11 Fig shows a comparison of the raw Hi-C matrices with those after the GF preprocessing on chromosomes 1 , 17 , and 20 . Performing partitioning analysis with and without GF preprocessing showed that both approaches yielded similar hub sets and partitions , and also when σ is varied within reasonable bounds , but the optimization on filtered datasets converged more rapidly . The Gaussian kernel width σ was chosen to balance between retaining structural information and computation speed: while increasing σ improved convergence rate , doing so smears out structural information in the high-resolution interaction matrices . Computation of effective interactions between partitions ( Eq 12 ) is not affected directly by GF as the raw interaction matrices are used for obtaining the πij values . The algorithms used in this study are implemented in a freely available Python package ChromaWalker ( https://bitbucket . org/ZhenWahTan/chromawalker ) , built on the standard SciPy stack of libraries ( NumPy , SciPy , Matplotlib , and Pandas ) , using a serial implementation on CPU . The run time for a full genome at 50kbp resolution , on a 3 . 4GHz Intel Core i7 CPU with 8GB RAM , is approximately 1 week . | A new era in chromatin research started with the availability of Hi-C data and new experimental techniques driving improvements in data resolution enable us to achieve a deeper understanding of the chromatin structure and function , while calling , at the same time , for the development of more advanced analytical methods . Though instrumental in the analysis of Hi-C data , both model-driven polymer models and data-driven statistical approaches are always based on several assumptions and require tweaking parameters . We interpret the Hi-C frequencies of chromatin interactions in terms of pairwise contact energies , obtaining corresponding energy landscape that represents the structure and interactions in chromatin . The ruggedness of this landscape is explored by the random walk of a travelling probe , which is formalized in the framework of a Markov State Model . The multilevel energy landscape induces metastability in the Markov process , revealing the hierarchy of chromatin structural organization . Structural partitions determined by the basins in the energy landscape are , thus , naturally obtained at different levels of hierarchy without any preliminary assumptions . Effective interactions between partitions are evaluated , providing a blueprint of the whole-genome organization and functional interactions , which is further substantiated by mapping of information on gene expression regulators and different epigenetic factors . The notion of affinity between partitions complements the picture by reflecting the degrees of association between partitions , calling for the modelling of chromatin dynamics and exploring its functional modulation . | [
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] | 2018 | Exploring chromatin hierarchical organization via Markov State Modelling |
Chitin synthase and chitinase play crucial roles in chitin biosynthesis and degradation during insect molting . Silencing of Dicer-1 results in reduced levels of mature miRNAs and severely blocks molting in the migratory locust . However , the regulatory mechanism of miRNAs in the molting process of locusts has remained elusive . In this study , we found that in chitin metabolism , two crucial enzymes , chitin synthase ( CHS ) and chitinase ( CHT ) were regulated by miR-71 and miR-263 during nymph molting . The coding sequence of CHS1 and the 3’-untranslated region of CHT10 contain functional binding sites for miR-71 and miR-263 , respectively . miR-71/miR-263 displayed cellular co-localization with their target genes in epidermal cells and directly interacted with CHS1 and CHT10 in the locust integument , respectively . Injections of miR-71 and miR-263 agomirs suppressed the expression of CHS1 and CHT10 , which consequently altered chitin production of new and old cuticles and resulted in a molting-defective phenotype in locusts . Unexpectedly , reduced expression of miR-71 and miR-263 increased CHS1 and CHT10 mRNA expression and led to molting defects similar to those induced by miRNA delivery . This study reveals a novel function and balancing modulation pattern of two miRNAs in chitin biosynthesis and degradation , and it provides insight into the underlying molecular mechanisms of the molting process in locusts .
Molting is a crucial process in insect growth and development [1 , 2] . Chitin , as a vital component of the cuticle of the epidermis , plays key roles in maintaining morphology and the molting process [3] . Because chitin is absent in plants and vertebrates , and insect growth and development are strictly dependent on chitin biosynthesis and degradation , chitin metabolism represents an attractive target for developing safe and effective insecticides [4] . The migratory locust Locusta migratoria , a worldwide insect pest species , undergoes five molting stages in its life cycle [5 , 6] . The chitin-mediated molting process is considered to depend on two crucial genes , chitin synthase ( CHS ) and chitinase ( CHT ) , which are regulated by molting hormone 20-hydroxyecdysone ( 20E ) and juvenile hormone [7 , 8 , 9 , 10 , 11] . Chitin synthases are the key regulatory enzymes for chitin synthesis in insects and represent a specific target of insecticides [12] . The LmCHS1 gene cloned from the migratory locust is expressed specifically in the epidermis during the molting stage . Knockdown of the LmCHS1 gene increases the number of non-molting and abnormal molting nymphs [6] . However , another paralog LmCHS2 contributes to the biosynthesis of chitin associated with the peritrophic matrix [13] . Moreover , chitinases are hydrolytic enzymes that are required for the degradation of glycosidic bonds of chitin [14] . TcCHT10 prevents larval molting and plays a vital role during the molting process at all developmental stages; the other paralogs , CHT5 and CHT7 , prevent molting and wings from folding properly only in adults [15 , 16] . An interesting feature of CHS1 and CHT10 in locusts is that the abrupt increase and decrease in transcript levels at the end of every nymph stage ( before molting ) suggest that the two key enzymes are likely precisely modulated in the molting process . However , the underlying regulatory molecular mechanisms of enzyme-dependent chitin metabolism and the molting process have remained elusive . MicroRNAs ( miRNAs ) , small non-coding regulatory RNAs , have emerged as key posttranscriptional regulators of gene expression in multiple biological processes [17] because they can directly trigger translational repression or mRNA degradation by low complementary base-pairing with the 3’UTRs of the target genes [18 , 19] . However , recent studies have shown that miRNAs can extensively target the protein-coding region of mRNAs in animals or insects [20 , 21 , 22] . Many studies have shown that miRNAs critically affect the molting of insects , thus resulting in molting defect phenotypes . For example , miR-8-5p and miR-2a-3p negatively regulate membrane-bound trehalase and phosphoacetylglucosamine mutase of the chitin biosynthesis pathway , leading to a significant reduction in survival rate along with a molting defect phenotype in the hemipteran insect Nilaparvata lugens [23] . Several distinct miRNAs have been approved in the regulation of insect metamorphosis . The loss of miR-2 up-regulates Kr-h1 mRNA , thereby leading to impaired metamorphosis [24 , 25] . Additionally , let-7 and miR-125 mutants induce temporal mis-regulation of specific metamorphic processes in Drosophila [26] . In the migratory locust , we reported that depletion of Dicer-1 , the enzyme that catalyzes the final step of miRNA biosynthesis , induced a molting defect [27] . Results indicated that miRNAs play a crucial role in regulating the molting process of locusts . However , the mechanism regarding how miRNAs affect posttranscriptional modifications in the molting process has not yet been fully elucidated . Considering that CHS1 and CHT10 are crucial molt-dependent enzymes that balance chitin metabolism in many insect species [15 , 16 , 28 , 29] , we chose CHS1 and CHT10 as candidate genes . We hypothesized that miRNAs might play essential roles in the regulation of CHS1- and CHT10-mediated molting processes . In this study , we performed small RNA transcriptome sequencing to identify expressed miRNAs in the integument of locusts . We found that the integument-expressed miR-71 and miR-263 directly target the two key genes CHS1 and CHT10 and regulate chitin production during the molting process , resulting in the successful molting of the migratory locust . Our results reveal a molecular mechanism by which miRNAs play a role in balancing the modulation of CHS1- and CHT10-dependent chitin metabolism during molting .
To identify the miRNAs associated with molting , we sequenced a transcriptome of small RNAs of the locust integument , which is an important tissue during the molting process in insects . A total of 15 , 459 , 187 sequencing reads were obtained , of which 4 , 590 , 268 ( 29 . 7% ) corresponding to mature and star strands were mapped to the known miRNA precursors of locusts [30] . Forty-five conserved miRNAs showed transcriptional activities ( reads per million threshold 1 ) in the integument of locusts . Their expression levels varied over several orders of magnitude . The top ten most highly expressed miRNAs were miR-9b , miR-184 , miR-14 , miR-100 , bantam , miR-71 , miR-275 , miR-305 , miR-263 and miR-279b ( Fig 1A ) . All of the expressed miRNAs were used for further miRNA candidate screening . CHS1 and CHT10 involved in chitin metabolism have been confirmed to regulate the insect molting process [15 , 16 , 28 , 29] . Using the miRanda software , we predicted the expressed miRNAs that could potentially bind to LmCHS1 and LmCHT10 . Thirteen miRNAs exhibited potential target sites in the 3’UTR and CDS regions of LmCHS1 , and 6 miRNAs possessed target sites located in the 3’UTR of LmCHT10 in locusts ( Fig 1B , S1 Table , and S2 Table ) . An additional prediction software , RNAhybrid , was used to further improve the target prediction efficiency . The RNAhybrid program also identified LmCHS1 and LmCHT10 as potential targets for miR-71 and miR-263 , respectively ( S1 Fig ) . Furthermore , we confirmed the absence of miR-71 and miR-263 binding sites in the Tweedle , Cryptocephal , Obstructor , Knickkopf , and ecd1 genes to exclude the other possible miR-71/miR-263 targets [31 , 32 , 33 , 34 , 35] , which can lead to molting defects similar to those caused by LmCHS1 and LmCHT10 . To confirm the correlation of the expression pattern between LmCHS1 , LmCHT10 and their target miRNAs , we performed stem-loop quantitative reverse transcriptase-polymerase chain reaction ( qRT-PCR ) to quantify the expression levels of these predicted miRNAs and target genes in the integument of second-instar nymphs ( S2 Fig ) . The overall expression of miRNAs ( miR-71 or miR-263 ) and that of the target genes ( LmCHS1 or LmCHT10 ) exhibited opposite patterns during the second nymph stage ( Fig 1C and 1D ) . The miR-71 expression levels showed the opposite wave-like pattern of miR-263 expression levels , with the highest level occurring at the mid-stage for miR-71 , whereas miR-263 expression decreased to the lowest level ( Fig 1D ) . In contrast , the mRNA expression of LmCHS1 was down-regulated at the mid-stage and up-regulated at the early and late stages . However , the mRNA expression of LmCHT10 was suppressed at the early and late stages and was promoted at the mid-stage ( Fig 1C ) . These data indicate that miR-71/miR263 expression is negatively correlated with LmCHS1 and LmCHT10 expression during new integument formation in the nymph stages . The results imply that there is a possible regulatory relationship between the miRNAs and the genes . To confirm the interactions of miR-71 , miR-263 and their targeting genes in vitro , we performed reporter assays using luciferase constructs fused to the coding region of LmCHS1 and the 3’UTR of LmCHT10 . Compared with the agomir control ( agomir-NC ) , the constructs with either the LmCHS1 or LmCHT10 binding sites produced lower luciferase activity when co-transfected with miR-71 or miR-263 agomir , respectively , in S2 cells ( Fig 2A and 2B ) . When the regions homologous to the “seed” sequence of miR-71 and miR-263 were mutated in the LmCHS1 and LmCHT10 reporter constructs , the luciferase activity returned to levels similar to those produced by mock transfection with the empty reporter plasmid ( Fig 2A and 2B ) . However , the luciferase activity of sites transfected with miR-252 , whose expression is negatively correlated with LmCHS1 , showed no change compared with the control ( S3 Fig ) . To further validate the effect of endogenous miR-71 and miR-263 in S2 cells on the luciferase activity , we investigated miRNA-71 and miR-263 levels in S2 cells . The mir-71 homolog was not detected in the fly genome [36] . The small RNA transcriptome data indicated that only a few reads for miR-263 ( 7 counts in GEO accession GSM272651 and 1 count in GEO accession GSM272652 ) were detected in S2 cells , implying a limited expression of miR-263 in S2 cells . We examined luciferase activity in S2 cells with antagomir-263 . The luciferase signals of the CHT10 construct incubated with antagomir-263 did not vary significantly compared with those of the control ( S4 Fig ) . The data suggested that the endogenously expressed miR-263 did not affect the luciferase assay results for the locust miR-263 . Thus , the predicted miRNA binding sites in LmCHS1 and LmCHT10 are functional and might be targeted by miR-71 and miR-263 , respectively , in S2 cells . Ago1 , as a RNA binding protein , is a core component of RISC involved in miRNA-mediated gene silencing . Anti-Ago1 RIP is a biochemical approach to identify the composition and organization of endogenous mRNAs , miRNAs associated with Ago1 proteins . This approach is widely used in interaction validation between miRNA and its target in vivo . We then performed an RNA immunoprecipitation assay in the integument to examine the interactions of miR-71 and miR-263 with their targeting genes in vivo ( Fig 2C and 2D ) . LmCHS1 or LmCHT10 were significantly enriched in the Ago1-immunoprecipitated RNAs from the integuments treated with agomir-71 or agomir-263 compared with those treated with agomir-NC . These results indicated that miR-71 and miR-263 directly regulate LmCHS1 and LmCHT10 in the locust integument , respectively . To determine whether miR-71/miR-263 were co-localized in the locust integument , we performed in situ analyses of miRNA-71/miR-263 and their targets by miRNA/mRNA fluorescence in situ hybridization ( FISH ) . Indeed , we found that miR-71 and LmCHS1 as well as miR-263 and LmCHT10 were both widely detected in the epidermal cells of the locust integument ( Fig 2E ) . Specifically , miR-71 is co-localized with LmCHS1 and miR-263 is co-localized with LmCHT10 in cells of the integument . The results suggest that in the locust integument , LmCHS1 and LmCHT10 interact directly with miR-71 and miR-263 , respectively , in a spatial manner . To determine the effects of miR-71 and miR263 on their target genes in vivo , we detected the expression levels of LmCHS1 and LmCHT10 after miRNA agomir ( overexpression ) or antagomir ( knockdown ) administration in the locust integument . We first assessed the miRNA expression changes by injecting locusts with miRNA agomir or antagomir in vivo to confirm the delivery efficiency of miRNA administration . The qPCR results showed that miR-71 and miR-263 levels were significantly induced and depleted by their agomir and antagomir treatments , respectively . As expected , the treatment had no effect on the expression of the negative control , let-7 ( S5 Fig ) . Moreover , the mRNA levels of LmCHS1 decreased by approximately 60% compared to those in the control locusts after agomir-71 injection ( Fig 3A ) . Additionally , antagomir-71 injection resulted in a significant up-regulation of the mRNA expression level of LmCHS1 ( Fig 3B ) . In contrast , inhibition of approximately 55% of LmCHT10 expression was observed after agomir-263 injection . Additionally , the mRNA levels of LmCHT10 were up-regulated by miR-263 knockdown ( Fig 3A and 3B ) . No significant effects on the expression of paralog genes LmCHS2 and LmCHT5 upon miR-71/miR-263 administration were observed , suggesting that the agomir/antagomir injection specifically acted on the target genes ( S6 Fig ) . Additionally , we sought to determine whether the administration of miR-71 had any effects on the expression of miR-263 , or vice versa . The results indicated that there was no interaction between miR-71 and miR-263 , which were involved in the regulation of two distinct processes ( chitin synthesis and degradation ) ( S7 Fig ) . Since 20E is believed to primarily regulate insect growth and development processes , including molting [2 , 37] , we further used 20E treatments to investigate whether 20E might play a relevant role in the regulation of the expression of miR-71 and miR-263 . The results indicated that 20E treatment depressed the expression levels of miR-263 , but did not have significant effects on miR-71 expression level ( S8 Fig ) . Because LmCHS1 and LmCHT10 are essential genes in chitin synthesis and degradation , we determined the effects of miR-71 and miR-263 on chitin production in the integuments after miRNA administration in vivo . The administration of the miR-71 agomir reduced chitin production by approximately 48% ( Fig 3C ) . In contrast , miR-71 knockdown significantly increased the chitin content ( by approximately 18% , p < 0 . 05 ) of the locusts . Accordingly , a significant increase in chitin content was observed after agomir-263 manipulation , and antagomir-263 injection decreased chitin content ( Fig 3D ) . Thus , administration of miR-71 and miR-263 may have profound regulatory effects on chitin production and content in vivo in locusts . To determine the function of miR-71 and miR-263 during the molting process , we monitored the molting of locusts after agomir-71 or agomir-263 injection , respectively . The locusts injected with agomir-71 or agomir-263 displayed a distinct molting defect phenotype . In total , of 25 nymphs injected with agomir-71 , 12 ( 48% ) died during the molting process from second instar to third instar , whereas only 8 . 7% ( 2 out of 23 ) of the control nymphs died during this process ( Fig 4A ) . Similarly , after injection of agomir-263 , the mortality reached 40 . 7% , which was significantly higher than that of the control ( only 7 . 4% ) during the molting process ( Fig 4C ) . In parallel , miR-71 and miR263 knockdown caused by injection of antagomir-71 or antagomir-263 resulted in incomplete ecdysis , with 24 . 0% and 20 . 7% mortality , respectively ( Fig 4B and 4D ) . After nymphs were injected with the miR-71 and miR-263 agomir or antagomir , the nymphs exhibited abnormal and unsuccessful molting ( Fig 4E–4H ) , in which a certain amount of the old cuticle was separated from the body but not detached from the body to any extent . Moreover , some nymphs showed a molting defect , in which legs failed to slough from the old cuticle , and other nymphs died without obvious molting defects due to molting arrest ( Fig 4E–4H ) . Treatment of locusts with agomir-71 and agomir-263 resulted in the down-regulation of LmCHS1 and LmCHT10 expression and a corresponding change in chitin content , thereby generating a significant molting defect ( Fig 4I ) . To further determine whether the abnormal layer of the cuticle was responsible for the molting defect induced by miR-71 and miR-263 , we performed hematoxylin and eosin staining and chitin staining in the integument by injecting agomir-71 or agomir-263 into the locusts ( Fig 5A and 5C ) . A significant decrease in chitin content occurred in response to miR-71 overexpression in the newly formed cuticle , which exhibited a severe deficiency due to diminished chitin synthesis ( Fig 5A ) . Similarly , RNAi for LmCHS1 prevented the synthesis of cuticle chitin , as expected in the newly formed cuticle ( Fig 5B and 5D ) . Conversely , miR-263 overexpression inhibited the degradation of the old cuticle , the chitin of which was not diminished compared with that of the agomir controls ( ig 5A and 5C ) . Consistent with the miR-263-mediated phenotype , knockdown of LmCHT10 transcripts hindered chitin degradation of the old cuticle , leading to a dramatically thickened layer of the old cuticle and impeding the shedding of the old cuticle during molting ( Fig 5B and 5D ) . Therefore , the change in chitin content of new/old cuticle regulated by miR-71 and miR-263 is a key mediator of defective molting .
Our previous studies confirmed that depletion of Dicer-1 prevented the molting process [38] and chitin-metabolic genes , including LmCHS and LmCHT , are related to molting in the migratory locust [7] . The results presented herein explored the link between the Dicer-mediated phenotype and LmCHS/LmCHT-associated molting . In this study , we found that miR-71 and miR-263 control chitin synthesis and degradation by targeting LmCHS1 and LmCHT10 , resulting in post-transcriptional regulation of the molting process in locusts ( Fig 4I ) . This miRNA-mediated mechanism of chitin metabolism provides insight into the molecular basis of the molting process in locusts . We found that miR-71 targets LmCHS1 and miR-263 targets LmCHT10 in the chitin metabolic pathway of locusts . Chitin synthases and chitinases are responsible for the synthesis or degradation of chitins , which represent two inverse processes [39] . Thus , we suspected that the two processes of chitin synthesis and degradation affect one another . Therefore , we tested whether these two miRNAs interacted with each other using agomir-71/263 treatment . However , this was not the case ( S7 Fig ) . The expression levels of the two miRNAs were very similar to those of the respective controls . In addition , 20-hydroxyecdysone ( 20E ) , as a key steroid hormone , coordinates multiple developmental events involving insect molting and metamorphosis [40] . Although 20E treatment is considered to be correlated with CHS and CHT expression , its roles in the regulation of CHS and CHT remain a matter of controversy . DmeCHS-1 and DmeCHS-2 transcription is activated by 20E during Drosophila metamorphosis [41] . MsCHS-1 gene is negatively controlled by 20E , reflecting a dual effect of 20E [42] . LmCHT5 and TmCHT5 gene expression can be induced by 20E during the molting process [43 , 44] . However , the pathway linking 20E to CHS or CHT is still largely unknown . miR-8-5p and miR-2a-3p act as molecular regulators that tune the chitin biosynthesis pathway in response to 20E [45] . Thereby , we wondered whether 20E might play a role in the regulation of the expression of miR-71 and miR-263 , leading to the precise expression of LmCHS1 and LmCHT10 . The 20E treatment inhibited miR-263 expression and induced LmCHT10 expression but did not affect the expression levels of miR-71 or LmCHS1 ( S1 Fig ) . Thus , the 20E-miR-263-CHT10 axis may switch the degradation of chitin on and off , whereas miR-71-CHS1 axis-mediated chitin synthesis is regulated by mechanisms other than 20E . The regulatory function of miR-263 is conserved across a broad range of insect species . We performed miRNA target prediction in other insect species for which CHT10/CHS1 UTR sequences were available in the NCBI GenBank . The prediction results revealed that miRNA-263 binding sites of CHT10 are present in several holometabolous insects including flies , beetles and mosquitoes , whereas no binding sites for miR-71 were identified in other insect species ( S3 Table ) . This finding suggests that these regulatory interactions have been evolutionarily conserved , indicating that there is selective pressure to maintain the regulatory interactions of miR-263 and CHT10 across species . Previous studies have shown that the expression patterns of CHT10 during developmental instar stages are quite similar among insects [15 , 46] , suggesting a common regulatory mechanism of the miRNA-263-dependent molting process . Our study provided experimental evidence that this regulatory mechanism is also present in hemimetabolous locusts . Members of Orthoptera occupy a more basal position in the insect lineage relative to holometabolous insects [47 , 48] . Thus , we presume that the regulatory roles of miR-263 in the molting process represent an ancestral function in insects that perhaps originated with the emergence of the common ancestor of hemimetabolous and holometabolous insects . miRNAs are precise regulators that are able to sharpen developmental transcription by increasing and reducing target expression to meet developmental demands [49] . Due to the reduced expression of CHS1 and CHT10 , the agomir treatments of miRNA-71 and miR-263 can cause severe deficiency of new cuticle synthesis and failed degradation of the old cuticle , respectively . Unexpectedly , the antagomir treatments of miR-71/miR-263 and the resulting up-regulation of their target genes resulted in a similar aberrant phenotype , indicating that the elevated ectopic expression of CHS1/CHT10 is also detrimental to the molting process . These results indicate that extremely high or low expression of CHS1 and CHT10 during a critical period of the molting process can result in the development of an aberrant molting phenotype . miRNAs could tune the transcriptional activities of target genes to physiologically relevant levels [50] . miR-71/miR-263 could directly interact with CHS1/CHT10 and play a role in ensuring an accurate level of their expression . The precise interactions of miR-71/miR-263 and CHS1/CHT10 regulate the molting process in a spatio-temporal manner . Taken together , our results show that the transcriptional activities of CHS1 and CHT10 are tuned to a precise level at which they can execute proper function , emphasizing the important roles of miRNA-mediated precise regulation in the molting process . The conclusion that emerges is that two miRNAs control the molting process by precisely regulating chitin metabolism . miR-71 and miR-263 suppress CHS1 and CHT10 transcript levels , thus preventing the progression of the molt to the next stage . This miRNA-mediated post-transcriptional regulation of chitin metabolism is particularly significant for understanding the molting process of locusts and potentially provides new targets for controlling locust plagues worldwide .
Locusts were obtained from the same locust colonies , which were maintained at the Institute of Zoology , Chinese Academy of Sciences , China . Nymphs were reared under a 14:10 light/dark photo regime at 30 ± 2°C and were fed fresh wheat seedlings and bran . Total RNA was extracted using TRIzol ( Invitrogen ) and treated with DNase I following the manufacturer’s instructions . The RNA concentration and purity were assessed in an Agilent 2100 Bioanalyzer ( Agilent ) to verify RNA integrity . Small RNA libraries were constructed using a TruSeq small RNA sample preparation kit ( Illumina ) . Briefly , the 3’ and 5’ RNA adapters were ligated to the corresponding ends of small RNAs . Following adapter ligation , the ligated RNA fragments were reverse transcribed using M-MLV reverse transcriptase ( Invitrogen ) . The resulting cDNA products were PCR amplified with two primers that were complementary to the ends of the adapter sequences . The PCR amplicons were separated by size in 6% Novex polyacrylamide gel for miRNA enrichment and sequenced on an Illumina Genome Analyzer IIx sequencing system . Using the Cutadapt software , we trimmed the low-quality reads and the reads showing sequence similarity to adaptor sequences at the start or end terminals . The quantifier module in the miRDeep ( version 2 . 0 . 0 . 5 ) software package was used to measure expression levels based on read counts . The ~300-bp sequences of the CDS and the 3′ UTR surrounding the predicted miR-71 and miR-263 target sites in CHS1 and CHT10 , respectively , were separately cloned into the psiCHECK-2 vector ( Promega ) using the XhoI and NotI sites . To generate the mutation version , the 8 nt of binding sites were mutated ( GTTTTTCA for CHS1; GTGCTATT for CHT10 ) , which include the region complementary to the miR-71 and miR-263 seed . S2 cells were co-transfected with 800 ng of the luciferase reporter vector or the empty vector and agomir-71 ( -263 ) at a 1:4 ratio using the Lipofectamine™ 2000 reagent ( Invitrogen ) according to the manufacturer’s instructions . The activities of the firefly and Renilla luciferases were measured 48 h after transfection with the Dual-Glo Luciferase Assay System ( Promega ) using a luminometer ( Promega ) . Results are expressed as the ratio Renilla/firefly luciferase activity ( mean ± SEM ) based on six independent replicates . The miRNA agomir or antagomir , each of which is a stable miRNA mimic or inhibitor , was used to validate the function of the miRNA in vivo . Briefly , 210 pmol of agomir-71 ( -263 ) or antagomir-71 ( -263 ) ( 500 μM; RiboBio ) was injected into the thoracic hemocoels of second-stadium nymphs two times at 48 h intervals . The agomir or antagomir negative controls ( 500 μM ) were also injected into the locust thoracic hemocoels ( RiboBio ) . All injections were administered using a nanoliter injector ( World Precision Instruments ) with a glass micropipette tip . Treated nymphs were subjected to phenotypic observation of molting process . Their integuments were harvested , snap-frozen , and stored at -80°C . Total RNA enriched for small RNAs was isolated from integuments using the mirVana miRNA isolation kit ( Ambion ) . Moloney murine leukemia virus ( M-MLV ) reverse transcriptase ( Promega ) and a miRNA first-strand cDNA synthesis kit ( Ambion ) were used to prepare the Oligo ( dT ) -primed cDNA and stem-loop cDNA , respectively . The miRNAs and mRNAs were subjected to qPCR using the SYBR Green miRNA expression and gene expression assays , respectively , according to the manufacturer’s instructions ( Tiangen ) ; qPCR was performed on a LightCycler® 480 instrument ( Roche ) . The PCR data were analyzed using the 2−ΔΔCt method of relative quantification . As endogenous controls , U6 snRNA and ribosomal protein RP49 were used to quantify the miRNA and mRNA expression levels , respectively . Dissociation curves were determined for each miRNA and mRNA to confirm unique amplification . The qPCR primers are listed in S4 Table . All the qRT-PCR reactions were performed in six biological replicates . Four integuments were involved in one biological replicate . A combined two-color fluorescence in situ analysis of miRNA-71 ( -263 ) and its targets was performed on the integuments of second-instar nymphs by co-labeling of the miRNA and its target according to the method described by Nuovo et al . [51] . An antisense locked nucleic acid ( LNA ) detection probe for miR-71 , miR-263 or a scrambled control ( Exiqon ) was labeled with double digoxigenin . Biotin-labeled antisense and sense probes of CHS1 and CHT10 were generated from linearized recombinant pGEM-T Easy plasmids using the T7/SP6 RNA transcription system ( Roche , Basel , Switzerland ) following the recommended protocols . Based on the timepoint at which higher expression activities were observed for miR-71 and miR-263 , we selected the nymphs on the third day of the 2nd instar stage for miR-71 and CHS1 co-localization detection and the nymphs on the fifth day of the 2nd instar stage for miR-263 and CHT10 co-localisation detection , respectively . The integuments were fixed in 4% paraformaldehyde overnight . The paraffin-embedded integument tissue slides ( 5 μm thick ) were deparaffinized in xylene , rehydrated with an ethanol gradient , digested with 20 μg/mL proteinase K ( Roche ) at 37°C for 15 min , and incubated with the LNA miRNA probes and its target RNA probe at 60°C for 5 min . The slides were then hybridized for 7–15 h at 37°C and washed in 0 . 2× SSC and 2% BSA at 4°C for 5 min . The slides were incubated in anti-digoxigenin–alkaline phosphatase conjugate ( 1:150 dilution ) for 30 min at 37°C , followed by incubation with the HNPP substrate . For biotin-labeled probes , a TSA kit ( Perkin Elmer , MA , USA ) including a streptavidin horse radish peroxidase-conjugate and fluorescein tyramide substrate was used . The signals of the miRNA and its target were detected using an LSM 710 confocal fluorescence microscope ( Zeiss ) . The primes for probe synthesis of CHS1 and CHT10 are listed in S4 Table . The RIP assay was performed using a Magna RIP Quad kit ( Millipore ) according to the manufacturer’s instructions , with slight modifications . The 2-day-old second instar nymphs were microinjected with agomir-71 or agomir-263 . A scrambled miRNA agomir was used as a negative control . Treated nymphs were subjected to RIP analysis 48 h later . Eight integuments of abdomen were collected and homogenized in ice-cold RIP lysis buffer . The homogenates were stored at -80°C overnight . A total of 5 μg of Ago-1 antibody ( Abmart ) or normal mouse IgG ( Millipore ) , which was used as a negative control , was pre-incubated with magnetic beads . The frozen homogenates in the RIP lysates were thawed and centrifuged , and the supernatants were incubated with the magnetic bead–antibody complex at 4°C overnight . The immunoprecipitated RNAs were reverse-transcribed into cDNA using random hexamers . qPCR was performed to quantify LmCHS1 and LmCHT10 . The supernatants of the RIP lysates ( input ) and the IgG controls were assayed to normalize the relative expression levels of the target genes . The chitin content of the locust integuments was quantified after miR-71 or miR-263 administration . The integument tissues of the locust nymphs were immediately dissected and stored in liquid nitrogen . Three integuments of locust abdomens were homogenized in liquid nitrogen and transferred to 3% SDS . The homogenates were incubated at 100°C for 15 min; then , 120% KOH was added . The pellets were re-suspended and incubated at 130°C for 1 h . After cooling , 0 . 8 ml ice-cold 75% ethanol was added , and the samples were shaken until the KOH and ethanol formed a single phase . The homogenates were then centrifuged at 4°C , and the supernatants were discarded . The pellets were washed with 40% cold ethanol containing insoluble chitosan . Approximately 50 μl of 10% NaNO2 and 50 μl of 10% KHSO4 were added to each sample , and the samples were centrifuged at 4°C . The supernatants were combined with 20 μl of 12 . 5% NH4SO3NH2 and 20 μl freshly prepared 0 . 5% ( wt/vol ) 3-methyl-2-benzothiazolone hydrazone hydrochloride hydrate solution , and the reaction was heated to 99 . 9°C for 3 min . After cooling , 20 μl of 0 . 83% FeCl3 . 6H2O solution was added to the reaction . Measurements of the reaction mixture were performed using a microplate reader at 650 nm using glucosamine as a standard . To knock down CHS1 and CHT10 , double-stranded RNA ( dsRNA ) was synthesized using T7 RiboMAXTM Express RNAi System ( Promega , USA ) following the manufacturer’s instructions . Each insect was injected with 3 μg of dsRNAs at day 3 of the second instar nymphs . Control nymphs were injected with equivalent volumes of dsGFP alone . Total 25 nymphs were injected with dsRNA for each gene . Nymphs were observed carefully after injection . The nymphs that typically showed abnormal ecdysis were used for subsequent extraction . Six abnormal nymphs and six control nymphs were used for hematoxylin and eosin staining and chitin staining . The SPSS 17 . 0 software ( SPSS Inc . ) was used for statistical analysis . The differences between treatments were compared using either Student’s t-test or one-way analysis of variance ( ANOVA ) followed by Tukey’s test for multiple comparisons . The Mann–Whitney U test was used to analyze the behavioral data due to its non-normal distribution characteristics . p < 0 . 05 was considered statistically significant . All results are expressed as means ± SEM . | Molting is a crucial process in the growth and development in insects . Disturbing the molting process represents an attractive strategy for developing safe and effective insecticides . The migratory locust is a hemimetabolous pest that undergoes five molting stages in its life cycle . Similar molting defects can be observed in expression silencing of the key genes both in miRNA processing and in chitin metabolism . However , any link between a specific miRNA to chitin metabolism has not yet been established . In this study , we elucidated a mechanism by which two miRNAs regulate chitin metabolism related to the molting process . We found that miR-71 and miR-263 directly repress two genes , chitin synthase1 ( CHS1 ) and chitinase10 ( CHT10 ) , which are required for chitin biosynthesis and degradation in chitin metabolism . Manipulation of miR-71 and miR-263 expression blocked molting and resulted in abnormal molting by negatively regulating the expression of LmCHS1 and LmCHT10 . Furthermore , both up-regulation and down-regulation of LmCHS1 and LmCHT10 by miRNA manipulation altered the chitin content of the new cuticle and old cuticles , leading to a similar aberrant molting phenotype . Our results demonstrate a balancing modulation pattern of two miRNAs in chitin biosynthesis and degradation that controlled the precise molting process in locusts . | [
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] | 2016 | miR-71 and miR-263 Jointly Regulate Target Genes Chitin synthase and Chitinase to Control Locust Molting |
We have shown previously that Sp100 ( a component of the ND10 nuclear body ) represses transcription , replication and establishment of incoming human papillomavirus ( HPV ) DNA in the early stages of infection . In this follow up study , we show that Sp100 does not substantially regulate viral infection in the maintenance phase , however at late stages of infection Sp100 interacts with amplifying viral genomes to repress viral processes . We find that Sp100 localizes to HPV16 replication foci generated in primary keratinocytes , to HPV31 replication foci that form in differentiated cells , and to HPV16 replication foci in CIN 1 cervical biopsies . To analyze this further , Sp100 was down regulated by siRNA treatment of differentiating HPV31 containing cells and levels of viral transcription and replication were assessed . This revealed that Sp100 represses viral transcription and replication in differentiated cells . Analysis of Sp100 binding to viral chromatin showed that Sp100 bound across the viral genome , and that binding increased at late stages of infection . Therefore , Sp100 represses the HPV life cycle at both early and late stages of infection .
Human papillomaviruses ( HPVs ) establish a persistent infection in the cutaneous and mucosal epithelia of their hosts [1] . The virus infects the basal layer of keratinocytes through a micro-fissure and establishes a persistent reservoir of infection in these dividing cells . When the infected cells differentiate during the process of tissue renewal , late viral replication and transcription are induced , and viral particles assemble in the most superficial layers of the epithelium . This persistent , differentiation-dependent life cycle requires several different stages of viral DNA replication: immediately upon infection there is a limited amplification of viral DNA; next the viral genome must become “established” in the cell and be maintained at a low copy number as an extrachromosomal replicon for many cell divisions; and finally , the viral genome must amplify to very high levels in differentiated cells [2] . Like many other DNA viruses , the early stages of HPV transcription and replication initiate at , or adjacent to , the nuclear structure , ND10 [3] . During primary infection , the viral minor capsid protein , L2 , delivers the viral DNA to the ND10 body by interaction with the PML protein , and this is important for efficient infection [4 , 5] . Furthermore , L2 causes reorganization of ND10 and the displacement of the ND10 factor , Sp100 [6] . In support of this finding , we have shown previously that Sp100 represses transcription of incoming HPV18 genomes [7] . During the maintenance stage of infection , levels of viral transcription and replication are not dramatically affected by the Sp100 proteins [7 , 8] . In cells containing extrachromosomal HPV18 genomes , we find that downregulation of Sp100 increased viral replication and transcription only ~1 . 5-fold ( this was not of statistical significance ) . Habiger et al . observed a similar increase in HPV31 transcription and replication in CIN612-9E cells , which did reach significance . Furthermore , they showed that interferon ( IFN ) κ induces Sp100 , which in turn represses HPV31 transcription [8] . During the productive stage of the HPV lifecycle , amplification of viral DNA is coincident with epithelial differentiation [9] . This amplification event is marked by a shift in transcriptional initiation from the early to the late promoter [10] . This results in three classes of transcripts: early transcripts that utilize the early promoter and early polyadenylation site; intermediate transcripts that use the late promoter and early polyadenylation site; and late transcripts that use both the late promoter and polyadenylation site [11] . Intermediate transcripts encode E1 , E2 and E4 proteins , and late transcripts encode the capsid proteins , L1 and L2 . The switch between HPV early and late transcription is highly dependent on host cell differentiation , and viral DNA replication is necessary for maximal late transcription [12] . Here we examine the role of Sp100 on viral genome amplification and viral transcription during the productive stage of the viral lifecycle . We observed that Sp100 associates with replication factories formed by expression of HPV16 E1 and E2 in keratinocytes , as well as replication foci formed upon differentiation in the HPV31 containing cervical cell line CIN612-9E . Sp100 is also associated with HPV replication foci at the onset of DNA amplification in the upper layers of a cervical CIN 1 lesion . We observed that Sp100 primarily repressed late HPV31 mRNA transcription , and limited viral replication , in differentiating cells . Using chromatin immunoprecipitation , we show that Sp100 binds across the viral genome and that binding increases upon differentiation . Together , these data show that Sp100 functions as a host restriction factor at both early and late times of the HPV lifecycle .
Nuclear foci that replicate HPV DNA can be formed by coexpression of the viral E1 and E2 proteins , and these have been previously shown to localize adjacent to ND10 bodies [3] . To investigate this further and to determine if Sp100 is also recruited to sites of viral DNA replication , we generated HPV16 replication foci in a human foreskin keratinocyte ( HFK ) conditionally immortalized cell line , 1A , as previously described [13] . 1A cells were transfected with HPV16-E1 ( pMEP9-16 EE-E1 ) and HPV16-E2 ( pMEP4-16 FLAG-E2 ) expression vectors , along with either empty vector DNA ( pKS ) , HPV16 origin-containing DNA ( pKS-16Ori ) or the recircularized HPV16 viral genome . The localization of the viral replication proteins with respect to PML and Sp100 was assessed by confocal microscopy . Both PML and Sp100 localized in nuclear foci ( ND10 bodies ) , which were often observed in association with the E1/E2 foci . In the absence of a viral replicon , PML localized with E1/E2 foci in ~30–40% cells ( Fig 1A , panels i-iv and Fig 1B ) . Introduction of replication competent DNA ( pKS-16Ori or a recircularized HPV16 genome ) increased the association of PML with E1/E2 foci to >95% ( Fig 1A , v-viii and ix-xii; Fig 1B ) . This confirmed that the association of PML with the HPV replication proteins is enhanced in the presence of replicating viral DNA [3] , and showed that Sp100 is also associated with HPV replication foci . PML and Sp100 associated with HPV replication foci in either a satellite association around or adjacent to E1/E2 foci , or internally within the replication factories . This internal association was seen only in the presence of replicating viral DNA and was observed more frequently in the presence of the entire HPV16 genome than the plasmid containing only an HPV16 origin ( Fig 2A , 2B and 2C ) . To better assess whether increased internal association of Sp100 in viral replication factories inhibited viral DNA synthesis , we directly detected HPV16 DNA in the foci by FISH . Similar to the data presented in Fig 2 , Sp100 was arranged in either a satellite configuration around viral DNA replication factories in cells stained for Sp100 and HPV16 DNA , or was found completely inside the entire replication factory ( Fig 3A ) . Further , our analysis of Sp100 localization , in conjunction with 3D image processing , showed that internal Sp100 was closely associated , rather than colocalized with viral DNA in replication foci ( Fig 3B and 3C ) . Notably , most cells that exhibited this internal Sp100 phenotype contained all available Sp100 in a single , and often largest , replication factory ( Fig 3B ) . However , it was difficult to conclude whether Sp100 was repressing viral DNA replication in these factories due to the substantial amounts of viral DNA in these foci . To further analyze the association of PML and Sp100 with replication foci , we examined the cell line , CIN612-9E , which is derived from an HPV31-positive cervical biopsy [14] and is frequently used to study the later stages of the HPV lifecycle [9] . 9E cells were grown on glass coverslips to confluence and cultured for five days in high calcium medium to induce differentiation . This results in amplification of viral DNA in nuclear foci when detected by HPV31 FISH . In CIN612-9E cells , we observed three replication foci phenotypes: cells containing several small nuclear foci of HPV31 DNA; cells with a single large focus of viral DNA; or cells containing a mixture of the two ( Fig 4A and 4B ) . Following differentiation , cells were fixed and combined FISH-IF was performed for HPV31 DNA and the PML and Sp100 proteins . In cells showing multiple , small , punctate FISH signals , almost 100% HPV31 DNA was either partially colocalized or adjacent to both PML and Sp100 nuclear foci ( satellite association ) ( Fig 4C ) . Furthermore , PML and Sp100 staining patterns were nearly identical , indicating a high degree of colocalization between the two proteins . However , in cells with a singular large focus of viral DNA , Sp100 and PML were observed inside the replication factory in ~15% or ~35% cells , respectively , and displayed a satellite association in most of the remaining cells ( Fig 4C ) . When Sp100 was found inside viral replication factories , PML was sometimes retained outside the foci or sometimes internally along with Sp100 . In ~10% cells , large replication foci were devoid of any Sp100 staining but remained associated with PML . We speculate that Sp100 may have been degraded within these large factories to overcome the anti-viral , repressive effects of Sp100 . Using CIN612-9E cells , we can study the regulation of the maintenance and the productive stages of the viral life cycle by directly measuring viral transcription and replication . To address the role of Sp100 in the maintenance phase , we assessed the effect of siRNA depletion of Sp100 on early and intermediate HPV mRNAs , and on viral genome copy number over three cell passes . 9E cells were seeded at low density and treated with either Ctrl ( control ) or Sp100 siRNA 24 hours later . Cells were harvested between 72 hours and 96 hours later and replated at low density . This process was repeated two more times and cellular DNA and RNA were collected at the end of pass 1 and pass 3 ( Fig 5A ) . Levels of E6*I , E1^E4 and E2 viral transcripts and Sp100 depletion efficiency were monitored by qPCR ( Fig 5B ) . This showed efficient downregulation of Sp100 over the three passes , but there was no consistent increase or decrease in viral mRNA after this treatment . Correspondingly , we were unable to observe reproducible changes in viral DNA copy number after three passes of Sp100 downregulation . Similarly , we concluded previously that Sp100 had only minimal effect on the levels of viral RNA and DNA in a cell line containing extrachromosomally replicating HPV18 [7] . Therefore , Sp100 does not affect viral transcription or replication in the maintenance phase of infection . Using CIN612-9E cells , we can also study the transition from the maintenance stage of the viral life cycle to the productive stage . Spink et al . demonstrated that differentiation activated the late viral promoter in CIN612-9E cells [12] , and so we assessed the effect of siRNA depletion of PML and Sp100 on early , intermediate and late HPV mRNAs in the context of differentiation . Proliferating CIN612-9E cells were transfected with siRNA to Sp100 , grown to confluence and cultured in medium containing 1 . 5 mM CaCl2 for three days ( see timeline in Fig 6A ) . siRNA depletion efficiency was monitored by qPCR for Sp100 using primers that detect all spliced transcripts for this gene ( Fig 6B ) . At the end of the experiment ( T = 6 ) , Sp100 mRNA was still reduced 75–85% compared to siCtrl ( Fig 6B ) . Differentiation was also monitored by the measurement of involucrin and filaggrin mRNA ( Fig 6B ) . We observed a substantial inhibition of differentiation in siPML treated cells [15] and so these samples were excluded from further transcriptional analysis . Sp100 depletion did not affect involucrin levels , though filaggrin was observed to increase at later stages of differentiation ( Fig 6B ) . Both filaggrin and involucrin are late keratinocyte differentiation markers [16] , and so it is unlikely that Sp100 downregulation is globally promoting differentiation . Furthermore , it has been shown that activation of papillomavirus late gene transcription and genome amplification upon differentiation is coincident with involucrin expression [17] . However , we cannot completely rule out that Sp100 effects on differentiation are indirectly affecting viral late functions . Analysis of four different HPV31 RNA species showed small increases in early ( E6*I; 1 . 2–1 . 5 fold ) , and intermediate ( E2; 1 . 0–1 . 3 fold and E1^E4; 1 . 4–2 . 0 fold ) in differentiated cells . However , consistently the late mRNA , L1 3590^5552 , was elevated 3–12 . 4 fold in cells depleted for Sp100 compared to control treated cells ( Fig 6C ) . We conclude that Sp100 most likely primarily represses late transcription . To analyze the effect of Sp100 on HPV31 genome amplification , CIN612-9E cells were analyzed by qPCR and Southern blotting ( Fig 7A and 7B ) . After Sp100 downregulation , the viral DNA copy number in differentiated cells was consistently increased . The magnitude of the increase was not high , probably because of the small percentage of cells that switch to the productive phase of viral replication after calcium treatment , but it was highly consistent . Taken together , these data suggest that Sp100 associates with viral replication factories to limit the viral DNA synthesized during genome amplification , and to repress transcription of late mRNAs transcribed from the newly amplified DNA . Sp100 encodes several splice variants , commonly referred to as Sp100A , Sp100B , Sp100C and Sp100HMG [18] and S1 Fig . Each of these isoforms share a common N-terminal domain that promotes dimerization [19] and another that mediates interaction with heterochromatin via association with the Heterochromatin Protein-1 ( HP-1 ) family of proteins [20] . Additionally , the Sp100B , C and HMG isoforms share a DNA binding motif known as the SAND ( Sp100 , AIRE-1 , NucP41/45 and DEAF-1 ) domain that directly binds DNA [21] . Sp100C and—HMG also have additional DNA binding ( HMG box ) and chromatin interacting motifs ( PHD and bromodomains ) that further implicate Sp100 in regulation of cellular gene expression [18] . To determine which Sp100 isoforms were responsible for repression of HPV transcription and replication , we first measured the isoforms present in primary HFKs . Using primers specific to either Sp100A , B , C or HMG , we found that all four Sp100 isoform transcripts were expressed in primary human keratinocytes ( S1 Fig , panel C ) . Sp100A mRNA was the most abundant isoform , followed by Sp100B mRNA . Sp100C and Sp100HMG mRNA were both detectable , but at levels four to fifteen times lower than Sp100A for Sp100C and Sp100HMG , respectively . At the protein level , the Sp100 isoforms are predicted to migrate at approximately 52 kDa ( Sp100A ) , 75 kDa ( Sp100B ) , or 97 kDa ( Sp100C and HMG ) . However , most studies detect only two predominant molecular weight species that migrate between 65–80 kDa and ~100 kDa when analyzed by SDS-PAGE and these species are frequently identified as either un-modified or SUMO-modified Sp100A [22] . To further characterize the Sp100 species present in HFKs , we transfected keratinocytes with expression vectors encoding EE-tagged versions of either Sp100A , B , C , HMG or an empty vector . The EE-tag adds only ten amino acids to Sp100 and thus can help delineate endogenous Sp100 isoforms . Immunoblot analysis with a pan-Sp100 antibody showed the same two predominant Sp100 species previously described ( ~65 kDa and ~80 kDa ) , as well as a low abundance of several higher molecular weight species ( S1 Fig , panel D ) . Comparison of lysate from cells transfected with the Sp100A expression vector to the empty vector lysate revealed two major Sp100 protein species that migrated slightly above the two major endogenous Sp100 bands ( S1 Fig , panel D ) . These are most likely unSUMOylated and SUMOylated forms of Sp100A ( S1 Fig , panel D ) , while the species migrating slightly above 100 kDa is most likely Sp100A that has acquired two SUMO modifications [23] . Lysates from cells transfected with an Sp100B expression vector showed a major band migrating at ~100 kDa , and a minor species at ~115 kDa ( S1 Fig , panel D ) . From the migration pattern , it seems that almost all endogenous Sp100B is SUMOylated . Lysates from cells transfected with either Sp100C or Sp100HMG expression vectors contained protein species that corresponded with the slowest migrating forms of endogenous Sp100 ( S1 Fig , panel D ) . Because Sp100C and Sp100HMG have nearly identical molecular weights , we were unable to distinguish between them . In summary , all four major splice variants of Sp100 are present in primary human keratinocytes . Type I-IFN upregulates Sp100A and other , higher molecular weight Sp100 species in several human cell types [24–26] . Treatment of HFKs with IFN-α upregulated all Sp100 isoforms with peak induction of all isoform mRNAs six hours post-exposure ( S2 Fig , panels A-F ) . The mRNA levels for Sp100A , -B , and -HMG increased approximately fivefold , while Sp100C mRNA levels were induced almost 10-fold over uninduced cells . We also examined the upregulation of Sp100 at the protein level following treatment with IFN . Upregulation of Sp100A and Sp100C/HMG ( both un-modified and SUMO-modified ) protein level was apparent 24 hours post-treatment and was sustained for at least 72 hours ( S2 Fig , panel G ) . On examination of Sp100 in situ , we observed a substantial increase in both the size and number of PML and Sp100 nuclear foci at 48 hours post-IFN treatment ( S2 Fig , panel H ) . These data demonstrate that the major isoforms of Sp100 are IFN-responsive in primary skin cells . Sp100A has been shown to activate a CMV-promoter controlled gene array by promoting chromatin decondensation , and adenovirus retains Sp100A in viral replication centers while excluding the SAND domain containing isoforms to evade Sp100-mediated repression [27] . Habiger and colleagues have also shown that the three longer isoforms of Sp100 can repress luciferase expression from HPV18 and HPV31 URR ( upstream regulatory region ) driven reporter plasmids [8] . To determine whether individual Sp100 isoforms regulate transcription and replication from the HPV genome , HFKs were coelectroporated with HPV18 recircularized genomes and each of the Sp100 expression vectors . In cells coelectroporated with an Sp100A expression vector , only a small ( and non-significant ) reduction in HPV18 E1^E4 or E6*I transcription was observed ( Fig 8 ) . However , the SAND domain containing isoforms ( B , C and HMG ) reduced viral transcription by approximately 50–60% . A mutation in the DNA binding region of the SAND domain of Sp100B reduced transcriptional activity similar to empty vector ( Fig 8A ) ; however , as shown in S1 Fig , panel D , the steady state levels of the mutated Sp100B was less than that of wild type , making it difficult to conclude that the SAND domain is crucial for the observed repression . We also analyzed viral replication in the same transfected samples . No measurable change in HPV18 replication was apparent in cells cotransfected with Sp100A ( Fig 8B ) , but the SAND domain containing isoforms of Sp100 repressed viral replication . Furthermore , in cells transfected with the expression vector encoding Sp100B containing the SAND domain DNA binding mutation , replication was restored to wild-type levels ( Fig 8B ) . However , as described above , we could not absolutely conclude that repression required the SAND domain function because of reduced stability of the protein . In conclusion , the three longer isoforms of Sp100 repress HPV transcription and replication . Since the repressive forms of Sp100 contain both DNA binding and chromatin binding domains , we sought to determine whether Sp100 binds to viral chromatin to repress transcription and replication in differentiated 9E cells . Primers were designed to amplify different regions across the HPV31 genome , and those that passed qPCR quality control are shown in Fig 9A . To ensure that immunoprecipitation by the Sp100 antibody was specific , we treated confluent 9E cells with interferon to increase Sp100 expression ( S3 and S4 Figs ) . This showed that the Sp100 isoforms are upregulated by interferon in confluent CIN612-9E cells ( S3 Fig , panel A ) , in a pattern very similar to that of primary HFKs ( S2 Fig ) . Chromatin was prepared from growing , confluent , and calcium treated 9E cells , as well as confluent 9E cells that had been treated for 24 hours with 25 ng/ml IFNα . Interferon increased the amount of Sp100 binding to HPV31 DNA by about threefold , indicating that the ChIP assay was specific . Additional controls using negative control antibodies ( IgG ) , and positive control histone H3 antibodies are shown in S4 Fig . This showed that the Sp100 antibody coimmunoprecipitates up to 70-fold more viral DNA than the IgG negative control . To further prove the specificity of the Sp100 antibody , Sp100 protein was downregulated by siRNA before ChIP analysis . This showed that there was a very substantial decrease in the amount of viral DNA immunoprecipitated after siSp100 treatment ( S4 Fig panels B and C ) . Therefore , the Sp100 ChIP assay is robust , and specific . Collectively , the ChIP analyses showed that Sp100 bound to all regions of the viral genome ( similar to histone H3 ) ; no specific region of the genome was targeted by Sp100 and so its repressive effect is viral sequence independent . We compared Sp100 binding to the HPV31 genome in growing , confluent , or differentiated 9E cells ( treated for three days in high CaCl2 medium after reaching confluence ) . Viral DNA copy number increased when cells reached confluency but did not increase further after culture in CaCl2 medium ( Fig 9 ) . We saw a pronounced increase in the amount of viral DNA coimmunoprecipitated with Sp100 as cells progressed from growing to confluence , and again after CaCl2 treatment . When the percentage of genome bound by Sp100 was calculated ( % input ) , it was observed that Sp100 binding increased both when viral DNA was amplified at cellular confluence and when differentiation was further increased after CaCl2 treatment . Overall , there was a 2 . 6 to 4 . 9 fold increase in Sp100 binding per viral genome upon differentiation ( Fig 9 ) . Therefore , Sp100 binding to the HPV genome increases as cells transition into the late , productive stage of infection . To obtain a more exact quantitation of the increase in Sp100-viral DNA association upon differentiation , we performed an absolute quantification of Sp100 protein and HPV31 DNA in chromatin immunoprecipitates from growing and differentiated 9E cells . The Sp100 protein content in input and immunoprecipitated chromatin samples was calculated by reference to a standard curve of in vitro translated Sp100 protein , and the absolute quantity of HPV31 genomes in each sample was measured through ChIP-qPCR analysis of input DNA and immunoprecipitated viral DNA against an HPV31 plasmid DNA standard curve . As shown in S5 Fig , there was ~ eightfold increase in the number of HPV31 genomes associated with Sp100 in differentiated cells , taking into account decreased Sp100 protein and increased viral DNA levels observed in input chromatin fractions after differentiation . Sp100 binding to viral DNA also increased substantially with interferon treatment ( S4 Fig ) . The viral DNA copy number showed only a small decrease after IFN treatment , as measured from input chromatin samples ( S4 Fig ) and so the observed increase in binding reflected increased Sp100 binding per viral genome . The increase in binding in response to IFN treatment supports the hypothesis that Sp100 binding to HPV31 DNA is an anti-viral response . Interestingly , the amount of histone H3 bound to viral DNA increased with differentiation . This could be due to increased chromatin accessibility in the rapidly amplifying viral DNA in replication foci , or changes in topological restraints , as the genomes switch from bidirectional theta replication to recombination-directed replication [28] . We have previously shown that there are different patterns and enrichments of modified histones in the replication foci in 9E cells [13] and follow-up studies will analyze the role of chromatin dynamics in late viral replication . For Sp100 to repress late HPV transcription , it must be expressed in cells that amplify viral DNA and transcribe late mRNAs . Nakahara and Lambert examined PML bodies in organotypic raft tissue and showed that the number of bodies were increased in HPV-infected cells and tissue [29] . They showed that the PML bodies were present in basal and suprabasal cells , but were no longer present in the most differentiated cells . To examine if the same is true for Sp100 , we stained sections of tissue derived from an HPV16-infected cervical lesion with antibodies to Sp100 and E4 , and combined this with FISH for viral DNA . We noted that , as described for PML by Nakahani and Lambert , Sp100 expression was increased in HPV-infected tissue compared to normal tissue . Foci of Sp100 were observed in basal cells and these increased throughout the stratum spinosum , but disappeared in the most differentiated cells . S6 Fig shows single cell quantitation of Sp100 expression in normal and HPV16-infected cervical tissue that confirms these findings . Viral DNA amplification occurs in the upper layers of HPV16-infected tissue and we wanted to determine whether this occurs in cells that still express Sp100 . As shown in Fig 10A , viral DNA amplification was initiated in cells that still contained Sp100 foci . In the layer of cells above this , full-blown DNA amplification could be observed and Sp100 was no longer present . A careful analysis and 3D reconstruction of cells initiating viral DNA amplification in the presence of Sp100 showed that in the majority of cells ( ~90% ) Sp100 foci were localized adjacent to , or engulfed by , HPV replication foci ( Fig 10B and 10C ) . Therefore , Sp100 foci and viral DNA replication foci interact at late times of infection , concomitant with viral DNA amplification and late gene transcription .
In this study , we found that HPV replication factories generated by expression of the E1 and E2 replication proteins , and those formed in differentiated cells containing the HPV genome , are associated with PML and Sp100 containing ND10 bodies . The ND10 bodies were mostly observed adjacent to , or in a satellite pattern around , the replication factories; however , PML and Sp100 proteins could also be observed inside large foci in close association with amplifying viral DNA . In cells containing E1-E2 generated replication foci , the normal , nuclear body staining pattern of Sp100 and PML was disrupted , and in many cases all nuclear Sp100 had accumulated into a single replication factory . Sp100-containing nuclear foci were also detected adjacent to viral replication foci in the upper layers of HPV-infected cervical lesions , and often single or multiple Sp100 foci were engulfed in a cloud of viral DNA . The role of Sp100 was analyzed in detail; we determined that Sp100 was associated with viral chromatin , and this association increased concomitant with the formation of replication foci in differentiated cells , resulting in repression of viral transcription and replication . Transient expression of the individual isoforms of Sp100 showed that the three longer isoforms of Sp100 ( Sp100B , C and HMG ) could limit viral replication and transcription . Sp100 proteins limit the transcription and replication of many viruses , often by repressing incoming genomes at the early stages of infection [7 , 8 , 26 , 27 , 30–35] . The replication factories that form in the late , productive phase of several viruses are also associated with ND10 bodies and late viral functions can also be specifically repressed by Sp100 [30 , 36 , 37] . We have shown previously that Sp100 represses viral transcription , replication and establishment at early times after infection [7] , and here we extend these findings to show that the differentiation-dependent , productive stage of HPV infection is also limited by Sp100 . Analysis of ND10 protein modulation of viral processes can be challenging because different components can have opposing effects [37 , 38] , and many viruses encode antagonists that negate anti-viral effects [38] . Moreover , both PML and Sp100 encode several different isoforms that differentially modulate viral transcription and replication . An additional challenge in studying the late phase of the HPV life cycle is that it is dependent on differentiation of the host keratinocyte . We successfully downregulated Sp100 in differentiated CIN612-9E cells , but siRNA-mediated repression of PML expression inhibited keratinocyte differentiation and so we were unable to further analyze the role of PML in differentiated cells . We also observed some effect of siRNA-mediated repression of Sp100 on filaggrin , but not involucrin , keratinocyte differentiation markers , and so there remains the possibility that some of the effects of siSp100 observed on late viral functions are indirect . Calcium induction of differentiation in HPV31 containing CIN612-9E cells induces viral DNA amplification and late gene transcription , though it is not possible to achieve the magnitude of viral DNA and RNA observed in vivo . Nevertheless , enough viral DNA amplification and late gene expression are induced to allow the effect of Sp100 to be measured . Repeated experiments showed that Sp100 depletion had only minimal effect on viral early or intermediate mRNA transcripts , but consistently upregulated late L1 transcripts and increased viral DNA amplification ( Figs 6 and 7 ) . This identifies a role for Sp100 in the productive stages in the viral lifecycle . Similarly , Sp100 has an anti-viral effect at both early and late stages of infection by hCMV [30] , and HSV [39] . Reactivation of EBV lytic infection also results in the association of EBV genomes and viral gene products with ND10 body components [36] . In the current study , we also observed Sp100 binding to viral chromatin and showed that this binding increased with differentiation . Thus , Sp100 likely functions as a repressor of late viral transcription and replication , providing infected cells with a late-stage anti-viral activity . We have previously shown that depletion of Sp100 during maintenance replication has only minimal effects on viral DNA copy number or viral transcription [7] , and we confirm that finding here for HPV31 containing-cells . Thus , Sp100 repression seems to be mostly confined to early and late stages of viral genome amplification . We speculate that this could be due to the unlicensed , unscheduled viral DNA replication that takes place at these stages . Viral DNA amplification is closely associated with the cellular DNA damage and repair response ( DDR ) [40] , and components of the DDR interact with or localize to ND10 [41–43] . The host DDR is important for amplification of viral DNA in differentiated cells [44 , 45] and the recruitment of DNA repair factors to replication factories within these cells is thought to facilitate viral DNA replication in cells that have exited the cell cycle and are no longer replicating the host DNA [28 , 46] . This suggests a complex relationship between late-stage HPV genome amplification , the DDR , and the intrinsic immune system . We also speculate that only viral transcription from templates that have undergone amplificational replication are subject to significant Sp100 repression . This would explain why early viral mRNA transcripts ( such as E6*I ) are repressed at early stages of infection [7] , while primarily late transcripts ( L1 ) are repressed in differentiated cells ( this study ) . We further examined the HPV-Sp100 interaction by characterizing the Sp100 isoforms expressed in primary keratinocytes to elucidate which isoforms could contribute to Sp100-mediated repression of HPV infection . We observed all four major Sp100 isoforms expressed in primary HFKs with Sp100A being the most abundant isoform at both the RNA and protein level ( S1 Fig ) . Furthermore , IFN-α enhanced expression of all four isoforms ( S2 Fig ) in a pattern very similar to that described for BJ fibroblasts [26] . In both cell types , Sp100C was expressed at relatively low levels compared to the other isoforms but was most strongly induced at the transcriptional level by IFN-α . Habiger et al . , also recently showed that most Sp100 isoforms are expressed at reduced levels in CIN 612-9E cells , compared to primary HFKs [8] , consistent with the global downregulation of interferon-induced genes in HPV-infected cells [47] . Notably , all Sp100 isoforms observed by immunoblot analysis could be induced by exogenous IFN in HPV31-containing differentiated 9E cells ( S3 Fig ) . We also observed an increase in the SUMOylation of Sp100A upon differentiation ( S3 Fig ) . However , we were unable to analyze the role of the Sp100 isoforms in differentiating CIN612-9E cells ( siRNAs against the individual isoforms showed substantial crosstalk ) . Nevertheless , by cotransfecting expression vectors for each isoform along with an HPV18 viral genome into keratinocytes , we found that expression of the SAND domain containing isoforms Sp100B , C and HMG significantly reduced viral gene expression and DNA replication ( Fig 8 ) . Mutation of the DNA binding domain of Sp100B ( Sp100B-Q ) abrogated this repression , but this result has to be interpreted with caution as the mutated protein had decreased steady state levels . A similar result was obtained by Habiger and colleagues who found that the three longer forms of Sp100 could repress transcription from an HPV URR driven reporter plasmid [8] . Correspondingly , the SAND domain containing isoforms of Sp100 also function as repressors of HSV immediate-early transcription [25 , 26] and adenovirus infection [27] . In the latter case , Sp100A is recruited to adenoviral replication centers , while the repressive isoforms ( B , C and HMG ) are redistributed away from these sites [27] . Sp100A can decondense viral chromatin and activate viral transcription in the context of CMV infection [48] , while Sp100B binds DNA and represses transcriptional activity of DNA containing unmethylated CpG motifs [49 , 50] . Therefore , the different Sp100 isoforms differentially regulate transcription and replication of several viruses . All four isoforms of Sp100 contain a motif that promotes interaction with ND10 bodies , but the three longer isoforms contain additional domains that bind DNA and chromatin [49 , 51] . This would be consistent with our finding that Sp100 colocalizes with , and binds to , viral chromatin . We did not observe specific association with any region of the viral genome and this could indicate that Sp100 is marking the genome for silencing rather than functioning at specific transcriptional elements or the viral replication origin . It has previously been shown that PML containing ND10 bodies are increased in the poorly differentiated layers of HPV-infected organotypic rafts compared to uninfected rafts , but are absent in the most superficial , differentiated layers [29] . Sp100 containing ND10 bodies have also been detected in HPV33-infected cervical tissues and similarly are absent in the upper layers of tissue [6] . This raised the question as to whether Sp100 was actually present in differentiating cells that are amplifying viral DNA . However , we show that there is a clear transition zone where Sp100 is still expressed and viral DNA amplification begins . Furthermore , nuclear foci of Sp100 ( likely ND10 bodies ) are very frequently located adjacent to , or engulfed by , the replicating DNA . In summary , Sp100 associates with replication foci at late times in infection and appears to repress this stage of infection by binding to viral chromatin . We have concluded that Sp100 functions as a repressor of late viral transcription and replication to provide infected cells with a late-stage anti-viral activity . However , there is also the strong possibility that HPV usurps the anti-viral functions of Sp100 to modulate the viral lifecycle . For example , Sp100-mediated repression could promote the shift from early infection to the maintenance phase of infection . Repression and condensation of viral chromatin at the late stage of infection could also prepare viral genomes for packaging in virion particles . Most likely , Sp100 attempts to restrict late viral infection , but HPV antagonizes this process , and probably hijacks some of its functions . This could explain the relatively modest effects of Sp100 we observe on late viral replication and transcription . In summary , we have shown that Sp100 represses viral replication and transcription at early and late stages of infection . Therefore , the host intrinsic immune system functions to restrict viral activity in newly infected cells , but is also activated when persistently infected cells switch into the productive stage of the viral life cycle upon differentiation . We predict that this is linked to the unscheduled viral DNA amplification that takes place at these times .
Wildtype genomes for HPV16 , HPV18 and HPV 31 cloned in prokaryotic vectors have been described previously [52–54] . The mammalian expression vectors for N-terminally glu-glu ( EE ) epitope tagged HPV16 E1 ( pMEP9-HPV16EE-E1 ) , and N-terminally FLAG epitope tagged HPV16 E2 ( pMEP4-HPV16FLAG-E2 ) have been described previously [55 , 56] . pKS ( - ) and pKS-HPV16 ori plasmids have been described previously [57] . EYFP-tagged versions of Sp100A , Sp100B , Sp100C , Sp100HMG , and Sp100B-W655Q were obtained from Susan Janicki ( Wistar Institute; Philadelphia , PA ) and have been described previously [48] . N-terminal , EE epitope-tagged versions of each Sp100 isoforms were generated by restriction digestion of pEYFP-Sp100A , pEYFP-Sp100B , pEYFP-Sp100C , pEYFP-Sp100HMG , pEYFP-Sp100B-W655Q with NheI and XhoI to remove the N-terminal EYFP tag and it was replaced with a synthesized DNA sequence containing a T7/SP6 promoter , β-actin mRNA leader sequence , and an EE epitope tag ( GeneART; Hamburg , Germany ) . The resulting plasmids were named pCMV-TnT-Sp100A , -Sp100B , -Sp100C , -Sp100HMG and -Sp100B-W655Q , respectively . To obtain recircularized HPV genomes , 10 μg HPV DNA , cloned in a prokaryotic vector , was digested with a restriction enzyme to release the viral DNA . Following digestion , the restriction enzyme was either heat inactivated or purified away from the digested DNA using HiPure PCR Purification Columns ( Roche AG; Mannheim , Germany ) . The released viral DNA was resuspended at 10 μg/ml in 1X ligation buffer ( 66 mM Tris-HCl , pH 7 . 5 , 5 mM MgCl2 , 5 mM DTT , 1 mM ATP ) with 40 units of T4 DNA ligase ( New England BioLabs; Ipswich , MA ) in a total volume of 900 μl and incubated at 16°C for 12–18 hours . Following ligation , NaCl was added to the solution to a final concentration of 0 . 5 M and the DNA was precipitated with 0 . 6 volumes isopropanol . Precipitated DNA was washed two times with 70% EtOH and resuspended in 1X TE buffer . The efficiency of viral genome religation was determined by separation of 200 ng pre- and post-ligation using DNA agarose gel electrophoresis . The final dsDNA concentration was determined by spectrophotometric analysis using the Nanodrop-1000 ( Life Technologies; Forest City , CA ) . The conditionally immortalized human foreskin keratinocytes ( HFK1a ) was developed in our laboratory and has been described previously [56] and was cultured in F-medium supplemented with the Rho-kinase inhibitor , Y-27632 ( Tocris Bioscience; Bristol , United Kingdom ) at a final concentration of 10 μM . CIN612-9E cells , a cell line derived from a CIN1 , HPV31-positive patient biopsy , that harbors extrachromosomal HPV31 genomes [9] , were grown in F-medium without antibiotics and was provided by Lou Laimins ( Northwestern University ) . Primary human keratinocytes were isolated from neonatal foreskins , as described previously [58] . Keratinocytes from each donor were independently designated HFK strain # . Cells were cultured in Rheinwald-Green F-medium ( 3:1 Ham’s F12/DMEM-high glucose , 5% fetal bovine serum , 0 . 4 μg/ml hydrocortisone , 8 . 4 ng/ml cholera toxin , 10 ng/ml epidermal growth factor ( EGF ) , 24 μg/ml adenine , and 6 μg/ml insulin ) on a layer of lethally irradiated NIH J2 3T3 murine fibroblasts [59] . Antibiotics were not used unless otherwise noted . NIH J2 3T3 mouse fibroblast cultures ( obtained from Craig Meyers , Penn State University ) were maintained at low passage and cultured in DMEM/10% newborn calf serum . Prior to coculture with human keratinocytes or CIN612-9E cells , feeders were exposed to 60 grays of γ-irradiation using a Model 30 J . L Shepard Cs-137 Mark I irradiator ( San Fernando , CA ) . For differentiation assays , CIN612-9E cells were transferred to low calcium basal medium ( Lonza Corporation; Walkersville , MD ) supplemented with SingleQuots for keratinocytes ( Lonza Corporation; Walkersville , MD ) containing bovine pituitary extract , hydrocortisone , and epidermal growth factor , grown until confluent , and then cultured for 96 hours in basal medium Lonza Corporation; Walkersville , MD ) , supplemented with 1 . 5 mM calcium chloride ( Lonza Corporation; Walkersville , MD ) . To induce Sp100 expression in ChIP assays , cells were cultured for 24 hours with 25 ng/ml carrier-free , human interferon α A ( PBL Assay Science; Piscataway , NJ ) in low calcium basal medium described above . HFK 1A cells cultured on glass coverslips in F-medium supplemented with 10 μM Y-27632 were transfected with HPV16 E1 and E2 expression vectors ( 400 ng each ) and 50 ng of pKS ( empty vector ) , pKS-16Ori , or recircularized HPV16 genome . DNA was introduced into cells using Fugene 6 ( Promega Corporation; Madison , WI ) . Fugene 6 was mixed 3:1 ( volume/mass ) with target DNA in serum-free Ham’s F-12 nutrient mixture for approximately 30 minutes at room temperature . Following incubation , lipid:DNA complexes were added dropwise to cells and incubated until cell harvest . E1 and E2 expression was induced 24 hours after DNA transfection with 3 μM CdSO4 for four hours and cells were fixed with 4% PFA for confocal microscopy analysis by indirect immunofluorescence . For cotransfection of HPV18 DNA with Sp100 isoform expression vectors , 2 μg HPV18 DNA and 1 μg Sp100 isoform DNA was incubated with 100 μl of room temperature Amaxa Nucleofection solution for 5 minutes as described in Fig legends . 106 cells were gently mixed with the DNA/Nucleofection solution and transferred to an electroporation cuvette . Electroshock was delivered using the pre-programed setting , T-007 . Cells were transferred to recovery medium ( F-Medium with 10% fetal bovine serum and no EGF ) and plated onto irradiated murine J2/3T3 fibroblasts . For RNA extractions , CIN612-9E cells were plated at a density of 1 . 3-8x104 cells/cm2 onto plates containing 1 . 3–1 . 5 x104 cells/cm2 of lethally irradiated J2/3T3 fibroblasts and cultured overnight . For ChIP experiments , CIN612-9E cells were cultured at a density of 4000 cells/cm2 on 15 cm plates containing 1 . 3–1 . 5 x104 cells/cm2 of lethally irradiated J2/3T3 fibroblasts . Pools of four siRNA duplexes that target PML ( L-006547-00-0010 ) , Sp100 ( L-015307-00-0005 ) or a non-targeting control siRNA ( D-00181a0-10-20 ) were purchased from Dharmacon ( GE Healthcare ) . siRNA transfections were performed as per manufacturer’s protocol . Briefly , siRNAs were complexed with Lipofectamine RNAiMAX transfection reagent ( Thermo Fisher Scientific ) in OptiMEM and added to cells with fresh F-medium at a final concentration of 20 nM . Cells were incubated with siRNA for indicated time periods before harvesting or further experimentation . All siRNA sequences are shown in S2 Table . For RNA extraction from keratinocyte coculture experiments , feeder cells were removed using Versene ( Life Technologies; Forest City , CA ) prior to harvest . Total RNA was isolated using the RNeasy Mini-RNA extraction kit ( Qiagen; Germantown , MD ) . RNA concentrations were determined using the NanoDrop 1000 spectrophotometer ( Life Technologies; Forest City , CA ) . RNA integrity analysis was performed by capillary electrophoresis using RNA 6000 Nano kits ( Agilent Technologies; Forest City , CA ) on a 2100 Bioanalyzer system ( Agilent Technologies; Forest City , CA ) . Reverse transcription reactions were performed with the Transcriptor First-Strand Synthesis kit ( Roche AG; Mannheim , Germany ) using 1 μg of total RNA , 60 μM random hexamers , and 2 . 5 μM oligo-dT primers and expression of the indicated genes was analyzed by qPCR using an ABI Prism 7900HT Sequence Detector or the QuantStudio 7 Flex Real Time PCR System ( Applied Biosystems; Forest City , CA ) using SYBR green PCR master mix ( Roche AG; Mannheim , Germany ) . Each reaction mixture contained 1× SYBR green master mix , cDNA from 1 μg of RNA , and 0 . 3 μM each oligonucleotide primer in a total volume of 20 μl . In each run , a 10-fold dilution series ( 2 . 5x105-2 . 5x10-2 fg ) of the target mRNA standard was included to generate a curve of threshold cycle ( Ct ) versus log10 quantity ( fg ) . DNA standards for the HPV31 mRNAs E1^E4 ( nt 857–877^3294–3296 ) , E6*I ( nt 186–210^413–416 ) and L1 ( nt 3562–3590^5552–5554 ) were commercially synthesized and cloned into the SfiI site of pMAT ( GeneART; Hamburg , Germany ) . A DNA standard for the intermediate HPV31 E2 mRNA ( nt 862–877^2646–2648 ) was generated by PCR amplification of HPV31 nucleotides 862–877^2646–2712 from cDNA derived from an HPV31-containing cell line and ligation of the PCR product into the cloning vector , pCR2 . 1 , using a TopoTA cloning kit ( Life Technologies; Forest City , CA ) . DNA standards containing amplicons for the cellular mRNAs Sp100A , Sp100B , Sp100C and Sp100 HMG were commercially synthesized and cloned into the SfiI site of pMAT ( GeneART; Hamburg , Germany ) . DNA standards for the cellular mRNAs of involucrin , filaggrin and cyclophillin A were generated by PCR amplification of involucrin , filaggrin and cyclophilin A cDNAs derived from primary human keratinocytes using commercially available primers from Qiagen ( Germantown; MD ) . pRSV-neo was purchased from ATCC ( Manassas , VA ) and has been described previously [60] . pDRIVE-GAPDH and pCMV-SPORT6-TBP were purchased from OpenBiosystems ( Pittsburgh; PA ) . The β-actin-GFP plasmid containing the human β-actin promoter upstream of the actin-GFP sequence was purchased from AddGene ( Cambridge; MA ) and has been described previously [61] . Gene expression was measured and the data are reported as the quantity of specific cDNA present in cDNA prepared from 12 . 5–50 ng of RNA ( labeled as quantity of RNA ) . The specificity of each primer pair was determined by dissociation curve analysis and the presence of single amplicons was confirmed by capillary electrophoresis using DNA1000 LabChips ( Agilent Technologies; Forest City , CA ) on a 2100 Bioanalyzer system ( Agilent Technologies; Forest City , CA ) or standard gel electrophoresis using 2 . 5% agarose-Tris acetate EDTA ( TAE ) . Primer sequences are shown in S1 Table . For all keratinocyte coculture experiments , feeder cells were removed using Versene ( Life Technologies; Forest City , CA ) prior to harvest . Total cellular DNA was isolated from keratinocytes using the DNeasy Blood and Tissue Kit ( Qiagen; Germantown , MD ) at specific times of infection . 1 to 5 ng of DNA was analyzed by qPCR using 300 nM primers and SYBRgreen Master mix ( Roche AG; Mannheim , Germany ) . Reactions conditions consisted of a 15 minute 95°C activation cycle , 40 cycles of 10 s 95°C denaturation and 30 s 60°C annealing and elongation . Copy number analysis was completed by comparing the unknown samples to standard curves of linearized HPV18 DNA . The DNA copy number of β-actin was used as an endogenous control for all DNA qPCR experiments . qPCR for HPV31 DNA copy number was performed following isolation of DNA from CIN612-9E cells . DNA was digested with HindIII and BamHI and amplified using HPV31-specific primers under the reaction conditions described above . All qPCR was performed using an ABI 7900HT PCR system or the QuantStudio 7 Flex Real Time PCR System ( Applied Biosystems; Forest City , CA ) . Primers for all PCR reactions are listed in S1 Table . For all keratinocyte coculture experiments , feeder cells were removed using Versene ( Life Technologies; Forest City , CA ) prior to harvest . Total DNA was harvested using the DNeasy Blood and Tissue Kit ( Qiagen; Germantown , MD ) . At least 1 μg total DNA was digested with either a single-cut linearizing enzyme ( HPV16 , BamHI; HPV31 , HindIII ) for the HPV genome , or with a non-cutter ( HPV16 , HindIII; HPV31 , BamHI ) to linearize cellular DNA . After digestion , samples were separated on 0 . 8% agarose-TAE gels . DNA was visualized with 0 . 5 μg/ml ethidium bromide and was transferred onto nylon membranes using a Turbo Blotter downward transfer system ( GE Healthcare; Pittsburg , PA ) . Membranes were UV cross-linked ( 120 mJ/cm2 ) , dried and blocked with hybridization buffer ( 3X SSC , 2% SDS , 5X Denhardt’s solution , 0 . 2 mg/ml sonicated salmon sperm DNA ) for 1 hour . After blocking , the membrane was incubated overnight with 25ng ( 32P ) -dCTP labeled probe ( specific for HPV16 or HPV31 ) in hybridization buffer ( 3X SSC , 2% SDS , 5X Denhardt’s solution , 0 . 2 mg/ml sonicated salmon sperm DNA ) . Hybridized DNA was visualized and quantitated by phosphor-imaging on a Typhoon Scanner ( GE Healthcare; Pittsburgh , PA ) . To generate HPV-specific radiolabeled probes , linear viral DNA was cleaved from vector sequences and gel-purified using QiaQuick Gel Extraction kits ( Qiagen; Germantown , MD ) . Radiolabeled probe was generated from 50 ng gel-purified linear HPV16 or HPV31 DNA with the Random Prime DNA Labeling Kit ( Roche AG; Mannheim , Germany ) . Cells were cultured on #1 . 5 18 mm glass coverslips and fixed with 4% paraformaldehyde/ PBS . Fixed cells were permeabilized with 0 . 1% Triton X-100 in PBS and blocked in 5% ( v/v ) normal donkey serum ( Jackson Immunoresearch; West Grove , PA ) . All primary antibody incubations were performed at 37°C for 1 hour . Antibodies used were: goat anti-PML A-20 ( Santa Cruz Biotechnology , Dallas , TX; dilution 1:50 ) ; rabbit anti-PML ( Sigma Corporation; St . Louis , MO; dilution 1:50 ) ; mouse anti-PML , PG-M3 Santa Cruz Biotechnology , Dallas , TX; dilution 1:50 ) ; rabbit anti-Sp100 ( Sigma Corporation; St . Louis , MO; dilution 1:500 ) ; chicken anti-EE ( Bethyl Laboratories; Montgomery , TX; dilution 1:100 ) ; mouse anti-FLAG M2 ( Sigma Corporation; St . Louis , MO; dilution 1:500 ) . Following primary antibody incubation , coverslips were washed three times in PBS . All secondary antibody incubations were performed at 37°C for 30 minutes . Alexa 488 , Alexa 564 , Alexa 594 , Rhodamine Red-X or Alexa 647 conjugated to the desired species’ immunoglobulin was purchased from Jackson Immunoresearch ( West Grove , PA ) and were used at 1:100 dilution . Coverslips were mounted using 10 μl ProLong Gold with DAPI ( Life Technologies; Forest City , CA ) . Cells were visualized using a Leica TCS-SP5 laser scanning confocal microscope ( Leica Microsystems; Buffalo Grove , IL ) . Cells were fixed and stained as described for IF . After antigen staining , cells were fixed again with methanol: acetic acid ( 3:1 , v/v ) at room temperature for 10 minutes followed by 2% PFA for 1 minute at room temperature to fix the antibodies in place . Cells were treated with RNace-iT cocktail ( Agilent Technologies; Forest City , A ) . at a dilution of 1:1000 for 1 hr at 37°C and subsequently dehydrated in 70% , 90% , and 100% ethanol for 3 minutes each and air dried . HPV16 or 31 DNA FISH probe was prepared using the FISH-Tag DNA Multicolor Kit labeling kit ( Life Technologies; Forest City , CA ) . DNA was conjugated to either Alexa 488 or Alexa 555 , according the manufacturer’s protocol . Hybridization was performed overnight in 1X Hybridization Buffer ( Empire Genomics; Buffalo , NY ) with 20–75 ng of labeled probe DNA at 37°C . Slides were washed at room temperature with 1X phosphate-buffered detergent ( PBD ) ( MP Biosciences , Inc; Cleveland , OH ) , followed by 1X wash buffer ( 0 . 5X SSC , 0 . 1% SDS ) at 65°C , and a final wash with 1X PBD at room temperature . Coverslips were mounted using Prolong Gold with DAPI ( Life Technologies; Forest City , A ) . and analyzed by confocal microscopy . For all keratinocyte coculture experiments , feeder cells were removed using Versene ( Life Technologies; Forest City , A ) prior to harvest . Whole cell lysates were extracted from primary human keratinocytes using SDS-extraction buffer ( SDS-EB ) ( 50 mM Tris , pH 6 . 8; 10% glycerol , 2% SDS ) supplemented with cOmplete ULTRA protease inhibitor cocktail ( Roche AG; Mannheim , Germany ) . 1X SDS-EB was added to each sample ( 200 μl/3 . 8 cm2 of culture dish area ) and the lysate was collected by scraping . To shear genomic DNA , samples were sonicated using a cup horn sonicator ( Sonics & Materials; Newtown , CT ) at 15% amplitude for 1–2 minutes . Following sonication , samples were heated to 95°C for 5 minutes , and then immediately cooled to room temperature . Protein concentration of each sample was determined using the Pierce BCA Assay Reagent ( Thermo Fisher Scientific; Waltham , MA ) . Protein samples were normalized to the same concentration using 1X SDS-EB , and 10–25 μg total protein was supplemented with 50 mM DTT and 4X LDS sample buffer to a volume of 25 μl . Samples were heated to 70°C for 10 minutes , cooled to room temperature , and separated by SDS-PAGE on 4–12% NuPage gradient gels ( Life Technologies ) in 1X MOPS buffer for 1–2 hours at 185V . Proteins were transferred overnight onto PVDF membrane ( EMD Millipore; Stafford , VA ) and subsequently immunoblotted . Primary antibodies used were: rabbit anti-PML ( Bethyl Laboratories; Montgomery , TX; dilution 1:1000 ) ; rabbit anti-Sp100 ( Sigma Corporation; St . Louis , MO; dilution 1:1000–1:2000 ) ; mouse anti-α tubulin ( Sigma Corporation; St . Louis , MO; dilution 1:10 , 000 ) ; rabbit anti-GFP ( AbCam; Cambridge , MA ) . Species appropriate secondary antibodies conjugated to horseradish peroxidase ( Thermo-Pierce; Waltham , MA ) were used at 1:10 , 000 and detected using SuperSignal WestDura Western Detection Reagent ( Life Technologies; Forest City , CA ) . The chemiluminescent signal was collected with a Kodak Bioimager 6000 ( Carestream Health; Rochester , NY ) or SynGene G:Box ( SynGene USA; Frederick , MD ) and the resulting signal was quantified . Chromatin samples from sub-confluent , confluent , and differentiated CIN612-9E cells were prepared as previously described [62] . Optimal conditions for shearing viral chromatin to 200–500 bp fragments were determined by Southern blot analysis to be similar to that of cellular DNA , and subsequently shearing of total DNA was monitored by agarose gel electrophoresis/ethidium bromide staining . For each cell culture condition , 20 μg chromatin was incubated overnight at 4°C with 1 μg ChromPure Rabbit IgG , whole molecule ( 011-000-003 ( Jackson ) ) , 2 μg anti-histone H3 antibody ( ab1791 ( Abcam ) ) , and 1 μg anti-Sp100 antibody ( HPA016707 ( Sigma ) ) . Dynabeads with protein G ( Invitrogen ) were blocked in 1 mg/ml ultrapure BSA ( Ambion ) and incubated with samples at 4°C to precipitate chromatin immunocomplexes . Dynabeads were subjected to wash steps and DNA was purified using the ChIP DNA Clean and Concentrator kit ( Zymo Research ) . Genomic regions of immunoprecipitated HPV chromatin were quantified with qPCR using an HPV31 plasmid standard curve . For Sp100 protein quantifications following elution of chromatin-immunocomplexes , 5 μg of the 20 μg IP was processed for ChIP-qPCR and 15 μg processed for immunoblot analysis . For immunoblot analysis , chromatin eluate was analyzed by Sp100 immunoblot analysis , as described above . Chemiluminescent signal was collected using a SynGene G:Box ( SynGene USA ) and the resulting signal quantified using GeneTools image analysis software ( SynGene ) . To calculate the absolute number of Sp100 molecules in each sample , an Sp100 standard curve was generated by in vitro translation of the pCMV-TnT-EE Sp100A plasmid using the TnT Quick Coupled Transcription/Translation System ( Promega ) . Empty pCMV-TnT-EE vector was translated as a negative control . 35S-methionine ( NEN , 1170Ci/mmol ) incorporation was determined by TCA precipitation of translated proteins , allowing calculation of moles of Sp100 . All images were collected with a Leica SP5 laser scanning confocal microscope ( Leica Microsystems ) using a 63X oil immersion objective ( NA 1 . 4 ) . 2D images were collected as a single optical slice for all experiments unless otherwise noted . Images for 3D analysis were collected at either 1 μm or 0 . 13 μm thickness per slice , as noted in the figure legends . Where noted , images were deconvolved using Huygens Essential ( Scientific Volume Imaging B . V . , VB Hilversum , Netherlands ) using manual background selection and a signal-to-noise ratio of 25:1 . Final images were processed using Leica AS AF Lite ( Leica Microsystems ) or IMARIS ( v7 . 7 . 1; Bitplane AG; Zurich , Switzerland ) . Exported images were arranged together as a figure , assembled using Adobe Illustrator ( Version CS5; Adobe Systems; San Jose , A ) and minimally processed as a group using Adobe Photoshop ( Version CS5; Adobe Systems; San Jose , CA ) . Formalin-fixed , paraffin-embedded tissues from normal and HPV16-infected cervix were deparaffinized , rehydrated , and incubated at 96°C for 30 minutes in epitope-retrieval buffer ( 25 mM Tris-HCl pH 8 . 5 , 1 mM EDTA , 0 . 05% SDS ) [63] . Sections were blocked with 5% normal donkey serum ( NDS ) in PBS for one hour in a humidifying chamber at 37°C . Sections were incubated at 37°C with rabbit anti-Sp100 polyclonal antibody ( Sigma HPA016707; 1:500 ) for one hour in 5% NDS/PBS , washed and incubated at 37°C with goat anti-rabbit polyclonal antibody conjugated to rhodamine red-X ( Jackson ImmunoResearch ) for one hour in 5% NDS/PBS and washed three times with PBS/0 . 05% Tween 20 . Sections were incubated at 37°C with anti-E4 antibody conjugated to Alexa 488 ( kindly provided by the Doorbar laboratory; 1:1000 ) for 1 hour in 5% NDS/PBS , washed three times with PBS/0 . 05% Tween 20 . Antibodies bound to tissue were briefly fixed with 3:1 methanol: acetic acid and subsequently 2% paraformaldehyde/PBS . RNA was removed with 1X RNace-IT cocktail ( Stratagene ) for one hour at 37°C . Sections were dehydrated with an ethanol series and allowed to dry for one hour . 75 ng HPV16 DNA conjugated to Alexa 647 ( FISH Tag DNA Multicolor Kit , Invitrogen ) in FISH hybridization buffer ( Empire Genomics ) with 500 ng human Cot-1 DNA ( Invitrogen ) was applied to each section and sealed with a coverslip . DNA was denatured for 5 minutes at 75°C and hybridized with probe for 20 hours at 37°C with a Thermobrite StatSpin System ( Leica ) . Slides were washed with 1X phosphate buffered detergent ( MP Biomedicals ) , then with 0 . 5X SSC with 0 . 1% SDS at 65°C , and again with 1X phosphate buffered detergent . Sections were incubated with 300 nM DAPI , washed three times with PBS , and mounted with Prolong Gold ( Invitrogen ) . Images were collected with a Leica Sp5 scanning confocal microscope with a 63X objective ( NA 1 . 4 ) . Z stacks of individual nuclei were imaged , deconvolved in Huygens Essential ( Scientific Volume Imaging B . V . , VB Hilversum , Netherlands ) and reconstructed as 3D surfaces in IMARIS ( v7 . 7 . 1; Bitplane AG; Zurich , Switzerland ) . Primary human keratinocytes were isolated from anonymized neonatal foreskins provided to the Dermatology Branch at NIH from local hospitals . The NIH Institutional Review Board ( IRB ) approved this process and issued an NIH Institutional Review Board waiver . | Host restriction factors act to limit viral infection as part of the intrinsic immune system . Upon entry into cells , human papillomaviruses ( HPV ) encounter anti-viral nuclear bodies , called ND10 , and components of these bodies , such as Sp100 , restrict early viral infection . If HPV escapes these defenses , it can establish a long-term , persistent infection . The eventual production of infectious HPV particles depends on the differentiation program of host keratinocytes . Here we show that in the transition from persistent to productive infection in differentiated keratinocytes , HPV again engages the Sp100 component of ND10 bodies . Sp100 is observed surrounding and inside replication centers in differentiated cells in culture , and in HPV-infected cervical lesions . This results in restriction of viral DNA amplification and late viral gene expression . These data collectively show that Sp100 mediates an anti-viral response at both early and late stages of viral infection . | [
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] | 2017 | Sp100 colocalizes with HPV replication foci and restricts the productive stage of the infectious cycle |
Infiltrating monocyte-derived macrophages ( MDMs ) and resident microglia dominate central nervous system ( CNS ) injury sites . Differential roles for these cell populations after injury are beginning to be uncovered . Here , we show evidence that MDMs and microglia directly communicate with one another and differentially modulate each other’s functions . Importantly , microglia-mediated phagocytosis and inflammation are suppressed by infiltrating macrophages . In the context of spinal cord injury ( SCI ) , preventing such communication increases microglial activation and worsens functional recovery . We suggest that macrophages entering the CNS provide a regulatory mechanism that controls acute and long-term microglia-mediated inflammation , which may drive damage in a variety of CNS conditions .
The immune system plays a pivotal role in development and homeostatic functions of the central nervous system ( CNS ) [1] . Immune system dysfunction can give rise to CNS disease [2] and its response to injury shapes recovery [3–5] . The cellular response to CNS injuries is stereotyped and involves the rapid reaction of tissue-resident microglia [6 , 7] and the recruitment of myeloid cells such as neutrophils and monocyte-derived macrophages ( MDMs ) within days [5] , and the activation of lymphocytes from the blood , meninges , and choroid plexus [8] . Two cell types that dominate CNS lesions are resident microglia and infiltrating MDMs . It is known that microglia and MDMs are ontogenetically distinct [9 , 10] , express cell type–specific transcripts and proteins [11 , 12] , and can thus potentially perform different functions at the site of injury [13–16] . However , their relative contribution to the injury response , and subsequent recovery , remains unclear . It is not known if these two cell types interact to specifically modulate each other’s function . Two of the primary functions of microglia and MDMs during CNS injury are phagocytosis and propagation of inflammation [17] . We have shown previously that after traumatic spinal cord injury ( SCI ) , cessation of microglia phagocytosis coincides with the infiltration of MDMs [14] . In animal models of stroke and CNS autoimmune disease , expression profiling of microglia after injury or at the onset of disease ( at the point of MDM infiltration ) shows that pathways involving core functions of microglia , such as inflammation , RNA transcription , and phagocytosis , are significantly down-regulated [16 , 18 , 19] . We therefore hypothesized that MDMs entering the CNS signal to resident microglia and modulate their function .
Three to five days after CNS injury , infiltrating macrophages are distributed across lesion sites and are therefore potentially able to interact with microglia [14 , 19 , 20] . After SCI , resident microglia and MDMs both increase in number around the lesion site [21] . We observed that MDMs and microglia are often in close proximity to one another ( S1A Fig ) ; however , whether they can regulate each other’s functions is not known . To assess whether the cessation of microglial phagocytosis seen after SCI correlates with the entry of MDMs into the CNS , as reported previously [14] , we used antibody-conjugated magnetic-bead sorting to isolate CD11b+ cells from spinal cord lesions in LysM-eGFP knock-in mice . LysM-eGFP reporter mice strongly express eGFP in myelomonocytic cells [22] but in less than 3% of microglia after SCI and other CNS injuries [19 , 20 , 23] . Therefore , isolated CD11b+ positive cells could be defined as resident microglia ( CD11b+/eGFP−ve ) or infiltrating MDMs ( CD11b+/eGFP+ve/Ly6G−ve ) after extraction from the injured spinal cord . To assess the effect of infiltrating cells on resident microglia , cells were isolated from spinal cord lesions prior to significant MDM infiltration ( one day ) and after infiltration ( three days ) . Immediately after isolation , cells were placed in vitro and incubated with pHrodo-labeled myelin for four hours . Flow cytometric analysis revealed that increasing numbers of MDMs at the lesion site correlates with a significant reduction in microglial phagocytosis ( Fig 1A–1E ) . We therefore hypothesized that MDMs entering the CNS signal to resident microglia and modulate their function . As an additional note , we detected significantly more MDMs one day after injury , as compared to naïve spinal cord ( S1B Fig ) . This is likely to represent macrophages in the process of entering the tissue , either through the vasculature or the meninges but before they have been reported to be seen in the parenchyma . It is possible that they could release signaling molecules from these locations that influence microglia , as seen in the reduction of phagocytosis at day one after SCI , compared to uninjured ( naïve ) mice ( Fig 1E ) . There is a further significant increase in infiltration of MDMs by day three , compared to one day after SCI ( S1B Fig ) . To directly test the hypothesis that MDMs modulate microglial function , we created a bilaminar culture system by plating bone marrow–derived macrophages ( BMDMs ) on coverslips with small paraffin spacers , which were then placed into wells containing adult microglia ( S2D Fig ) . Primary adult mouse microglia were cultured under conditions that retain a transcriptional profile more similar to their in vivo counterparts , as compared to other media conditions , primary microglial cultures from neonates or microglial cell lines [11] ( S2A and S2B Fig ) . Other features , such as genes that reflect region-specific factors or function , may be altered when cells are placed in culture . The addition of Transforming growth factor ( TGF ) -β was not only necessary for a gene expression profile more similar to freshly isolated microglia but also showed greater ramified morphology in culture at seven days ( S2C Fig ) . Microglia “signature” genes were down-regulated during lipopolysaccharide ( LPS ) -induced inflammation , which supports their description as homeostatic [11] . There is no further modulation of these microglial genes in the presence of macrophages ( S2E Fig ) . As a control for cell numbers , we assessed modulation of these microglial genes by coculturing with microglia instead of BMDMs ( S2F Fig ) . Suppression of inflammatory genes in adult microglia does not occur when cocultured with adult microglia . For these experiments , adult mouse microglia were cultured with or without adult microglia and stimulated with LPS ( 100 ng/mL ) ( S2F Fig ) . Using this bilaminar in vitro system , we assessed if soluble factors released by these two cell types affect phagocytic function in one another . Microglia and BMDMs were cocultured in the bilaminar system for 24 hours , separated , and incubated with pHrodo-labeled myelin for 90 minutes . Phagocytic uptake was assessed with flow cytometry . Uptake of pHrodo-labeled myelin was significantly decreased in microglia cultured in the presence of BMDMs compared with microglia cultured alone ( Fig 1F and 1G ) . Surprisingly , myelin phagocytosis by BMDMs was significantly increased after coculture with adult microglia ( Fig 1H and 1I ) . These findings reveal direct communication between the two cell types divergently affecting phagocytic function . We next investigated macrophage effects on inflammatory gene expression in microglia and macrophages from adult mice and humans . We recently described a mathematical model of cytokine signaling , which found four inflammatory cytokines , interleukin ( IL ) -1β , tumor necrosis factor ( TNF ) , IL-6 , and IL-10 , to be key nodes in the inflammatory network [24] . After LPS stimulation , coculture with BMDMs significantly down-regulated these genes in mouse and human microglia ( Fig 1J and 1L ) . In contrast , IL-1β expression was increased in LPS-stimulated macrophages cocultured with mouse microglia ( Fig 1K ) . There were nonsignificant trends towards increases in inflammatory cytokines in human macrophages in the combined presence of LPS and microglial cells ( Fig 1M ) , contrasting with the significant suppression of these genes in microglia in the same conditions ( Fig 1L ) . These experiments show the direct suppressive effects of macrophages on microglia , and reciprocal but divergent effects of microglia on macrophages . In the bilaminar culture system in mouse and human microglia , we found significant suppression of pro-inflammatory cytokines IL-1β , TNF , and IL-6 in the presence of macrophages ( Fig 1J and 1L ) . We also found a significant reduction in human microglia of IL-10 , a canonical brake on inflammation [25 , 26] . Despite IL-10’s opposing function to the pro-inflammatory cytokines , all four cytokines are increased with LPS 24 hours after stimulation . We have recently described the complexities of cytokine networks over time and shown how the modulation of one cytokine in the system may result in varying temporal kinetics of the others [26] . Here , we examined a single time point , but as all four cytokines were suppressed by the presence of macrophages , we sought to examine whether microglial transcription , in general , became globally suppressed by macrophages . To understand the global effects of macrophage suppression on microglia , we transcriptionally profiled LPS-activated adult mouse microglia in the presence or absence of macrophages . A total of 1 , 076 genes were significantly differentially regulated in activated microglia in presence of macrophages , with approximately 50% up-regulated and 50% down-regulated . Ingenuity pathway analysis ( IPA ) revealed that the most dysregulated canonical signaling pathways were those related to nuclear factor ( NF ) -κB signaling , a master regulator of inflammation ( Fig 2A ) and apoptosis and cell death ( Fig 2B ) . Network analysis revealed three major clusters of genes distributed across two distinct regions reflecting distinct gene co-expression patterns ( Fig 2C ) . In the major gene cluster 1 ( 185 genes ) , which comprised genes down-regulated within microglia in the presence of macrophages , we found that the top upstream regulators , predicted to be inhibited with high confidence , included MyD88 , IL-1β , and TNF ( Fig 2D ) . These analyses support our findings that gene expression within the major inflammatory cascades in microglia is suppressed in the presence of macrophages . As IL-1β was one of the key cytokines to be differentially regulated in microglia and macrophages in the coculture experiments ( Fig 1J and 1K ) , we searched for factors known to regulate IL-1β in microglia . Prostaglandin E2 ( PGE2 ) signaling via the EP2 receptor has been reported to reduce IL-1β expression in microglia [27] . In addition , EP2 receptor signaling has also been reported to reduce phagocytosis [28–30] . We therefore hypothesized that PGE2 signaling via microglial EP2 receptors could be responsible for the suppressive effects of macrophages . Inducible microsomal prostaglandin E synthase-1 ( mPGES ) and EP2 receptor were up-regulated during inflammation in mouse and human microglia and macrophages in vitro ( Fig 3A–3D and S3A Fig ) and in vivo in mice after SCI ( Fig 3E ) . EP2 receptor expression was also up-regulated 22-fold when assessed by transcriptional array ( S3A Fig ) . Transcript levels for EP1 and 4 were down-regulated in mouse microglia in vitro , suggesting they are not involved ( S3A Fig ) . mPGES and hydroxyprostaglandin dehydrogenase ( HPGD ) work in concert to regulate PGE2 production and release as HPGD converts PGE2 to its biologically inactive metabolite [31] . In human macrophages , mPGES was significantly increased and HPGD was significantly down-regulated during inflammation ( LPS ) when cocultured with microglia , compared with macrophages cultured alone ( without LPS ) ( Fig 3B ) , suggesting greater PGE2 production in stimulated macrophages in the presence of microglia . The expression of EP2 receptor in human microglia was not significantly up-regulated upon stimulation with LPS ( Fig 3D ) . However , the data show that it is expressed , allowing the cells to detect PGE2 . Taken together , these experiments show that the components needed to allow PGE2 signaling at the EP2 receptor in microglia are up-regulated during inflammation and may be utilized for macrophage–microglia communication . To functionally assess the role of the EP2 receptor , we treated adult mouse microglia with the EP2 specific agonist , Butaprost . Treatment of microglia significantly reduced TNF , IL-6 , and IL-10 in the same manner as the macrophage-mediated suppression of these genes ( Fig 3F ) . This contrasted with the effects of Butaprost on BMDMs , which only reduced TNF expression ( S3E Fig ) . Butaprost also significantly reduced phagocytosis by microglia ( Fig 3G ) . Macrophage suppression of microglial phagocytosis was rescued by the selective EP2 antagonist , PF-04418948 [32] ( Fig 3H ) . In addition , unlike wild-type ( WT ) BMDMs , macrophages that lack mPGES ( mpges −/− ) do not suppress microglial phagocytosis ( Fig 3I ) . Taken together , these results show that PGE2 produced by peripherally derived macrophages plays a major role in suppression of microglial phagocytic function via EP2 receptors . To investigate this mechanism in vivo , we performed SCI in WT and mpges −/− mice and assessed the phagocytic microglial response . In addition , as our in vitro data show that PGE2 derived from macrophages suppresses microglial phagocytosis , we performed SCI in C–C chemokine receptor type 2 ( CCR2 ) null mice to compare the microglial response in a lesion that contains very few MDMs ( see Fig 5A and 5B ) [33] . Three days after SCI in WT , mpges −/− , and CCR2 null mice , microglia were isolated from the lesion and ex vivo phagocytosis of pHrodo ( Green ) -myelin was quantified by flow cytometry ( Fig 4A and 4B ) . Microglia from WT mice after SCI ( i . e . , with macrophages and PGE2 present in the lesion ) , showed low levels of phagocytosis ( Fig 4A and 4B ) . In contrast , microglia from mpges −/− and CCR2 null mice showed significantly increased phagocytosis ( Fig 4A and 4B ) , indicating that in the absence of PGE2 , or the absence of macrophages in the lesion , microglial phagocytic function is increased . To assess the effect of blocking the EP2 receptor , in vivo , on microglial phagocytosis , we injected pHrodo-myelin into the brain ( corpus callosum ) of WT mice , together with vehicle or the EP2 receptor antagonist ( PF-04418948 ) . We assessed brain tissue three days after injection with immunofluorescence and confocal microscopy . In the corpus callosum of mice injected with pHrodo-myelin and vehicle , the area containing pHrodo-myelin is mainly populated with CD11b+ , Tmem119-negative cells ( MDMs ) ( Fig 4C ) . In mice injected with pHrodo-myelin and EP2 antagonist , there is a significant increase in Tmem119+ microglial cells in the area containing pHrodo-myelin ( Fig 4D and 4E ) . Many of these Tmem119+ microglia contain or are closely associated with the fluorescently tagged myelin ( Fig 4D and 4F ) . Importantly , the percentage of total microglial cells in the corpus callosum that are in contact with or contain pHrodo-myelin is significantly increased in EP2 antagonist–injected mice compared with controls ( Fig 4C , 4D and 4F ) . These data indicate that blocking the EP2 receptor pathway in vivo promotes microglial phagocytic activity and increases recruitment of microglia to the site of injury . In summary , in vivo and in vitro studies suggest macrophage production of PGE2 acts at the EP2 receptor to mediate suppression of microglial phagocytosis . We also sought to investigate macrophage effects on microglial cell death and proliferation , as microglia proliferate at the sites of CNS injury [14 , 34] . In vitro , the presence of macrophages did not significantly reduce microglial viability ( S4A Fig ) . However , when combined with inflammation ( LPS stimulation ) , macrophages significantly reduced viability , suggesting an increase in microglial cell death , compared with untreated microglia ( S4A Fig ) . This fits with our transcriptional profiling data , which show that apoptotic and cell death pathways are significantly dysregulated under similar conditions ( Fig 2B ) . These data suggest that macrophages can affect microglial apoptosis under inflammatory conditions in vitro and warrant further analysis . To investigate macrophage effects on microglial proliferation , we used Click-iT EdU assay ( Invitrogen ) , which incorporates 5-ethynyl-2′-deoxyuridine ( EdU; a nucleoside analog of thymidine ) to DNA during active DNA synthesis . We found no evidence that macrophages affect the proliferation of microglia in vitro with bilaminar cultures ( S4B and S4C Fig ) . We also depleted circulating macrophages prior to their infiltration after SCI using clodronate liposomes in LysM-eGFP mice to assess microglial proliferation with Ki67 . Clodronate significantly depleted eGFP+ infiltrating cells at the lesion site when assessed five days after injury ( S4D and S4E Fig ) , but this had no effect on microglial proliferation ( S4F and S4G Fig ) . To investigate whether macrophages modulated another important microglial function , namely rapid process extension towards microlesions , we induced laser lesions in organotypic hippocampal slice cultures ( OHSCs ) , as done previously [35] , in the presence or absence of BMDMs . We also investigated the initial reaction of microglial morphologies in an in vivo model of traumatic brain injury ( TBI ) [36] with local administration of Butaprost versus vehicle control . Initial process extension and acute morphological changes that are dependent on purinergic receptor signaling [6 , 36] are not affected in OHSCs by the presence of macrophages ( S4H–S4M Fig ) or altered by the EP2 agonist in TBI ( S4N–S4O Fig ) . In summary , coupled with our transcriptional profiling data , these results highlight that macrophages target specific pathways and functions in microglia , such as inflammation and apoptosis , but do not affect microglial proliferation or their rapid process extension response to injury . These findings may be of significance , as the rapid reaction of microglia in the early phases of injury are thought to be protective [35 , 37 , 38] . Our results suggest that peripheral macrophages do not interfere with this response . Mice that lack CCR2 cannot successfully recruit MDMs to traumatic CNS lesions [33] . We showed that this leads to increased microglial phagocytic activity ( Fig 4A and 4B ) . Therefore , we next assessed whether lack of MDM infiltration affects activation of microglia and functional recovery in vivo after SCI using CCR2 null mice [33] . Although CCR2 has been reported to be expressed in injured neurons , expression appears variable between species and between investigators [39–41] . We are not aware of other genetic evidence for CCR2 protein expressed in neurons [13 , 42] . CCR2 is also reported to be expressed in a subset of T-regulatory cells ( Tregs ) [43] . Although it is unknown what role CCR2+ Tregs play after SCI , it has been shown that T cells may play a beneficial role in CNS repair [44] . Despite this , a major phenotype five days after SCI was that MDMs were almost absent in the lesioned spinal cord of CCR2rfp/rfp ( CCR2 KO ) mice , as compared with WT mice ( Fig 5A and 5B ) . Neutrophil infiltration was not affected five days after injury ( Fig 5C ) . Three days after injury , there was a trend to an increase , but this was not statistically significant ( S4P Fig ) . To study the impact of the absence of MDMs on microglial-mediated inflammation , we isolated microglia from SCI lesions four and seven days after injury and assessed the expression profiles of 86 inflammatory genes using a PCR array . Four days after SCI in WT mice , approximately half of these genes were significantly down-regulated in microglia . Seven of the top 20 most down-regulated genes in WT mice were significantly less down-regulated in CCR2 KO mice , indicating that the absence of MDMs at the lesion resulted in less suppression of microglial inflammation ( Fig 5D ) . Moreover , pathways identified as being suppressed by macrophages in our in vitro bilaminar system , such as inflammation driven by MyD88 and NF-κB and apoptotic pathways driven by Trp53 and Bcl2 were also significantly more suppressed when MDMs were present at the lesion ( Fig 5D ) . Seven days after SCI , microglial inflammatory genes continued to be dysregulated ( Fig 5E ) . Components of important inflammatory pathways continued to be significantly less suppressed in the absence of macrophages , such as MyD88 , Il17a , and Cxcl2 . However , dysregulation of microglial gene expression was less unidirectional than factors associated with pro-inflammatory response , such as Irf1 , Tlr9 , and Il12b , and growth factors such as Egf and Tgfb1 associated with recovery were significantly more down-regulated in microglia , in the absence of infiltrating macrophages ( Fig 5E ) . Our previous work shows that initial perturbation to inflammatory networks is likely to cause unpredictable patterns of expression at later time points [24 , 26] . Therefore , to investigate the long-term consequence of the initial loss of microglial suppression and subsequent dysregulation , we assessed long-term activation of microglia and its impact on functional recovery after SCI in CCR2 KO versus WT mice ( Fig 6A–6G ) . Up-regulation of CD11b ( αM integrin ) is well established as a readout of microglial/macrophage activation [45–47] , and CD86 is a costimulatory receptor up-regulated during inflammation in microglia in vivo [48 , 49] . CD11b expression in microglial cells was already increased at a cellular level in mice lacking macrophage infiltration ( CCR2 KO ) seven days after SCI versus controls ( Fig 6A ) . There was a trend to an increase in CD86+ microglia but it did not reach statistical significance ( S4Q Fig ) . To assess microglial activation 28 days after injury , we quantified CD11b and CD86 expression by immunofluorescence of tissue sections caudal to the lesion epicenter ( Fig 6C and 6D ) . Importantly , 28 days after SCI , CD11b and CD86 immunoreactivity was greater in area and intensity in CCR2 KO mice despite the lack of infiltrating MDMs , which also express CD11b and CD86 ( Fig 6C and 6D ) . In other words , CD11b and CD86 expression is markedly increased in microglia in CCR2 KO mice 28 days after SCI . These results indicate that preventing the communication between MDMs and resident microglia contribute to long-term microglial activation after CNS injury . We also investigated whether increased microglial inflammation in the absence of infiltrating macrophages in CCR2 KO mice influences functional recovery and histopathology . CCR2 KO mice showed greater myelin loss , an indicator of secondary tissue damage , caudal to the lesion 28 days after SCI , compared with controls ( Fig 6E and 6F ) . The increased microglial activation associated with the absence of macrophage influx after SCI is associated with worse locomotor recovery in CCR2 KO mice compared with WT controls , as measured by the Basso Mouse Scale ( BMS ) ( Fig 6g ) .
The role of microglia in CNS injury and disease is now considered critical to the pathological process [50] . Our work suggests a novel concept that macrophages from the peripheral circulation , which enter the CNS after injury , may act to modulate microglial activation , thus preventing microglial-mediated acute and chronic inflammation . These findings support previous work that shows blocking CCR2-dependent macrophage infiltration with an anti-CCR2 antibody worsens locomotor recovery after CNS injury [51 , 52] . However , these earlier papers [51] did not show how macrophages mediate these effects . Our work now shows that infiltrating macrophages suppress microglial activation by reducing their expression of inflammatory molecules and ability to phagocytose , thus preventing chronic microglia-mediated inflammation in the CNS . Other work has suggested that subsets of infiltrating macrophages are detrimental to SCI [53] , and it has been reported that CCR2 antagonism , producing a 50% reduction in infiltrating macrophages , is beneficial after TBI [54] . Also , CCR2 KO mice showed acute and transient behavioral improvement after intracerebral hemorrhage ( one and three days ) , but this was not sustained at seven days [55] . Here , our finding that inhibition of macrophage entry to the CNS results in a worse outcome after SCI is supported by work that defines specific beneficial macrophage populations in multiple CNS injury and disease contexts [56–59] . Our results now suggest a new mechanism by which infiltrating macrophages mediate their beneficial actions via the regulation of microglial activation . Such a mechanism will operate alongside macrophage-intrinsic mechanisms . We observed that macrophages regulate microglia in both mouse and human cells . This is important as it represents an independent replication of the concept in a different laboratory . It shows that macrophages derived from the blood ( human ) or bone marrow ( mouse ) appear to have similar effects on microglia and that the findings may be relevant to human disease . It is still controversial as to whether the net effect of microglia is beneficial or detrimental to CNS injury [4 , 5 , 60]; however , the kinetics of the microglial response must be considered . There is evidence that the initial responses of microglia , which occur in the first few minutes to several hours after injury , are beneficial and limit the expansion of CNS lesions [35 , 37 , 38] . Conversely , prolonged microglial dysregulation and neuroinflammation are deleterious to the CNS [61] . Therefore , the initial microglial response to injury may be beneficial , but prolonged inflammation and activation are potentially detrimental to recovery . Our data suggest that macrophages play a role in mitigating this detrimental response by infiltrating the injury site and reducing microglial-mediated inflammation and chronic microglial activation . The absence of this protective mechanism may contribute to a worse outcome when infiltrating macrophages do not enter the CNS after SCI in CCR2 KO mice . To our knowledge , this is the first description of such a cellular mechanism to reduce deleterious consequences of CNS injury . Microglial cells are now prime targets in drug discovery for CNS injuries and neurodegenerative diseases [62] . To properly assess the roles of microglia in CNS injury , our data suggest that the context , timing , and interaction with macrophages should also be considered . Attempts to target either of these two cell populations should be approached with caution and a better understanding is needed of their divergent and complex roles in injury and disease . The heterogeneity and region-specific differences in microglia [63] and macrophage populations [64] will also need to be considered . Recent work has shown that peripherally derived macrophages can engraft the brain and maintain an identity distinct from microglia [65] , thus opening the possibility for therapeutic engraftment of MDMs to the CNS and allowing macrophage–microglia cross talk in disease contexts . Cell-to-cell interactions between different brain resident cell types are now becoming evident [66–69] . During inflammation , microglia have also been shown to drive astrocyte-mediated toxicity [66] , which , subject to the context , is dependent on microglial NF-κB signaling [67] . Our data show that peripheral macrophages regulate the NF-κB signaling pathway in microglia that , in turn , reduce inflammatory mediators , such as TNF , which can drive astrocyte-mediated toxicity [66 , 67] . This raises the possibility that macrophage signaling to microglia may have subsequent effects in other CNS cells , such as astrocytes . In summary , we suggest that infiltrating macrophages provide a natural control mechanism against detrimental acute and long-term microglial-mediated inflammation . Manipulation of peripherally derived infiltrating cells may provide a therapeutic treatment option to target microglial-mediated mechanisms that cause or exacerbate CNS injury and disease .
All animal procedures were approved by the Animal Care Committee of the Research Institute of the McGill University Health Centre and followed the guidelines of the Canadian Council on Animal Care and the ARRIVE guidelines for reporting animal research [70] . Before surgical interventions and cardiac perfusions , mice were deeply anesthetized by intraperitoneal injection of ketamine ( 50 mg/kg ) , xylazine ( 5 mg/kg ) , and acepromazine ( 1 mg/kg ) . Human brain tissue was collected during clinical practice , fully anonymized , and therefore available for use under the legislation of the Tri-Council Policy Statement two and Plan d'action ministériel en éthique de la reserche et en intégrité scientifique of Quebec and Canada . This study was carried out in accordance with the guidelines set by the Biomedical Ethics Unit of McGill University , approved under reference ANTJ2001/1 , and conducted in accordance with the Helsinki Declaration . C57BL/6 ( Charles River , St-Constant , QC ) , heterozygote lysM+/EGFP mice ( kindly provided by Dr . Thomas Graf and obtained from Dr . Steve Lacroix ) ; homozygote CCR2RFP/RFP and their C57BL/6J controls ( Jackson ) ; heterozygote Cx3CR1+/gfp ( Jackson ) and Ptges−/− mice [71] ( obtained from Dr . Maziar Divangahi , McGill University ) , aged 8–14 weeks , were kept under a 12-hour light/dark cycle with ad libitum access to food and water . The LysM-eGFP mouse was originally generated by Faust and colleagues , 2000 . EGFP is expressed specifically in the myelomonocytic lineage by using homologous recombination . This was achieved by knocking the enhanced GFP ( EGFP ) gene into the murine lysozyme M ( lys ) locus and using a targeting vector , which contains a neomycin resistant ( neo ) gene flanked by LoxP sites and “splinked” ends , to increase the frequency of homologous recombination . Removal of the neo gene through breeding of the mice with the Cre-deleter strain led to an increased fluorescence intensity [22] . Female mice were anesthetized by intraperitoneal injection of ketamine ( 50 mg/kg ) , xylazine ( 5 mg/kg ) , and acepromazine ( 1 mg/kg ) and a moderate contusion injury ( 50 kDa force; 500–600-μm tissue displacement ) was made at the T11 thoracic vertebral level using the Infinite Horizon Impactor device ( Precision Scientific Instrumentation , Lexington , KY ) , as previously described [72] . Male C57BL/6 mice ( 8–12 weeks ) were , anesthetized , transcardially perfused and brains removed and kept in ice-cold Hanks Balanced Salt Solution ( HBSS ) . Cerebellum and meninges were removed , and brain was cut into small pieces . Tissue was enzymatically dissociated using Neural Tissue Dissociation Kit ( P ) ( Miltenyi cat # 130-092-628 ) according to the manufacturer’s instructions , with modifications . Following digestion , tissue was transferred to a 15-mL Dounce on ice and homogenized with 20× passes of a large clearance pestle . Tissue was resuspended in 35% isotonic percoll and overlaid with HBSS . Following centrifugation ( 400g; 45 minutes ) , myelin was removed , and pure populations of microglial cells were selected using CD11b microbeads ( Miltenyi #130-093-634 ) , as previously described [63 , 73] . Pure ( >95% CD11b-positive ) adult microglia were resuspended at 8×105 cell−mL ( approximately two brains per mL ) in media ( DMEM F12 , 10% fetal bovine serum [FBS] , 1% penicillin/streptomycin [P/S] ) , with 10% L-cell conditioned media , a source of macrophage colony-stimulating factor ( M-CSF ) , or 10 ng/mL recombinant mouse M-CSF ( R and D cat no 416-ML-010/CF ) , and 50 ng/mL recombinant human TGF-β1 ( Miltenyi cat no: 130-095-067 ) to maintain their transcriptional profile , as previously described [11] . Cells were plated in pre-coated poly-L-lysine plates , media was changed at three days , and experiments were performed at seven days . At seven days , microglia were collected and microglial “signature” genes assessed by qPCR . Network analysis and Markov clustering ( see below ) were performed to assess conditions driving cells to a similar phenotype of their freshly isolated counterparts . CD11b+ cells ( myeloid cells ) were collected by magnetic bead cell sorting , as above , from SCI lesions of lys-EGFP-ki mice ( 2 . 5 mm either side of the epicenter ) at 1 or 3 days after injury , or from uninjured controls . The CD11b+ fraction was immediately plated into 96 well plates ( pre-coated poly-L-lysine , one animal per well ) in DMEM F12 media containing 10% FBS . Cells were incubated for four hours with pHrodo ( Invitrogen ) -labeled myelin and taken for FACS analysis . BMDMs were generated as previously described [74] from adult male C57/BL6− or Ptges−/− mice . Mice were euthanized , and their femurs were removed . Bone marrow was flushed out and homogenized , and RBCs were hypotonically lysed . After washing , cells were cultured in RPMI media containing 10% FBS , 10% L-cell-conditioned media , and 1% P/S for seven days . Adult microglia and BMDMs ( from the same animal when possible ) were cultured separately for seven days , as described above; BMDMs were then replated on poly-L-lysine–coated glass coverslips . Cells were plated at 2×105 per 25-mm coverslip ( for insertion in 6 well plates ) of 4×104 per 12-mm coverslip ( for insertion in 24 well plates ) . These numbers were calculated to match the number of microglia in the same well as , three days after SCI in vivo , the proportion of microglia to macrophages around the lesion ( 54% ± 8% versus 46% ± 8% ) . Therefore , equal numbers of microglia and BMDMs were plated into wells in vitro . Coverslips were pre-mounted with 3 small paraffin droplets on the same surface as the cells were plated , as previously described for neuron-astrocyte cocultures [75] . BMDMs were allowed to adhere overnight . Bilaminar culture experiments began by inserting coverslips containing BMDM and wax paraffin nodule face down into culture plates containing adult microglia . All experiments were performed in DMEMF12 serum-free media . Adult microglia and BMDMs were cocultured in the bilaminar system , as above , for 24 hours . At 24 hours , BMDM coverslips were removed from the microglial wells and placed in new cell culture wells . Following separation , both adult microglia and BMDMs were incubated with pHrodo Red ( ThermoFisher , Mississauga , ON ) labeled myelin for 90 minutes . pHrodo Red is weakly fluorescent at neutral pH but increasingly fluorescent as the pH drops , therefore an increasing signal can be detected because of pHrodo-myelin entering the lysosome . After treatment , cells were trypsinized , harvested , and labeled for FACS analysis . Adult microglia and BMDMs were cocultured in the bilaminar system , as above . Wells containing microglia and/or BMDMs were treated with vehicle or LPS ( 100 ng/mL ) for four hours . Cells were then washed and fresh , serum-free media was added to each well for a further 20 hours . At 24 hours , supernatants were harvested and cells lysed with 350 μL of RLT lysis buffer ( Qaigen , Germantown , MD ) and snap frozen until RNA extraction . For the in vitro proliferation assay , microglia rather than BMDMs were plated on coverslips . Cells were treated as above , with the addition of Click-iT EdU ( 20 μM ) at the time of LPS administration . EdU is a nucleoside analog of thymidine that is incorporated into DNA during active DNA synthesis and was used according to the manufacturer’s instructions ( Invitrogen , Carlsbad , CA ) . To obtain MDMs , monocytes were isolated from healthy human venous blood . PBMCs were isolated from whole blood using Ficoll–Paque density gradient centrifugation ( GE Healthcare , Piscataway , NJ ) . CD14+ monocyte isolation was done using immune-magnetic bead selection according to the manufacturer’s instructions to achieve 95%–99% purity ( Miltenyi Biotec ) , as determined by flow cytometry . Cells were cultured in RPMI ( Invitrogen , Carlsbad , CA ) supplemented with 10% FBS , 0 . 1% P/S , and 0 . 1% L-glutamine . Cells were plated at a density of 5×105 cells/mL in 6-well tissue culture plates for four days and matured in vitro to become MDMs , using supplementation with recombinant M-CSF ( 25 ng/mL , PeproTech , Rocky Hill , NJ ) . Human microglia were isolated from adult brain tissue using previously described protocols [76] . Adult microglia were derived from surgical resections of brain tissue from pharmacologically intractable nonmalignant cases of temporal lobe epilepsy . The tissue provided was outside of the suspected focal site of epilepsy-related pathology . Briefly , tissue was obtained in pieces <1 mm3 and treated with DNase ( Roche , Nutley , NJ ) and trypsin ( Invitrogen , Carlsbad , CA ) for 30 min at 37 °C . Following dissociation through a nylon mesh ( 37 μm ) , the cell suspension was separated on a 30% Percoll gradient ( GE Healthcare , Piscataway , NJ ) at 31 , 000g for 30 minutes . Glial cells ( oligodendrocytes and microglia ) were collected from underneath the myelin layer , washed , and plated at a density of 2×106 cells/mL in tissue culture flasks . After 24 hours in culture , microglia were separated by the differential adhesion properties of the cells . Microglia were grown for four days in flasks before gently collecting using 2 mM EDTA ( Sigma-Aldrich , Oakville , ON ) ; cells were then plated in minimum essential medium ( MEM , Sigma-Aldrich , Oakville , ON ) supplemented with 5% FBS , 0 . 1% P/S , and 0 . 1% L-glutamine at a density of 5×105 cells/mL on a glass 25-mm cell culture insert in a 6-well plate . Following addition of microglia to the MDM-containing well , as described above in the bilaminar coculture system , cells were exposed to 100 ng/mL lipopolysaccharide ( serotype 0127:B8 , Sigma-Aldrich ) for 24 hours before collection in Trizol Reagent ( Invitrogen , Carlsbad , CA ) for subsequent total RNA isolation using the Qiagen RNeasy mini kit following the manufacturer's instructions . RNA isolated was treated immediately with DNase ( Qiagen , Germantown , MD ) . Reverse transcription and cDNA generation were performed using random hexaprimers ( Roche ) and the Moloney murine leukemia virus-RT enzyme ( Invitrogen , Carlsbad , CA ) at 42 °C . PCR reaction cycling was performed according to the ABI PRISM 7000 Sequence Detection System default temperature settings ( two minutes at 50 °C , 10 minutes at 95 °C , followed by 40 cycles of 15 seconds at 95 °C , one minute at 60 °C ) . TaqMan quantitative real-time PCR was used to measure mRNA expression levels for all mRNAs . Relative gene expression data were calculated according to the 2−ΔΔCt method . For phagocytosis assay after treatment with EP2 receptor agonist , adult microglia were treated for one hour with Butaprost ( 1 μM ) ( Cayman Chemicals , Ann Arbor , MI ) prior to incubation with pHrodo-myelin . For treatment with EP2 antagonist , bilaminar cocultures of adult microglia and BMDMs were treated with PF-04418948 , ( 10 μM ) ( Sigma-Aldrich , Oakville , ON ) prior to incubation with pHrodo-myelin . For treatment of adult microglia or BMDMs with Butaprost during inflammation , wells containing microglia or BMDMs were treated with vehicle or LPS ( 100 ng/mL ) and Butaprost ( 1 μM ) for four hours . Cells were then washed and fresh , serum-free media containing Butaprost ( 1 μM ) was added to each well for a further 20 hours . At 24 hours , supernatants were harvested and cells lysed with 350 μL of RLT lysis buffer ( Qaigen , Germantown , MD ) and snap frozen until RNA extraction . Injections were made into the right motor cortex of eight-week-old female C57/BL6J mice . A 2 × 2 mm opening was made in the skull just to the right side of the midline and just below bregma . A 26G needle attached to a 10-μL World Precision Instruments NanoFil syringe was used . The needle was inserted into the cortex just deep enough for the entire bevel of the needle to be inside the brain—a depth of approximately 0 . 46 mm . The EP2 receptor antagonist ( PF-04418948 ) ( Sigma-Aldrich , Oakville , ON ) was initially dissolved in 100% DMSO to a 50-mM stock solution . This solution was diluted in PBS , pH 8 . 0 , for a final injection concentration of 1 uM . The vehicle controls were a corresponding dilution of DMSO in PBS , pH 8 . 0 . The total volume injected was 2 μL . Each injection contained 20 μg of pHrodo-labeled myelin . The pHrodo-myelin was prepared as follows: 18 . 5 μL myelin ( 15 mg/mL ) + 25 μL of pHrodo was suspended in 206 . 5 μL of PBS , pH 8 . 0 , and incubated 45 minutes at RT in the dark on the rocker . Samples were then spun down at 4000g for 10 minutes and resuspended in 25 μL of PBS plus either vehicle or antagonist . Three days after injection , mice were humanely killed and transcardially perfused , as described below . Cultures were prepared as previously described [77] . Briefly , 300-μm hippocampal slices were prepared from P6-7 CX3CR1gfp/+ mice and cultured on semiporous nylon membranes ( Millipore , Bedford , MA ) for 9–11 days , with medium changes every two days . The culture medium contained 6 . 5 mg/mL glucose , 25% HBSS , 25% horse serum , 50% minimum essential medium with Glutamax , and 0 . 5% P/S ( Gibco , Mississauga , ON ) . Five experimental conditions were assessed: ( 1 ) control slices in serum-free slice culture medium , ( 2 ) OHSCs incubated with a feeder layer of BMDMs for 24–36 hours , ( 3 ) OHSCs incubated with a feeder layer of BMDMs pre-stimulated with LPS ( 100 ng/mL , two hours ) for 24–36 hours , ( 4 ) OHSCs incubated with BMDMs added directly onto the slice ( 5×105/slice ) , for 24–36 hours , and ( 5 ) OHSCs incubated with a LPS pre-stimulated ( 100 ng/mL , two hours ) BMDMs added directly onto the slice ( 5×105/slice ) for 24–36 hours prior to laser lesioning . Laser lesions and imaging of microglial responses were performed with a customized Olympus FV1200MPE equipped with a MaiTai HP DeepSee-OL IR laser and a 25× XLPlan N objective . Slice cultures were perfused at 1–2 mL/minute with room temperature artificial cerebral spinal fluid containing ( in mM ) NaCl ( 126 ) , NaHCO3 ( 26 ) , KCl ( 2 . 5 ) , NaH2PO4 ( 1 . 25 ) , glucose ( 10 ) , MgCl2 ( 2 ) , and CaCl2 ( 2 ) , corrected for osmolarity with sucrose . Laser lesions and imaging were performed sequentially at 820 nm , which provided excitation of both GFP and lipofuscin [35] . Lesions were performed in a 3-μm circular ROI at a depth of 40–70 μm in the central plane of the imaged volume , using 2–7 scans at 60% laser power , with a pixel dwell time of 10 μs . Post-lesion images were 30-μm z-stacks with a 1 . 5-μm step size , once per minute for 15 minutes . Analysis was performed with MTrackJ for ImageJ [78] . For mTBI experiments , CX3CR1gfp/+ mice were anesthetized with ketamine , xylazine , and acepromazine and maintained at a core temperature of 37 ºC . Hair was removed from the head using hair clippers and Nair . An incision was made in the scalp to expose the skull and a metal bracket was secured on the skull bone over the barrel cortex . The bone was quickly thinned to a thickness of about 20–30 μm . Once thinned , the blunt end of a microsurgical blade was used to compress the skull bone into a concavity without cracking the skull . Artificial spinal fluid ( ACSF ) or 1 μM Butaprost ( Cayman Chemicals , Ann Arbor , MI ) in ACSF was immediately applied to the skull after mTBI and kept on throughout the imaging experiment . Mice that had mTBI procedures were imaged using a Leica SP8 two-photon microscope equipped with a 12 , 000-Hz resonant scanner , a 25× color corrected water-dipping objective ( 1 . 0 NA ) , a quad HyD external detector array , and a Mai Tai HP DeepSee Laser ( Spectra-Physics , Santa Clara , CA ) tuned to 905 nm . Three-dimensional time-lapse movies were captured in z-stacks of 15–30 planes ( 3-μm step size ) at 1–2-minute intervals . Signal contrast was enhanced by averaging 10–12 video frames per plane in resonance scanning mode . Three-dimensional time-lapse movies were imported for analysis into Imaris software ( Bitplane ) . “Jellyfish” microglia were quantified by counting cells that had a process that was 20 μm or more in diameter at any time within about two hours of imaging immediately following mTBI . This number was then divided by the area of reactive microglia for each mouse . Clodronate liposomes used to deplete the population of infiltrating macrophages were purchased through the website , www . clodronateliposomes . com , and were prepared by Nico Van Rooijen in the Netherlands . Clodronate liposomes were injected on the day of spinal cord contusion and at days 1 , 2 , 3 , and 4 after the injury . For each treatment , 100 μL/10 g body weight was administered intravenously ( IV ) and 50 μL/10 g intraperitoneally ( IP ) . Saline injections were administered to control animals at the equivalent time . For analysis of cells from ex vivo experiments , cells were harvested with trypsin and stained with eflouro eFluor780 viability dye ( 1:1 , 000; eBioscience , Mississauga , ON ) , blocked with FC-receptor blocked ( 1:200; BD Bioscience ) , and stained with CD11b-V450 , Ly6G-PerCP-Cy5 . 5 ( all 1:200; BD Bioscience , Mississauga , ON ) . LysM-GFP and pHrodo Red labeled myelin were detected in the FITC and PE channels , respectively . CD11b+/Ly6G−/LysM-GFP− ( microglia ) CD11b+/Ly6G−/LysM-GFP+ ( macrophages ) were assessed for pHrodo-labeled myelin; the content was detected in the PE channel . For analysis of cells from in vitro experiments , adult microglia or BMDMs were harvested with trypsin and stained with eflouro eFluor780 viability dye ( 1:1 , 000; eBioscience , Mississauga , ON ) , blocked with FC-receptor blocked ( 1:200; BD Bioscience ) , and stained with CD11b-V450 ( 1:200; BD Bioscience , Mississauga , ON ) . pHrodo-labeled myelin was detected in the PE channel . For analysis of cells from in vivo experiments , spinal cord tissue was harvested and transferred to a 15-mL Dounce homogenizer on ice and homogenized with 20× passes of a large clearance pestle . Tissue was resuspended in 70% and overlaid 30% isotonic Percoll . Following centrifugation ( 900g; 25 minutes ) , cells at the Percoll interface were collected , washed , and resuspended . Cells were blocked with FC-receptor blocked ( 1:200; BD Bioscience , Mississauga , ON ) and stained with CD45-PE-Cy7 , CD11b-V450 , Ly6G-APC , or FITC , Ly6C-APC-Cy7 ( all 1:200; BD Bioscience , Mississauga , ON ) . CCR2-RFP was detected in the PE channel . Cells were acquired using a BD FACS Canto II and analyzed using FlowJo software . For fluorescently activated cell sorting ( FACS ) , a BD FACSAria Fusion ( BD Bioscience , Mississauga , ON ) was used to isolate CD11b+ve/CD45lo/Ly6G−ve/Ly6C−ve microglial cells , which were directly sorted in Trizol for subsequent RNA extraction . All gating strategies not contained within the figures are presented in the Supporting information ( S5 Fig ) . Total RNA from FACS-sorted microglia was extracted from WT and CCR2-KO spinal cord injured mice , four and seven days after injury , or genotype-matched uninjured controls , as described previously [79] . Briefly , CD45hi/CD11b+ve/Ly6G−ve/Ly6C−ve microglial cells were recovered in 700 μL of Trizol , vortexed , centrifuged , snap frozen in dry ice , and kept at −80 °C until further use . On the day of extraction , samples were thawed and 140 μL of chloroform ( Fisher ) was added , vortexed for 15 seconds , and centrifuged at 12 , 000g at 4 °C for 15 minutes . The clear aqueous layer was recovered and 1 μL of Glycoblue ( 20 mg mL–1 , Ambion; AM9515 ) was added , followed by 1:1 volume of isopropanol ( Thermofisher Mississauga , ON ) ( approximately 400 uL ) . Samples were kept overnight at −20 °C to precipitate the RNA . The next day , the samples were centrifuged at maximum speed for 20 minutes at 4 °C . At this point , a blue pellet of RNA is visible . The pellet was washed twice with 75% RNAse-free ethanol; it was let dry at room temperature and resuspended in 14 μL of RNAse-free H2O . To assess inflammatory and immune-related genes , an RT2 Profiler PCR array was used ( Qiagen; PAMM-181Z ) , following instructions provided by the manufacturer . This array screened for 84 genes , and the data obtained were analyzed with the online Qiagen analysis software ( RT2 profiler PCR array data analysis V3 . 5 ) , http://www . qiagen . com/shop/genes-and-pathways/data-analysis-center-overview-page/ . We performed quantitative real-time polymerase chain reactions ( RT-qPCRs ) to assay for the expression levels of multiple transcripts . RNA was extracted from cultured adult microglial BMDMs using the RNeasy Mini Kit ( Qiagen , Germantown , MD ) . For spinal cord tissue , samples were homogenized , and total RNA was extracted using the RNeasy Lipid Tissue Kit ( Qiagen , Germantown , MD ) . Reverse transcription was performed with the Quantinova Reverse Transcription Kit ( Qiagen , Germantown , MD ) , and qPCR was performed using 1 μL of cDNA with Fast SYBR Green Master Mix ( Applied Biosystems , CA ) on a Step-One Plus qPCR machine ( Applied Biosystems ) . Peptidylprolyl isomerase A ( PPIA ) was used as an internal control gene . The 2−ΔΔCt method was used to calculate the cDNA expression fold change following standardization relative to PPIA [24 , 80] . All primers had a Tm of 60°C . Primer sequences were as follows: Mouse p2ry12 forward: 5′ CTG GGA CAA ACA AGA AGA AAG G 3′ p2ry12 reverse: 5′ CCT TGG AGC AGT CTG GAT ATT 3′ mertk forward: 5′ CCT CCA CAC CTT CCT GTT ATA TT 3′ mertk reverse: 5′ TGT TGC TCA GAT ACT CCA TTC C 3′ itgb5 forward: 5′ GGA TCA GCC AGA AGA CCT TAA T 3′ itgb5 reverse: 5′ AAT CTT CAG ACC CTC ACA CTT C 3′ gas6 forward: 5′ AGG AGA CAG TCA AGG CAA AC 3′ gas6 reverse: 5′ TTG AGC CTG TAG GTA GCA AAT C 3′ fcrls forward: 5′ AAT CAC ATT CTC CTG GCA TAG G 3′ fcrls reverse: 5′ GCA TGG CTT TCC CTG ATA GT 3′ c1q forward: 5′ GAA AGG CAA TCC AGG CAA TAT C 3′ c1q reverse: 5′ GGT GAG GAC CTT GTC AAA GAT 3′ Tnf forward: 5′ TTG CTC TGT GAA GGG AAT GG 3′ Tnf reverse: 5′ GGC TCT GAG GAG TAG ACA ATA AAG 3′ Il6 forward: 5′ CTT CCA TCC AGT TGC CTT CT 3′ Il6 reverse: 5′ CTC CGA CTT GTG AAG TGG TAT AG 3′ Il1b forward: 5′ ATG GGC AAC CAC TTA CCT ATT T 3′ Il1b reverse: 5′ GTT CTA GAG AGT GCT GCC TAA TG 3′ Il10 forward: 5′ ACA GCC GGG AAG ACA ATA AC 3′ Il10 reverse: 5′ CAG CTG GTC CTT TGT TTG AAA G 3′ Ptges forward: 5′ CCA CAC TCC CTC TTA ACC ATA AA 3′ Ptges reverse: 5′ GCC AGA ATT GTA GGT AGG TCT G 3′ EP1 forward: 5′ CTC TCG ACG ATT CCG AAA GAC 3′ EP1 reverse: 5′ GTG GCT GAA GTG ATG GAT GA 3′ EP2 forward: 5′ GCC TTT CAC AAT CTT TGC CTA CAT-3′ EP2 reverse: 5′ GAC CGG TGG CCT AAG TAT GG-3′ EP4 forward: 5′ CAA GCA TGT CCT GTT GCT TAA C 3′ EP4 reverse: 5′ GTC GGT TCA GCT ACG CTT TA 3′ Total RNA was quantified using a NanoDrop Spectrophotometer ND-1000 ( NanoDrop Technologies ) and its integrity was assessed using a 2100 Bioanalyzer ( Agilent Technologies ) . Sense-strand cDNA was synthesized from 100 ng of total RNA , and fragmentation and labeling were performed to produce ssDNA with the Affymetrix GeneChip WT Terminal Labeling Kit according to the manufacturer’s instructions ( ThermoFisher-Affymetrix , Mississauga , ON ) . After fragmentation and labeling , 3 . 5 μg DNA target was hybridized on Mouse Clariom S Assay ( ThermoFisher-Affymetrix ) and incubated at 450 °C in the Genechip Hybridization oven 640 ( ThermoFisher-Affymetrix ) for 17 hours at 60 rpm . Clariom S were then washed in a GeneChips Fluidics Station 450 ( ThermoFisher-Affymetrix ) using Affymetrix Hybridization Wash and Stain kit according to the manufacturer’s instructions ( ThermoFisher-Affymetrix ) . The microarrays were finally scanned on a GeneChip scanner 3000 ( Affymetrix ) . Microarray data sets were normalized by the robust multiarray averaging ( RMA ) method in Affymetrix Expression Console ( Affymetrix ) . To assess whether there were transcripts differentially expressed between activated microglia and activated microglia in the presence of macrophages , normalized data sets were compared in Affymetrix Transcriptome Analysis Console ( TAC ) Software . To assess whether there were transcripts differentially expressed between LPS-stimulated microglia and LPS-stimulated microglia in the presence of macrophages , normalized data sets were compared by ANOVA with a p < 0 . 05 cutoff . Differentially regulated gene lists were then assessed with the IPA software tool ( Qiagen ) . To investigate gene co-expression relationships between groups , a pairwise transcript-to-transcript matrix was calculated in Miru from the set of differentially expressed transcripts using a Pearson correlation threshold r = 0 . 86 . A network graph was generated in which nodes represent individual probe sets ( transcripts/genes ) , and edges between them the correlation of expression pattern , with Pearson correlation coefficients above the selected threshold . The graph was clustered into discrete groups of transcripts sharing similar expression profiles using the MCL algorithm ( inflation , 2 . 2; minimum cluster size , 10 nodes ) . After SPI in mice , locomotor recovery was evaluated in an open field test using the 9-point BMS [81] . The BMS analysis of hind limb movements and coordination was performed by two individuals who were trained in the Basso laboratory , and the consensus score was taken . The final score is presented as mean ± SEM . The 11-point BMS subscore was also assessed . Animals were perfused with 4% paraformaldehyde in 0 . 1 M PBS , pH 7 . 4 , at three , five and 28 days . Spinal cord segments containing the lesion site , or whole brain from pHrodo-myelin injected mice , were removed and processed for cryostat sectioning ( 20-μm-thick cross sections ) . Immunofluorescence was performed using rat anti-CD11b ( 1:250; Serotec , Raleigh , NC ) , rabbit anti Iba1 ( 1:500; Wako , Richmond , VA ) , anti-rat CD86 ( 1:200; BD Bioscience , Mississauga , ON ) , rabbit anti-P2ry12 ( 1:500; kindly provided by Dr . Oleg Butovsky ) , rabbit anti-Tmem119 ( 1:1 hybridoma supernatant; kindly provided by Dr . Mariko Bennett , Ben Barres Lab ) , rabbit anti-Ki67 ( 1:500; Abcam , Cambridge , MA ) , and chicken anti-GFP ( 1:500; Abcam , Cambridge , MA ) and detected using the appropriate secondary antibodies at 1:500; anti-rabbit Alexa Fluor 568 or 647 , anti-rat Alexa Fluor 568 or 647 , and anti-chicken Alexa Fluor 488 ( Invitrogen , Carlsbad , CA ) . All images were visualized using a confocal laser scanning microscope ( FluoView FV1000; Olympus ) using FV10-ASW 3 . 0 software ( Olympus ) and prepared with ImageJ . Histochemical staining with Luxol fast blue was used to assess myelin loss 28 days after injury . Myelin was quantified as a measure of Luxol fast blue ( mean gray value , ImageJ ) across the whole cross section , measured at 200-μm intervals over the 2-mm length of the cord . In spinal cord sections 28 days after injury , single plane images at a depth of 8 μm were acquired at two sites either side of the midline and included white and gray matter , as depicted in Fig 4 . Images were taken 600 μm caudal to the lesion epicenter to avoid overt tissue cavities but include putative areas of MDM and microglia cell interactions . Images were acquired and analyses performed whilst blinded to genotype . Area of fluorescence and fluorescence intensity ( as measured by Integrated Density [IntDen] , which is the product of area and mean gray value ) were quantified with ImageJ . For quantification of pHrodo-myelin injected mice , images were acquired from three consecutive slices around the injection site and analyses performed whilst blinded to treatment . The corpus callosum provided a boundary at which microglia cells and their association with pHrodo were quantified . All relevant data are within the paper and its Supporting information files ( S1 Data ) . Microarray data are available at Gene Expression Omnibus ( accession number GSE102482 ) . Data were analyzed using one-way and two-way ANOVAs with Bonferroni correction or Student t tests when appropriate and as indicated . Data were checked for compliance with statistical assumptions for each test , including normal distribution and equal variances across groups . Tests were two-tailed throughout . Statistical significance was considered at p < 0 . 05 . Data show mean ± SEM . | The immune and the central nervous systems are now thought to be inextricably linked . In response to injury , the immune system shapes CNS recovery through a complex of molecular and cellular mediators . However , it is unclear how the kinetics , magnitude , and components of this response can be harnessed to improve CNS restoration . The two immune cells that dominate CNS lesions are resident microglia—already present before the injury—and infiltrating macrophages , which enter from the blood after injury . Both cells are thought to be critical to the outcome , yet it is unknown if , or how , they interact . To investigate this , we used mouse and human cells in microglia–macrophage coculture systems and an in vivo model of traumatic spinal cord injury . We show that infiltrating macrophages suppress key functions of microglia , such as removal of tissue debris and propagation of inflammation . Preventing macrophage–microglia communication increases microglial activation and worsens recovery . We suggest that infiltrating macrophages from the blood provide a natural control mechanism against detrimental acute and long-term microglial-mediated inflammation . Manipulation of the peripheral macrophages may provide a therapeutic treatment option to target microglial-mediated mechanisms that cause or exacerbate CNS injury and disease . | [
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] | 2018 | Peripherally derived macrophages modulate microglial function to reduce inflammation after CNS injury |
Gene drives have enormous potential for the control of insect populations of medical and agricultural relevance . By preferentially biasing their own inheritance , gene drives can rapidly introduce genetic traits even if these confer a negative fitness effect on the population . We have recently developed gene drives based on CRISPR nuclease constructs that are designed to disrupt key genes essential for female fertility in the malaria mosquito . The construct copies itself and the associated genetic disruption from one homologous chromosome to another during gamete formation , a process called homing that ensures the majority of offspring inherit the drive . Such drives have the potential to cause long-lasting , sustainable population suppression , though they are also expected to impose a large selection pressure for resistance in the mosquito . One of these population suppression gene drives showed rapid invasion of a caged population over 4 generations , establishing proof of principle for this technology . In order to assess the potential for the emergence of resistance to the gene drive in this population we allowed it to run for 25 generations and monitored the frequency of the gene drive over time . Following the initial increase of the gene drive we observed a gradual decrease in its frequency that was accompanied by the spread of small , nuclease-induced mutations at the target gene that are resistant to further cleavage and restore its functionality . Such mutations showed rates of increase consistent with positive selection in the face of the gene drive . Our findings represent the first documented example of selection for resistance to a synthetic gene drive and lead to important design recommendations and considerations in order to mitigate for resistance in future gene drive applications .
Naturally occurring gene drives—selfish genetic elements that are able to bias their own inheritance and rapidly invade a population , even starting from very low frequencies—have inspired proposals to harness their power to spread into a population of insect disease vectors traits that manipulate their biology in ways that could suppress or eliminate disease transmission [1–4] . In particular for malaria , transmitted exclusively by mosquitoes of the Anopheles genus , historical gains in reducing the disease burden have been largely achieved by the correct implementation of vector control measures ( residual insecticides and bed nets ) [5] . Though these measures have been instrumental in substantially reducing malaria transmission , they are insufficient by themselves to eradicate the disease in the near future at the current level of investment [6] . Gene drive technology could help in developing a self-sustaining , species-specific and affordable vector control measure much needed to achieve disease eradication in the future . Gene drives based on the activity of DNA nucleases able to recognise specific target sequences were first proposed over a decade ago and have received much attention recently due to the advent of new , easily programmable nucleases such as CRISPR-Cas9 that have allowed us and others to build functioning gene drives that show rates of inheritance from a heterozygous parent close to 100% , compared to the expected Mendelian inheritance of 50% [1 , 7–9] . The principle behind the technology is to re-program a nuclease to cleave a specific site of interest in the genome and to insert the nuclease within this recognition site . The gene drive is designed to be active in the germline , so that in diploid organisms heterozygous for the gene drive the nuclease causes a double stranded break ( DSB ) at the target site on the homologous chromosome not containing the gene drive . The DSB can be repaired either by simple end-joining ( EJ ) of the broken strands or via homology-directed repair ( HDR ) where the DSB is resected and the intact chromosome used as a template to synthesise the intervening sequence . In the case of a gene drive , repair via HDR thus leads to a copying of the drive from one chromosome to another and the conversion of a heterozygote into a homozygote . Hence the force of gene drive is determined by a combination of the rate of cleavage of the nuclease in the germline , and the propensity for the cell machinery to repair the broken chromosome by HDR . We and others have shown that in germline cells the rates of HDR following a nuclease-induced DSB can be almost two orders of magnitude greater than EJ , a fact which explains the extraordinarily high rates of gene drive inheritance observed [7 , 8 , 10 , 11] . On the other hand EJ repair can lead to the creation of small insertions or deletions at the target site that , although occurring initially at low frequency , might be expected to be selected for in the target organism if they prevent the gene drive nuclease acting and there is a negative fitness cost associated with the gene drive [1 , 7 , 11–13] . This possibility has been recognised since the first proposal of this type of gene drive [1] , with much theory being dedicated to it recently [13 , 14] and recent empirical evidence of its occurrence in Drosophila[10] . To lower the likelihood of resistance arising there are several potential mitigation strategies including , but not limited to , the targeting of conserved sequences that are less tolerant of mutations and the targeting of multiple sequences , akin to combination therapy [1 , 12] . We previously developed a gene drive designed to spread into a mosquito population and at the same time reduce its reproductive potential by disrupting a gene essential for female fertility , thus imposing a strong fitness load on the population [11] . To investigate the long term dynamics of the emergence of resistance to a gene drive imposing such a load we continued to monitor the frequency of this gene drive over generations and analysed the target locus for evidence of mutagenic activity that could lead to the development of resistant alleles that block gene drive activity and restore gene functionality . Our findings show that a range of different resistant alleles can be generated and some of these are subsequently selected for and show dynamics consistent with our modelling predictions . These results provide a quantitative framework for understanding the dynamics of resistance in a multi-generational setting and allow us to make recommendations for the improvement of future gene drive constructs that relate to choice of target site and regulation of nuclease expression in order to retard the emergence of resistance .
A proof-of-principle CRISPR-based gene drive designed for population suppression was previously developed in our laboratory ( Fig 1A ) . This gene drive disrupted a haplosufficient gene ( AGAP007280 , the putative mosquito ortholog of nudel [15] ) required in the soma and essential for female fertility [11] . The gene drive also contained an RFP marker gene for the visual detection of individuals inheriting the drive . In our experiments individuals heterozygous for the gene drive transmitted the drive , regardless of their sex , to more than 99% of their offspring . We observed in these mosquitoes a marked reduction in fertility ( ~90% ) in females heterozygous for the drive , due to ectopic expression of the nuclease under control of the germline vasa2 promoter that resulted in conversion to the null phenotype in somatic cells . In spite of this fitness disadvantage experimental data showed that the gene drive could increase rapidly in frequency in a caged population due to the exceptionally high rates of inheritance bias . From a starting population ( G0 ) in two duplicate cages of 600 individuals with a 1:1 ratio of transgenic heterozygotes and wild type individuals , the gene drive progressively increased in frequency to 72–77% by G4 . This rate of increase was slightly higher than predicted by a deterministic model but within the limits of stochastic variation expected [11] . Due to a combination of the partial dominance of the sterility phenotype in heterozygous females and the previously documented generation of target site mutations conferring resistance to the gene drive [11] , this first gene drive was not expected to maintain high levels of invasion . Nonetheless it represented a useful experimental model to investigate the long term dynamics of the de novo generation of target site mutations and their selection at the expense of a gene drive imposing a large reproductive load . We therefore maintained this cage experiment for 25 generations and used the presence of the RFP marker in the gene drive construct as a proxy to estimate the frequency of individuals containing it . The frequency of gene drive progressively increased in both cages , peaking at around generation 6 , and thereafter we observed a gradual and continuing decrease such that by G25 the frequency of individuals with the gene drive was less than 20% . To investigate whether the gradual decline in the gene drive frequency observed in the cage experiment was due to the selection of pre-existing variant target sites in the population or the generation and selection of nuclease-resistant indels , we used deep sequencing of a PCR amplicon comprising sequences flanking the target site on pooled samples of mosquitoes from early ( G2 ) and late ( G12 ) generational time points ( Fig 1B ) . The expected amplified region from the original wild type sequence was 320bp long , with the putative cleavage point within the target site residing after nucleotide 208 ( Fig 2A ) . Ultra-deep sequencing of PCR reactions were performed on pooled DNA under non-saturating conditions so that the number of reads corresponding to a particular allele at the target site is proportional to its representation in the pool . We developed a computational method to analyse the sequences edited by the CRISPR-based gene drive close to the nuclease target site . In the colony of mosquitoes that we used there are a number of pre-existing single nucleotide polymorphisms ( SNPs ) within the amplicon that do not overlap the nuclease target site and are present at varying frequencies ( S1 Fig ) . Our method identified small insertions and deletions introduced by the repair system and used the presence of surrounding SNPs to characterize the haplotypes on which they arose . Because the PCR only amplifies the non-drive allele , the frequencies reported below refer to their frequency within this class , rather than within the population as a whole . Mapping amplicon sequences reconstructed from the sequenced against the Anopheles gambiae reference genome ( PEST strain , AgamP4 , Vectorbase ) we observed a large repertoire of deletions already in the G2 generation , with a wide range of sizes and centred around the predicted nuclease cleavage site after nucleotide 208 ( Fig 2A—one cage trial shown as a representative example ) , and a lower proportion of small insertions , consistent with the known mutational activity of the nuclease . By contrast , ten generations later we observed a much reduced diversity of indels . We then considered all alleles that reached a frequency of at least 1% in any sample , classified these as to whether the indel caused a frameshift in the coding sequence of the target gene or was in-frame , and analysed their frequency over time ( Fig 2B ) . The predominant target allele in the G2 was still the reference ( non-mutated ) allele at 63% and 48% in cages 1 and 2 , respectively ( Fig 2B and S1 Table ) , while the second major class ( at least 15% in each replicate ) was represented by a wide range of non-reference alleles , each present at low frequency ( <1% ) , consistent with the stochastic generation of a broad range of indels . Thus at a time when the gene drive was still increasing in frequency there was a significant accumulation of mutations at the target site that would likely render it refractory to the homing mechanism of copying . Of note , three separate indels causing in-frame deletions of 3- or 6bp ( 202-TGAGGA , 203-GAGGAG , 203-GAG; where 203 refers to the starting site of the indel in the reference amplicon and “-”means deletion ) were present among a large number of indels at low but appreciable frequencies in the G2 . Such short in-frame deletions may result in only minimal disruption to the final encoded protein while at the same time proving resistant to the gene drive . Indeed these three deletions , plus a 6bp in frame insertion ( 207+AAAGTC ) , had increased significantly in frequency to make up the 4 most abundant non-drive alleles in the G12 , almost to the exclusion of the reference allele ( present at 6% and 0 . 4%; S1 Table ) . At the same time , a wide range of frameshift indels that were present in G2 had fallen in frequency in G12 to either below the 1% threshold or were not detected at all ( Fig 2B and S1 Table ) . The most parsimonious explanation for these results is that a wide range of frameshift and in-frame indels was created by the gene drive , yet only short in-frame indels were selected for because they restore functionality to the target gene while protecting the sequence from gene drive activity . These ‘restorative’ mutations are likely to be most strongly selected when the frequency of the gene drive is high in the population—when the majority of individuals are homozygous for the driver , the relative gain in viable offspring from an individual with a gene drive balanced by a resistant restorative mutation is that much higher . Small in-frame deletions can arise by either classical non-homologous end-joining ( NHEJ ) , or an alternative form of end-joining ( microhomology-mediated end-joining , MMEJ ) that relies on alignment of small regions of microhomology , as little as 2 base pairs , on either side of the DSB , resulting in loss of the intervening sequence [16] . Consistent with this latter possibility , at least three of the most frequent alleles in the G12 generation can be explained by MMEJ via 3bp repeats ( Fig 2C ) . To investigate whether the most common indels at G12 had single or multiple origins , we used the naturally occurring SNPs in the sequences flanking the recognition sequence . In cage 1 the deletion 203-GAGGAG was present on 10 separate haplotypes in G12 , with each haplotype being present at ratios broadly similar to their ratios in the starting population ( S1 Fig ) , suggesting that the same deletion was generated at least 10 times independently and that there was no detectable selective advantage to any particular haplotype surrounding the deletion . In cage 2 the predominant allele was 202-TGAGGA ( 68% of all non-reference alleles ) , an end-joining deletion that shows no apparent features of a MMEJ event , and was found on 5 separate haplotypes . The progressive increase in frequency of specific mutations at the target site , concomitant with a decrease of gene drive activity , strongly suggested that they conferred resistance to cleavage while still ensuring a normal functional activity of nudel . To confirm this hypothesis we crossed individual RFP+ females from G20 with wild type males and assessed both their fertility and the transmission rate of the drive . We also sequenced the target site of each parental female to characterize allelic variants at the target locus . This analysis failed to detect wild type sequence at the target site ( Fig 3A ) among 70 individuals tested; instead every individual showed an indel , indicating that each female tested was heterozygous for the gene drive and balanced by a mutated target site . In cage 1 the 203-GAGGAG 6bp deletion was the predominant allele ( 23/31 individuals ) while in cage 2 another 6bp deletion ( 202-TGAGGA ) was predominant ( 37/39 individuals ) . The relative frequency of each allele was consistent with the results obtained using pooled amplicon sequencing performed on the G12 individuals . Of those heterozygous females that could be confirmed as having mated , the vast majority ( 56/58 ) generated viable progeny ( average clutch size 119 +/- 35 . 9 eggs , average hatching rate 78 . 5% +/- 19 . 9%; Fig 3B and S2 Table ) at rates significantly higher than those previously observed in females heterozygous for the gene drive and a wild type allele ( 90 . 7% overall reduction in fecundity ) [11] , suggesting that the mutations detected at the target site substantially restored the functionality of nudel . There was no obvious difference in fertility associated with the 5 indels sampled in this assay ( Fig 3B ) . The two major indels across the two cages result in relatively conservative changes to the overall amino acid sequence of the final gene product—two glutamate residues missing ( 203-GAGGAG ) or two glutamates missing and a conversion of a serine to arginine ( 202-TGAGGA ) . Finally , the offspring of these individuals showed a non-biased inheritance of the gene drive ( 50 . 82% mean +/-5 . 96% standard error; total RFP+ offspring 50 . 85% ) , consistent with normal Mendelian segregation ( Fig 3B ) . Thus , as well as restoring nudel function , the mutated sequences were also resistant to the gene drive . Conceivably a breakdown in the nuclease component ( e . g . mutation in the Cas9 coding sequence , or the gRNA sequence ) could be an additional explanation for the Mendelian transmission of the gene drive element and restored fertility in heterozygous females that we observed . To assess this possibility we took the male offspring ( ‘sons’ ) of the above crosses that inherited the construct and crossed them in turn to wild type females . We assumed that if the gene drive construct was still functional it should show a biased inheritance when the resistant target site allele had been replaced with a wild type one . Indeed , in these sons we saw a significant increase in the transmission of the gene drive to their progeny , but the observed rate ( mean 60 . 13% +/- 13 . 9% S . E . ; total RFP+ progeny 59 . 6% ) was much lower than that previously observed ( ~99% inheritance ) [11] . A similar phenomenon of reduced homing has been observed in the offspring of another mosquito species [8] , and more recently in Drosophila [10] , when the drive construct was inherited from the mother and when the same vasa germline promoter was used to transcribe the Cas9 nuclease . The reduced gene drive activity in the immediate offspring of heterozygous mothers was attributed to the persistence of maternally-deposited Cas9 in fertilized embryos , leading to double stranded DNA breaks being repaired preferentially by end-joining mechanisms in the early zygote possibly before paternal and maternal homologous chromosomes are aligned . Consistent with this explanation , males in the subsequent generation ( ‘grandsons’ ) that had received only a paternal copy of the gene drive had exceptionally high homing rates , with 97 . 5% of progeny inheriting the gene drive ( Fig 3C ) . The drop in homing seen in sons receiving a maternal dose of Cas9 ( 59 . 6% transmission vs . 97 . 5% in grandsons ) allows us to estimate an ‘embryonic end-joining’ rate of 79 . 6% of wild type alleles being converted to cleavage-resistant alleles . This rate of embryonic end-joining is much higher than that observed in the germline at or just prior to meiosis ( ~1% [11] ) and is predicted to reduce the rate of spread of the gene drive , due to a reduced frequency of cleavable alleles [17] , and increase the rate at which restorative resistant alleles can arise and be selected . Cleavage due to maternally deposited Cas9 could potentially be followed by HDR instead of end-joining , effectively leading to ‘embryonic homing’ , where the cleaved allele is converted to the non-cleaved allele . In the case of a resistant allele this could lead to an individual heterozygous for the allele being converted into a homozygote in the early zygote , thereby further accelerating the spread of the resistant allele in the population . One signature of embryonic homing of a resistant allele would be novel hybrid haplotypes due to partial conversion of the haplotype surrounding the wild type allele where the DSB was generated to the haplotype surrounding the resistant allele . Looking in detail at the most abundant resistant allele in each cage , we failed to observe such a signature , and all resistant haplotypes were already pre-existing in the population ( S1 Fig ) , suggesting that if this phenomenon is occurring then the resection following cleavage and resultant conversion encompasses a section longer than the ~300bp covered in our sequenced amplicon . The key qualitative results from the cage experiments—that a gene drive can increase in frequency in a susceptible population even if it reduces individual fitness , and that the spread of a gene drive can in turn lead to the spread of mutants that are resistant to cleavage and restore individual fitness—are fully consistent with expectations from population genetic models [13 , 18 , 19] . To investigate how well such models can account for the quantitative details of the cage experiments , we extended the model of Deredec et al . [18] to incorporate our observations of embryonic cleavage by maternally derived nuclease ( which is independent of inheritance of the gene drive ) , and have two classes of resistant allele ( in-frame functional and frame-shift non-functional; see S1 Text and S1 File ) . Due to the sex-specific fitness effects of our construct , the model also has a separate treatment of females and males . Using this model and the baseline parameter values from the single-generation crosses , we generated the expected allele frequency dynamics over the 25 generations of the experiment ( Fig 4 ) . Again , the qualitative fit to the observed dynamics is good , but there are quantitative differences . For example , the model predicts that at G12 the original wild type allele will be 9 . 3% of all non-drive alleles , while our observed rates were 6% and 0 . 4% in cages 1 and 2 , respectively ( Fig 2B , S1 Table ) . The model also recapitulates the observation that while non-functional resistant alleles initially outnumber the functional ones , because they are produced more frequently , by the end of the experiment it is the functional ones that predominate . Importantly , for the gene drive itself , the model captures the essential aspects of the observed dynamics , showing an initial increase in frequency followed by an eventual loss , though in earlier generations our observed frequencies exceeded the predicted frequencies ( Fig 4B ) . To investigate what might explain this discrepancy , we examined the effect of varying each of the different parameter values individually in the model , and found that small variations in the fertility of females heterozygous for the gene drive had the largest effect in increasing the match between observations and expectations . Keeping the experimental estimates of all other parameters unchanged , the least squares best fit occurred at a dominance coefficient of 0 . 70 ( Fig 4B and S1 File ) , compared to our previous direct estimate of 0 . 9 , with lower confidence limit of 0 . 86 [11] . We also used our model to investigate the potential impact of HDR after embryonic cleavage caused by maternal deposition of Cas9 , and found this parameter has little effect on the expected rate of resistance emergence when the rates of meiotic homing are as high as we observe .
We have analyzed the dynamics of a gene drive deliberately designed to impose a fitness load on a population , and characterized the resistant or compensatory mutations which it generated and selected for . As with any control approach aimed at suppressing an organism , ‘push back’ from the target organism is to be expected . One of the advantages of the modular gene drives investigated here is that contingency in planning for and overcoming resistance can be foreseen and built into the system in a number of ways . First , the use of multiple gene drives targeting separate sequences has long been considered an essential pre-requisite for any gene drive intended as a functional vector control tool [19] and the ease with which the guide RNA expression constructs can be multiplexed lends CRISPR-based gene drives this flexibility [12 , 20] . Second , it will be useful to target sites at which sequence changes are likely to destroy function . The nuclease target site in the gene AGAP007280 described in this report was not chosen according to any prioritisation based on high levels of sequence conservation that would imply functional constraint—a feature expected to mean that resistant mutations are less likely to restore function of the gene . Clearly the choice of the target should be guided by the extensive genomic data that is now available on sequence conservation both across different Anopheles species [21] and within An . gambiae [22] . This data revealed a posteriori that for the target site in AGAP007280 used here there is pre-existing variation in this species at at least 8 of the 20 nucleotides covered by the gRNA . Going forward low tolerance of sequence variation at the target site should be a key criterion for designing a gene drive . Third , our results show that one of the key drivers in the generation of resistant alleles is the nuclease activity itself , followed by end-joining , and a significant proportion of these alleles are created as a result of maternally deposited nuclease in the early zygote where end-joining repair predominates over homology-directed repair . We suggest this maternal effect may be suppressed either through the use of more tightly regulated promoters to restrict nuclease expression to the early germline or through the addition of destabilising modifications to the nuclease , either of which are expected to reduce perdurance in the embryo . Fourth , an additional consideration in the choice of target site may take into account the propensity for a particular double strand break to be repaired more readily into a resistant , restorative ( R2 ) allele , for example due to microhomology either side of the cleavage site that more readily re-creates an in-frame allele than a frameshift allele . Where MMEJ is the predominant end-joining repair pathway , this feature could be incorporated into target site choice to ensure the most likely end-joining repair event is an out of frame allele and therefore not likely to be selected . Our approach of pooled sequencing of a targeted region allowed us to reliably detect even low frequency signatures of gene drive activity and reveal the complex dynamics of different genotypes emerging over time . Certainly for the future improvement of gene drives it will be important to have a faster method to triage for the most robust gene drives least prone to resistance without a multi-generational cage experiment , a laborious and time consuming process that should be reserved for more extensive evaluation of the best candidates . A simple way to do this would be to apply the method of amplicon sequencing described here in a screen where all generated mutant alleles are balanced against a known null allele to see if they restore function to the target gene . The potential for rapid emergence and spread of resistance highlights not only one of the technical challenges associated with developing a gene drive , but also how intentionally releasing a resistant allele in to a population could be a simple and effective means of reversing the effects of a gene drive , if it fully restores function [23] .
These experiments were essentially as described before in Hammond et al . [11] Briefly , in the starting generation ( G0 ) L1 mosquito larvae heterozygous for the CRISPRh allele at AGAP007280 were mixed within 12 hours of eclosion with an equal number of age-matched wild-type larvae in rearing trays at a density of 200 per tray ( in approx . 1L rearing water ) . The mixed population was used to seed two starting cages with 600 adult mosquitoes each . For subsequent generations , each cage was fed after 5 days of mating , and an egg bowl placed in the cage 48h post bloodmeal to allow overnight oviposition . After allowing full eclosion a random sample of offspring were scored under fluorescence microscopy for the presence or absence of the RFP-linked CRISPRh allele , then reared together in the same trays and 600 were used to populate the next generation . After a generation had been allowed the opportunity to oviposit , a minimum of 240 adults were removed and stored frozen for subsequent DNA analysis . For the sequence analysis , a minimum of 240 adult mosquitoes taken at generations G2 and G12 of the cage trial experiments were pooled and extracted en masse using the Wizard Genomic DNA purification kit ( Promega ) . A 332 bp locus containing the target site was amplified from 40 ng of genomic material from each pooled sample using the KAPA HiFi HotStart Ready Mix PCR kit ( Kapa Biosystems ) , in 50 μl reactions . Specially designed primers that carried the Illumina Nextera Transposase Adapters ( underlined ) , 7280-Illumina-F ( TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGGAGAAGGTAAATGCGCCAC ) and 7280-Illumina-R ( GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGCGCTTCTACACTCGCTTCT ) were used to tag the amplicon for subsequent library preparation and sequencing . The annealing temperature and time were adjusted to 68°C for 20 seconds to minimize off-target amplification . In order to maintain the proportion of the reads corresponding to particular alleles at the target site , the PCR reactions were performed under non-saturating conditions and thus they were allowed to run for 20 cycles before 25 μl were removed and stored at -20°C . The remnant 25 μl were run for an additional 20 cycles and used to verify the amplification on an agarose gel . The non-saturated samples were used to prepare libraries according to the Illumina 16S Metagenomic Sequencing Library Preparation protocol ( Part # 15044223 Rev . A ) . Amplicons were then purified with AMPure XP beads ( Beckman Coulter ) followed by a second PCR amplification step with dual indices and Illumina sequencing adapters using the Nextera XT Index Kit . After PCR clean-up via AMPure XP beads and validation performed with Agilent Bioanalyzer 2100 , the normalized libraries were pooled and loaded at a concentration of 10 pM on Illumina Nano flowcell v2 and sequenced using the Illumina MiSeq instrument with a 2x250bp paired-end run . Sequencing data of the amplified genomic region were analysed using available tools and developed scripts in R v3 . 3 . 1 . Raw reads were cleaned up for low quality and trimmed for the presence of adapters using Trimmomatic v . 0 . 36 [24] . Paired-end reads were merged together in order to reconstruct the whole amplicon sequence using PEAR v0 . 9 . 10 [25] . Resulting assembled identical fragments were then clustered using fastx_collapser module from the FASTX v0 . 0 . 13 suite ( http://hannonlab . cshl . edu/fastx_toolkit/ ) and aligned to the reference amplicon with vsearch tool v2 . 0 . 3 [26]which implements a global alignment based on the full dynamic programming Needleman-Wunsch algorithm . We considered for downstream analysis only sequences represented by at least 100 reads in each dataset . The blast6 output files from the alignment phase were parsed by ad hoc written R scripts to identify sequence variants containing insertions and/or deletions in the target site . The quantification of each allelic variant was measured as relative alternative allele frequency by summing up the reads representing that particular variant in the dataset . Finally , for each identified variant , we examined the single nucleotide variants ( SNVs ) along the full amplicon and selected the ones with a minimum alternative allele frequency of 2 . 5% for the purposes of haplotype calling . As part of a sequencing effort one year prior to the start of this experiment 12 males and 12 females from our A . gambiae G3 laboratory colony were subjected to individual genome resequencing . Mosquitoes were chosen randomly as pupae of differing ages from separate trays of a large cohort of the colony population ( census population size >2000 ) in order to minimise biased sampling from a reduced number of founders . After emerging as adults whole mosquitoes were individually homogenised and genomic DNA was extracted with Promega Wizard Genomic DNA extraction kit . Paired-end reads ( 2 x 100bp ) obtained from the Illumina HiSeq 2000 sequencing were aligned to A . gambiae PEST reference genome assembly ( AgamP4 , VectorBase ) using BWA-MEM ( Li and Durbin 2009 , v0 . 7 . 15 ) . Alignments were sorted using Samtools ( v1 . 5 ) and raw SNPs and indels were called using HaplotypeCaller tool from Genome Analysis Toolkit ( GATK , v3 . 7 ) for each of the 24 samples in the same 320bp region around the nuclease target site in AGAP007280 gene that was used for the pooled amplicon sequencing . Raw SNPs were then merged using GATK GenotypeGVCFs tool . No indels were observed in this step for the selected region . We used SHAPEIT2 ( Delaneau et al . 2013 , v2 ) for the final haplotype estimation from previously obtained unphased genotypes of 24 individuals . Raw sequencing reads from the 24 individuals have been submited to NCBI Sequence Read Archive ( SRA ) under project accession number PRJNA397539 . We use a discrete generation deterministic model to explore how the dynamics of gene-frequencies depend on underlying parameters . We suppose there are four possible alleles: Wildtype ( W ) , driver allele ( H ) , and two mutant alleles that are resistant to homing , R1 which is fully functional and R2 which is recessive but non-functional ( i . e . , H/R2 and R2/R2 type females are sterile ) . We assume alleles segregate at meiosis according to Mendelian inheritance except in W/H males and females , where segregation may be distorted by cleavage followed by either homing or non-homologous repair . Our model also allows for the possibility that eggs from females with at least one H allele will contain the driver nuclease ( regardless of the egg’s own genotype ) , in which case cleavage and repair may occur in the embryo . The mathematical details of the model are given in the S1 Text , and model outputs from user-defined parameters values can be seen using a computable document format ( Wolfram CDF Player ) available as a file ( S1 File ) . Baseline parameter values for the model are provided in the legend accompanying Fig 4 . Individual females containing at least one copy of the RFP-linked CRISPRh gene drive were selected as virgins from the G20 generation and allowed to mate with 5 wild type male mosquitoes , essentially as in Hammond et al . [11] . The fecundity of females and transmission of the gene drive was measured by counting larval offspring positive for the RFP marker . To check mating status of females , spermathecae were dissected and examined for the presence of sperm . Unmated females were censored from the fertility assay . Sons of each gene drive mother from the G20 generation were kept together and allowed to mate in groups of approximately 5 males with an equal number of wild type females and assessed for rates of transmission of the gene drive . The male offspring of these sons ( grandsons ) inheriting the drive from their fathers were in turn assessed in the same way , keeping lineages separate . | Gene drives are selfish genetic elements that are able to bias their own inheritance among offspring . Starting from very low frequencies they can rapidly invade a population in just a few generations , even when imposing a fitness cost . Gene drives based on the precise DNA cutting enzyme CRISPR have been shown recently to be highly efficient at copying themselves from one chromosome to the other during the process of gamete formation in mosquitoes , resulting in transmission to 99% of offspring instead of the 50% expected for a single gene copy . One proposed use for CRISPR-based gene drives is in the control of mosquitoes by designing the gene drive to target mosquito genes involved in fertility , thereby reducing their overall reproductive output and leading to population suppression . Like any intervention designed to suppress a population these gene drives are expected to select for mutations in the mosquito that are resistant to the drive and restore fertility to mosquitoes . We have analyzed the origin and selection of resistant alleles in caged populations of mosquitoes initiated with a gene drive construct targeting a female fertility gene . We find the selected alleles are in-frame insertions and deletions that are resistant to cleavage and restore female fertility . Our findings allow us to improve predictions on gene drive behaviour and to make concrete recommendations on how to improve future gene drive designs by decreasing the likelihood that they generate resistance . | [
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"organisms"
] | 2017 | The creation and selection of mutations resistant to a gene drive over multiple generations in the malaria mosquito |
The fungal pathogen Candida albicans causes lethal systemic infections in humans . To better define how pathogens resist oxidative attack by the immune system , we examined a family of four Flavodoxin-Like Proteins ( FLPs ) in C . albicans . In agreement with previous studies showing that FLPs in bacteria and plants act as NAD ( P ) H quinone oxidoreductases , a C . albicans quadruple mutant lacking all four FLPs ( pst1Δ , pst2Δ , pst3Δ , ycp4Δ ) was more sensitive to benzoquinone . Interestingly , the quadruple mutant was also more sensitive to a variety of oxidants . Quinone reductase activity confers important antioxidant effects because resistance to oxidation was restored in the quadruple mutant by expressing either Escherichia coli wrbA or mammalian NQO1 , two distinct types of quinone reductases . FLPs were detected at the plasma membrane in C . albicans , and the quadruple mutant was more sensitive to linolenic acid , a polyunsaturated fatty acid that can auto-oxidize and promote lipid peroxidation . These observations suggested that FLPs reduce ubiquinone ( coenzyme Q ) , enabling it to serve as an antioxidant in the membrane . In support of this , a C . albicans coq3Δ mutant that fails to synthesize ubiquinone was also highly sensitive to oxidative stress . FLPs are critical for survival in the host , as the quadruple mutant was avirulent in a mouse model of systemic candidiasis under conditions where infection with wild type C . albicans was lethal . The quadruple mutant cells initially grew well in kidneys , the major site of C . albicans growth in mice , but then declined after the influx of neutrophils and by day 4 post-infection 33% of the mice cleared the infection . Thus , FLPs and ubiquinone are important new antioxidant mechanisms that are critical for fungal virulence . The potential of FLPs as novel targets for antifungal therapy is further underscored by their absence in mammalian cells .
Oxidative stress poses a great threat to cells . Unchecked oxidative damage to DNA , proteins , and lipids causes disruption of physiological processes , harmful mutations , and cell death [1] . To prevent these destructive effects , cells utilize a variety of mechanisms to protect against oxidation . These antioxidant mechanisms are especially important for pathogens to resist the oxidative attack by the immune system [2] . As a result , the human fungal pathogen Candida albicans relies on several different mechanisms , such as extracellular , cytoplasmic , and mitochondrial forms of superoxide dismutases to break down superoxide radicals [3–5] . Other intracellular mechanisms include catalase to detoxify H2O2 and glutathione to promote a reducing environment [6] . Cellular membranes require special protection from oxidation . The plasma membrane is particularly vulnerable because it directly faces oxidative attack by macrophages and neutrophils . Protecting the plasma membrane is critical for survival . In addition to forming a protective barrier around the cell , it functions in a wide range of essential processes including nutrient uptake , ion homeostasis , pH regulation , cell wall synthesis , and morphogenesis . This membrane is also vulnerable because it contains polyunsaturated fatty acids ( PUFAs ) . Approximately 30% of the C . albicans fatty acids are polyunsaturated linoleic ( 18:2 ) or linolenic ( 18:3 ) acids [7 , 8] . PUFAs are very sensitive to peroxidation due to the ease with which the hydrogens can be abstracted from the methylene bridges ( -CH2- ) that lie in between the double bonds [9 , 10] . This leaves an unpaired electron on the carbon that can react with O2 to form a peroxyl radical , which can in turn abstract the hydrogen from another PUFA to continue the cycle . Thus , lipid peroxidation starts a chain reaction that propagates to other lipids . The resulting oxidative damage can also spread to other cellular constituents , including proteins and DNA . Several lines of evidence suggested that a family of four uncharacterized Flavodoxin-Like Proteins ( FLPs ) present in C . albicans could play a novel antioxidant role at the plasma membrane . The FLPs , which are encoded by PST1 , PST2 , PST3 , and YCP4 , are induced by oxidative stress [11] . The FLP genes contain consensus sites in their promoter regions for the binding of Cap1 , a transcription factor that is induced by oxidative stress , and for a subset of these genes Cap1 has been shown to bind to the promoter and regulate expression [12 , 13] . The S . cerevisiae FLPs ( Pst2 , Rfs1 , Ycp4 ) have been suggested to promote resistance to oxidative stress [14–16] , although their physiological role is not known [17] . It is also interesting that the C . albicans FLPs are likely to act at the plasma membrane , since their orthologs in S . cerevisiae are associated with the plasma membrane [18] . The FLPs are highly conserved in bacteria , plants , and fungi , but surprisingly not in mammalian cells [19] . Biochemically , the most well studied FLP is the E . coli WrbA protein . It uses flavin mononucleotide ( FMN ) as a cofactor and acts as a NAD ( P ) H quinone oxidoreductase [20–22] . FLPs from fungi , plants and other bacteria have also been shown to act as NAD ( P ) H quinone oxidoreductases , indicating that this is a conserved property of this family [15 , 23–27] . A special feature of FLPs is that they carry out a two-electron reduction of a quinone to quinol ( see structures in Fig 1A ) . This converts both carbonyl groups on the benzoquinone ring to hydroxyl groups . In contrast , other pathways that promote a one-electron reduction of quinone form a semiquinone intermediate that is a hazardous reactive oxygen species [9 , 10] . Although the physiological role of WrbA is not known , there is suggestive evidence that it promotes resistance to oxidative stress [19 , 21 , 27] . Quinone reductases could promote resistance to oxidative stress in several ways . One is that they can reduce and detoxify small molecule quinones that are produced by some organisms for defense or created as benzene metabolites [28 , 29] . In addition , they could act on endogenously produced quinones , such as ubiquinone ( coenzyme Q ) , an isoprenylated benzoquinone . Ubiquinone is well known for its role in the mitochondrial electron transport chain , but it is also present in other cellular membranes , where it can undergo redox cycling to act as an antioxidant [30–34] . Mammalian cells use the enzyme Nqo1 ( NAD ( P ) H quinone oxidoreductase ) , formerly known as DT-diaphorase , to safely carry out a two-electron reduction of ubiquinone and avoid semiquinone formation [35 , 36] . Nqo1 is analogous to FLPs in that it uses NAD ( P ) H for reducing potential , but it differs in overall amino acid sequence and the active site is distinct from the FLPs , in part due to the fact that the active site of Nqo1 binds FAD as a cofactor rather than FMN [19] . However , it is not known how fungal cells , including C . albicans , carry out this function since they lack an obvious ortholog of NQO1 . Therefore , in this study we examined a quadruple mutant lacking all four FLP genes ( PST1 , PST2 , PST3 and YCP4 ) . The results demonstrate that these proteins represent a new mechanism for protecting C . albicans against oxidative stress that is required for virulence in a mouse model of systemic candidiasis .
Four FLPs were identified in C . albicans based on their high sequence identity ( 45–50% ) and similarity ( ~65% ) to the well-studied E . coli WrbA ( S1 Fig ) . This type of enzyme is advantageous because it uses NAD ( P ) H to carry out a two-electron reduction of toxic quinones that avoids creation of the semiquinone radical ( Fig 1A ) [21] . The conserved residues are concentrated in the active site near the location of the FMN co-factor . The four C . albicans FLPs share a similar structure , although Ycp4 contains C-terminal extension of about 90 amino acids that ends in a CAAX box , indicating it is likely to be lipid modified ( S1 Fig ) . To examine their role in the diploid C . albicans , a quadruple mutant strain was constructed that lacks both copies of all four FLP genes . Fortuitously , PST3 and YCP4 are adjacent in the genome and were deleted simultaneously using the HIS1 and LEU2 selectable markers . Subsequent deletion of the PST1 and PST2 genes was carried out by successive use of the SAT Flipper that employs a recyclable SAT1 selectable marker [37] . For brevity , this pst1Δ pst2Δ pst3Δ ycp4Δ strain will be referred to as the Δ/Δ/Δ/Δ mutant . The sensitivity of this strain to quinones was tested by spotting dilutions of cells onto agar medium containing p-benzoquinone ( BZQ ) or menadione ( MND ) , a heterocyclic napthoquinone ( Fig 1B ) . The growth of the Δ/Δ/Δ/Δ strain was clearly inhibited by these small molecule quinones , indicating it is more sensitive to quinones than either the wild type control or a complemented strain in which one copy of each of the FLP genes was reintroduced . FLPs in bacteria and plants have also been suggested to have a role in fighting oxidative stress , but their physiological role is not known [19 , 21 , 25 , 27] . Therefore , given the importance of antioxidant enzymes for microbial pathogens , we spotted the cells on medium containing H2O2 and found that the Δ/Δ/Δ/Δ mutant was more sensitive to this oxidant ( Fig 1B ) . Since the FLPs are associated with the plasma membrane in S . cerevisiae [18] , we further tested two other peroxides that are more hydrophobic . Interestingly , the Δ/Δ/Δ/Δ mutant was also very sensitive to tert-butyl hydroperoxide ( TBHP ) and cumene hydroperoxide ( CHP ) , which are more hydrophobic than H2O2 and more likely to preferentially oxidize membranes . The Δ/Δ/Δ/Δ mutant was next assayed for sensitivity to polyunsaturated fatty acids ( PUFAs ) , which can auto-oxidize and initiate a chain reaction of lipid peroxidation [10 , 33] . PUFAs are more readily oxidized because the presence of double bonds flanking a methylene group ( -CH2- ) weakens the methylene C-H bond , making it much easier to abstract a hydrogen [9] . This leaves a carbon with an unpaired electron that readily reacts with oxygen to form a peroxyl radical ( LOO• ) . For example , linolenic acid , which has three unsaturated double bonds , is much more likely to auto-oxidize to form a peroxyl radical than is monounsaturated oleic acid . The peroxyl radical can then abstract a hydrogen from another PUFA to form a lipid peroxide ( LOOH ) and a new lipid radical that can further extend a chain reaction of lipid peroxidation [9 , 10] . Linolenic acid was also used for this analysis because previous studies showed that it efficiently induced lipid peroxidation and cell death in S . cerevisiae [33] . Interestingly , growth of the Δ/Δ/Δ/Δ mutant was strongly inhibited by the polyunsaturated linolenic acid ( LNA; Fig 1B ) . In contrast , the Δ/Δ/Δ/Δ mutant grew as well as the control cells in the presence of the monounsaturated oleic acid ( OA ) . Taken together , these results indicate that the FLPs are needed for C . albicans to combat a variety of oxidative stresses . The effects of linolenic acid on C . albicans were analyzed further in quantitative assays . A time course of cell death was assayed by incubating cells for different times in the presence of 0 . 5 mM linolenic acid followed by plating dilutions on agar medium to determine the viable colony forming units ( CFUs ) . The results confirmed the spotting assays . The Δ/Δ/Δ/Δ mutant showed a significant trend toward decreased viability by 6–8 h that was not observed for the wild-type control or complemented strains ( Fig 2A ) . Analysis of the dose-response to incubation with linolenic acid for 6 h revealed a loss of viability starting at 0 . 25 mM that became more significant at 0 . 5 and 1 . 0 mM ( Fig 2C ) . In contrast , the cells remained viable after incubation in the monounsaturated oleic acid ( S2 Fig ) . To determine whether linolenic acid caused an increase in lipid peroxidation , cells were assayed for thiobarbituric acid reactive substances ( TBARS ) [33 , 38] . This assay detects malondialdehyde ( MDA ) , a common byproduct of lipid peroxidation . As expected , both the Δ/Δ/Δ/Δ mutant and the control cells showed elevated TBARS after incubation for different times with linolenic acid ( Fig 2B ) . However , the Δ/Δ/Δ/Δ mutant showed a significantly higher level of TBARS than the control cells at 4 and 6 h . By 8 h , the results of the TBARS assays were quite variable . This may have been due to difficulties in dealing with the high fraction of dead cells during the analysis . Dose-response assays showed that the TBARS in the Δ/Δ/Δ/Δ mutant started trending upward at 0 . 25 mM and was significantly higher than control cells at 0 . 5 mM and 1 . 0 mM linolenic acid ( Fig 2D ) . These results demonstrate that linolenic acid stimulated higher levels of lipid peroxidation in the Δ/Δ/Δ/Δ mutant . For comparison , mutants lacking a single FLP gene ( pst1Δ , pst2Δ , pst3Δ or ycp4Δ ) , two genes ( pst3Δ ycp4Δ ) , or three genes ( pst2Δ , pst3Δ ycp4Δ ) were also tested for their sensitivity to 0 . 5 mM linolenic acid ( Fig 2E and 2F ) . However , no significant changes in either CFU or lipid peroxidation level were detected compared to the wild type control . As will be described further below , this is consistent with redundancy of the different FLP genes in C . albicans . To gain additional evidence that the effects of linolenic acid were due to oxidation , cells were incubated with α-tocopherol ( vitamin E ) , a hydrophobic reducing agent that localizes to membranes and has been shown to prevent lipid peroxidation in other organisms [9 , 33] . Treatment of cells with α-tocopherol alone had no detectable effects on CFUs or lipid peroxidation . In contrast , the addition of α-tocopherol significantly decreased the killing activity of linolenic acid in both WT and the Δ/Δ/Δ/Δ mutant ( Fig 3A ) . Similarly , α-tocopherol reduced the levels of lipid peroxidation to below the limit of detection , as determined by the TBARS assay ( Fig 3B ) . To confirm whether quinone reductase activity is important to promote resistance to oxidative stress in C . albicans , the Δ/Δ/Δ/Δ mutant was engineered to express two distinct types of NAD ( P ) H quinone oxidoreductases: rat NQO1 and E . coli wrbA . NQO1 and wrbA were selected because their proteins have been well-studied biochemically [21 , 39 , 40] . These genes were expressed under the control of the strong ADH1 promoter . As a control , cells were also engineered to express GFP in a similar manner . Incubation of the cells in the presence of 0 . 5 mM linolenic acid for 6 h showed that expression of either wrbA or NQO1 rescued the viability of the Δ/Δ/Δ/Δ mutant ( Fig 4A ) . In contrast , the Δ/Δ/Δ/Δ mutant or the Δ/Δ/Δ/Δ mutant that expressed only GFP showed a significant drop in CFUs . Similarly , expression of wrbA or NQO1 , but not GFP , diminished lipid peroxidation in cells that were exposed to linolenic acid ( Fig 4B ) . Growth assays on agar plates also showed that wrbA and NQO1 could complement the increased sensitivity of the Δ/Δ/Δ/Δ mutant to H2O2 , tert-butyl hydroperoxide , cumene hydroperoxide and menadione ( Fig 4C ) . The ability of two distinct quinone reductases to complement the Δ/Δ/Δ/Δ mutant phenotype demonstrates that this activity plays a key antioxidant role in C . albicans . The properties of the different quinone reductase homologues were examined by expressing individual genes in the Δ/Δ/Δ/Δ mutant . The C . albicans genes were reintroduced under control of their native promoters , whereas wrbA and NQO1 were controlled by the ADH1 promoter . Growth assays were performed to test the ability of cells carrying only one quinone reductase gene to resist different quinones and oxidants . All of the different quinone reductases were able to promote resistance to H2O2 , tert-butyl hydroperoxide , and linolenic acid ( Fig 5A ) . However , some of the strains had differential ability to resist cumene hydroperoxide and the small molecule quinones: p-benzoquinone and menadione ( Fig 5A and summarized in Fig 5B ) . The strain expressing only PST3 was very interesting in that it showed the strongest resistance to p-benzoquinone and menadione ( Fig 5A ) . Although several strains displayed obvious resistance to 75 μM p-benzoquinone , only the PST3-expressing strain was resistant to 100 μM p-benzoquinone . It grew remarkably better than the other strains , and nearly as well as the complemented strain that carries one copy of each FLP gene . Similarly , it also grew better than the other strains on medium containing menadione . In contrast , the PST3-expressing strain did not show significant resistance to cumene hydroperoxide and was more weakly resistant to linolenic acid , which are considered to be good inducers of lipid peroxidation . This strain was , however , more resistant than the Δ/Δ/Δ/Δ mutant to H2O2 and tert-butyl hydroperoxide , indicating that it can provide protection against some oxidants . Thus , it appears that Pst3 can preferentially act on small molecule quinones . In agreement with this , a pst3Δ strain was sensitive to the inhibitory effects of p-benzoquinone and menadione , whereas the pst1Δ , pst2Δ and ycp4Δ mutants were not ( Fig 5C ) . The increased sensitivity of the pst3Δ mutant to p-benzoquinone and menadione were the only phenotypes we detected for the single mutants as we did not detect increased sensitivity to oxidizing conditions ( Fig 2 ) . Some of the other strains expressing a single quinone reductase showed the opposite phenotype of being more resistant to oxidants than to the small molecule quinones . For example , the strains expressing PST2 , YCP4 , wrbA , or NQO1 all showed improved resistance to cumene hydroperoxide and linolenic acid compared to the Δ/Δ/Δ/Δ mutant , but were not significantly more resistant or were more weakly resistant to the small molecule quinones under the conditions tested ( Fig 5A ) . The different phenotypes indicate that there are functional differences between the various quinone reductases . The major quinone found in cells , ubiquinone ( coenzyme Q ) , is known to have two key functions . It plays a central role in the mitochondrial electron transport chain , and it is also present in other cellular membranes where it can function as an antioxidant [30–34] . To investigate the relationship between ubiquinone and oxidative stress , both copies of COQ3 were deleted from C . albicans to prevent ubiquinone synthesis . As expected , a C . albicans coq3Δ mutant was not able to grow on glycerol , a carbon source that requires respiration to be utilized ( Fig 6A ) . In contrast , the Δ/Δ/Δ/Δ mutant readily grew on glycerol ( Fig 6A ) . Interestingly , the coq3Δ mutant was very sensitive to H2O2 , even more so than the Δ/Δ/Δ/Δ mutant ( Fig 6A ) . Spot assays also showed that the coq3Δ mutant was more sensitive to linolenic acid than the Δ/Δ/Δ/Δ mutant . For comparison , two previously constructed mitochondrial mutants were examined that lack components of Complex I of the electron transport chain [41] . Both orf19 . 2570Δ and orf19 . 6607Δ failed to grow on glycerol medium , as expected ( Fig 6A ) . However , they were not more sensitive to linolenic acid and showed perhaps only a minor increase in sensitivity to H2O2 . This indicates that a mitochondrial defect does not account for the increased sensitivity to oxidation of the coq3Δ mutant , consistent with ubiquinone also playing a major role as an antioxidant . Analysis of cell viability after incubation with 0 . 5 mM linolenic acid for 6 h revealed a larger drop in CFUs for the coq3Δ mutant than for the Δ/Δ/Δ/Δ mutant ( Fig 6B ) . The coq3Δ mutant also displayed significantly higher levels of TBARS under these conditions ( Fig 6C ) . Similar results have been observed in S . cerevisiae , as a coq3Δ mutant in this yeast is also sensitive to oxidation and lipid peroxidation [33] . These results demonstrate that ubiquinone plays an important role as an antioxidant to prevent lipid peroxidation and oxidative stress in C . albicans . It is noteworthy that the coq3Δ mutant was not significantly more sensitive to p-benzoquinone and menadione , even though it was very sensitive to H2O2 and linolenic acid ( Fig 6A ) . This suggests that the FLPs in C . albicans can detoxify these small molecule quinones in the absence of ubiquinone , thereby prevent them from causing oxidative damage . FLPs were fused to GFP to examine their subcellular localization . Pst1-GFP and Pst3-GFP were detected at the plasma membrane by fluorescence microscopy ( Fig 7A ) . To improve detection for the other two FLPs , the strong ADH1 promoter was used to express GFP fusions to the PST2 and YCP4 genes . These GFP-Pst2 and GFP-Ycp4 fusion proteins gave a strong plasma membrane signal ( Fig 7B ) . The GFP-tagged FLPs all showed a slightly patchy distribution in the plasma membrane , suggesting that they localize in part to the eisosome subdomains , as do their S . cerevisiae orthologs [18 , 42] . Cytoplasmic GFP signal was also detected in cells . However , this could be due to proteolytic cleavage of the FLP proteins resulting in the presence of free cytoplasmic GFP , as Western blot analysis detected a strong signal at the expected size of GFP ( ~30 kD ) ( S3 Fig ) . The role of the FLPs in virulence was examined using a mouse model of hematogenously disseminated candidiasis [43] . After injection via the tail vein with 2 . 5 x 105 C . albicans cells , BALB/c mice infected with the wild type control strain succumbed to infection with a median time of 8 days ( Fig 8A ) . Similar results were observed for the complemented version of the Δ/Δ/Δ/Δ strain . In contrast , all mice infected with the Δ/Δ/Δ/Δ mutant survived to the end of the experiment ( Day 28 ) . No CFUs were detected in the kidneys from these mice , indicating that they had cleared the infection ( Fig 8B ) . To determine whether the Δ/Δ/Δ/Δ mutant failed to initiate an infection , or if it was cleared more rapidly , kidneys were examined at early times post infection . The kidney is a sensitive organ to test the ability of C . albicans to initiate an infection , since this fungus grows rapidly in the kidneys during the first two days after infection [44 , 45] . At day 2 post infection , the wild type and Δ/Δ/Δ/Δ mutant were both present at similarly high levels of CFU/g kidney , indicating they grew well initially ( Fig 8B ) . Histological analysis showed that foci of C . albicans growth in the kidney overlapped with clusters of leukocytes ( Fig 8C ) . However , by the 4th day post infection , the median CFU/g kidney was 100-fold lower for mice infected with the Δ/Δ/Δ/Δ mutant than the wild type . Furthermore , 33% of the mice ( 3/9 ) had no detectable CFU/g kidney at day 4 , indicating that they had cleared the infection . Thus , the FLPs are required for the persistence of C . albicans systemic infection . Previous studies have shown that oxidation sensitive mutants , including those with defects in catalase , thioredoxin , or superoxide dismutatase , show normal or only slightly increased sensitivity to killing by neutrophils [46] . Similar results were obtained when the Δ/Δ/Δ/Δ mutant was examined for sensitivity to killing by macrophages derived from mouse bone marrow cells . Although the Δ/Δ/Δ/Δ mutant showed a slight increase in killing by macrophages , the difference was not statistically significant ( S4 Fig ) . Analysis of the pst3Δ ycp4Δ double mutant and the pst2Δ pst3Δ ycp4Δ triple mutant showed that they did not display a significant virulence defect in mice ( S5 Fig ) . Mice infected with the triple mutant appeared to show slightly increased survival ( median 12 . 5 days ) compared to the wild type control strain ( median 8 days ) , but this difference was not statistically significant using a log rank test ( Mantel-Haenszel ) . These results are consistent with the general redundancy of the FLP genes seen in the in vitro studies . In addition , since both the double and triple mutant lack PST3 , this indicates that the special role this FLP plays in detoxifying small quinones does not appear to be important in systemic candidiasis .
Biochemical studies of FLPs from bacteria , fungi , and plants have shown that they use NAD ( P ) H to reduce quinones in a manner that avoids creation of hazardous semiquinone intermediates [23–27] . The Δ/Δ/Δ/Δ mutant was rescued by expression of E . coli wrbA ( Fig 4 ) , confirming that NAD ( P ) H quinone oxidoreductase activity plays an important antioxidant role in C . albicans . Furthermore , heterologous expression of mammalian NQO1 in the Δ/Δ/Δ/Δ mutant also rescued its sensitivity to oxidation and lipid peroxidation . Nqo1 does not share obvious sequence similarity with FLPs even though it carries out a similar enzymatic activity . Although there are some underlying structural similarities between Nqo1 and FLPs , they are quite distinct [19] . For example , Nqo1 binds FAD as a cofactor instead of FMN , and it forms dimers rather than tetramers as seen for wrbA . These observations provide strong support that the key function of the C . albicans FLPs is to act as quinone reductases . Analysis of Δ/Δ/Δ/Δ cells engineered to express a single FLP gene indicated that they have overlapping but distinct functions . Pst3 provided the best protection against the small molecule quinones p-benzoquinone and menadione ( Fig 5 ) . In agreement with this , a pst3Δ mutant was the only single FLP deletion mutant that was more sensitive to the small molecule quinones ( p-benzoquinone and menadione ) ( Fig 5 ) . In contrast , cells expressing only PST3 were less able to resist other oxidants , such as linolenic acid or cumene hydroperoxide . These phenotypes are consistent with different functional properties . However , it is also possible that some of these differences are due to differential expression of the various FLP genes under the different conditions that were tested . The most likely target for the quinone reductase activity of FLPs in C . albicans is ubiquinone ( coenzyme Q ) . Ubiquinone has a benzoquinone head group and a hydrophobic isoprenylated tail that localizes it to membranes [32 , 51] . Analogous to its well-known role as an electron carrier in the mitochondria , ubiquinone is present in other cellular membranes where its reduced form ( ubiquinol ) can act as an antioxidant [30–34] . In particular , ubiquinol is thought to be able to reduce lipid radicals that would otherwise propagate a chain reaction of lipid peroxidation to cause more extensive damage [9 , 10] . To determine if ubiquinol plays an important antioxidant role in C . albicans , COQ3 was deleted to block its synthesis . The coq3Δ mutant was found to be very sensitive to oxidative stress and also displayed increased levels of lipid peroxidation in response to linolenic acid ( Fig 6 ) . In further support of the conclusion that FLPs act on ubiquinone , rat NQO1 , which is known to reduce ubiquinone [35 , 36] , can rescue the defects of the Δ/Δ/Δ/Δ mutant ( Figs 4 and 5 ) . There were interesting differences between the Δ/Δ/Δ/Δ mutant and the coq3Δ mutant that reveal insights into their roles . Whereas the coq3Δ mutant was highly sensitive to oxidizing conditions promoted by peroxides or PUFAs , it was not significantly altered in sensitivity to p-benzoquinone and menadione ( Fig 6 ) . This indicates that the FLPs can reduce quinones in the absence of ubiquinol . The coq3Δ mutant was also much more sensitive than the Δ/Δ/Δ/Δ mutant to H2O2 and linolenic acid . One possibility is that other reductases can contribute to reduction of ubiquinone in the absence of the FLPs . However , if these enzymes use a one-electron mechanism for reduction of quinones , they will generate deleterious semiquinone radicals that would contribute to the phenotype of the Δ/Δ/Δ/Δ mutant . The FLPs were required for virulence in a mouse model of hematogenously disseminated candidiasis ( Fig 8A ) . Whereas the median survival time was 8 days for mice injected with 2 . 5 x 105 wild type C . albicans , all of the mice infected with the Δ/Δ/Δ/Δ mutant survived to the end of the experiment ( day 28 ) . Thus , the Δ/Δ/Δ/Δ mutant appears to have a stronger virulence defect than was reported for other C . albicans oxidation sensitive mutants including a cat1Δ catalase mutant [52] , a sod1Δ or sod5Δ superoxide dismutase mutant [53 , 54] , a grx2Δ glutathione reductase mutant [53] , or a tsa1Δ thioredoxin peroxidase mutant [55] . Interestingly , the Δ/Δ/Δ/Δ mutant could initially grow in the kidney essentially as well as a wild type strain ( Fig 8B ) . However , by day 4 there was about a 100-fold decrease in median CFUs and 3 out of 9 mice cleared the infection . This decline in CFUs for the Δ/Δ/Δ/Δ mutant correlates with the influx of neutrophils ( Fig 8C ) that typically peaks about day 2 of infection [44 , 45] . By day 28 , all of the mice infected with the Δ/Δ/Δ/Δ mutant lacked detectable CFU and appear to have cleared the infection . Generally similar results were reported for a C . albicans cat1Δ catalase mutant that also grew well initially , but then CFUs declined in most infected mice [52] . In this regard it is also significant that a wrbAΔ mutant of the bacterial pathogen Yersinia tuberculosis can initiate an infection but is defective in establishing a persistent infection [56] . This key role in virulence for the FLPs indicates they have strong potential to serve as novel targets for antifungal therapy . New therapeutic approaches are needed; ~40% of patients with systemic candidiasis succumb to the infection even with current antifungal therapy [57 , 58] . This outcome is likely to worsen , as drug resistance is a growing problem for two of the three most commonly used antifungal drugs [59 , 60] . An important advantage of targeting FLPs is that they do not have orthologs in humans . The analogous NAD ( P ) H quinone oxidoreductases in mammals , Nqo1 and Nqo2 , are very different [19] . Pharmacological studies on Nqo1 have identified multiple ways that quinone reductases can be targeted . One approach is to identify inhibitors , such as dicoumarol that blocks the Nqo1 activity [61] . In addition , the ability of Nqo1 to reduce small molecule quinones has been studied as a basis for cancer chemotherapy . The fact that many cancer cells overexpress NQO1 has been exploited to develop novel therapies in which quinone compounds are reduced by Nqo1 to convert them into a toxic form that preferentially kills cancer cells [36 , 62] . Similarly , Nqo1 has also been shown to reduce benzoquinone-containing ansamycin drugs , which makes these compounds more potent inhibitors of the Hsp90 chaperone [63] . This suggests yet another way drugs targeting FLPs could be useful , since Hsp90 inhibitors can prevent the emergence of drug resistance in C . albicans [64] . Thus , the important roles of FLPs in oxidative stress response and virulence , combined with their absence in mammalian cells , identifies them as important new targets for therapeutic strategies aimed at combating fungal and bacterial pathogens .
All procedures were approved by the Stony Brook University IACUC Committee ( #1686 ) . Mice were considered to be moribund if food and water could no longer be accessed and then humane euthanasia was performed by carbon dioxide inhalation as per instructions from the Department of Laboratory Animals at Stony Brook University . Oleic acid , linoleic acid , linolenic acid , α-tocopherol ( vitamin E ) , hydrogen peroxide , tert-butyl hydroperoxide , cumene hydroperoxide , menadione , p-benzoquinone , thiobarbituric acid ( TBA ) , hydrochloric acid , and 1 , 1 , 3 , 3-tetramethoxypropane were purchased from Sigma-Aldrich Corp . Trichloroacetic acid was from the Alfa Aesar Company , and nourseothricin from Werner BioAgents . The C . albicans strains used in this study are described in Table 1 . Cells were grown in SD medium ( yeast nitrogen base synthetic medium with dextrose ) [65] . C . albicans deletion mutants were created in strain SN152 ( arg4Δ his1Δ leu2Δ ) by homologous recombination , as described previously [66] . Mutant strains that carry homozygous deletion of PST1 , PST2 , PST3 , YCP4 , or COQ3 were constructed with strain SN152 by the sequential deletion of both copies of the targeted gene . Gene deletion cassettes were generated by PCR amplification of the LEU2 or HIS1 selectable marker gene [66] , using primers that also included ~80 bp of DNA sequence homologous to the upstream or downstream region of the targeted open reading frame ( ORF ) . Cells that had undergone homologous recombination to delete the targeted gene were identified by PCR analysis . A pst3Δ ycp4Δ double mutant strain was constructed by simultaneous deletion of both genes , taking advantage of the fact that they are adjacent in the genome . Homozygous triple and quadruple deletion mutation strains were then constructed by sequential deletion of both copies of the targeted gene using the SAT1 flipper method to recycle the selectable marker [37] . Similar phenotypes were observed for independent isolates . Deletion strains were then made prototrophic by transforming with the ARG4 gene to correct the remaining auxotrophy . Complemented strains , in which the wild-type FLP gene was reintroduced into the corresponding deletion mutant , were constructed by first using PCR to amplify the corresponding FLP gene plus 2000 base pairs ( bp ) upstream and 501 bp downstream of the PST1 open reading frame ( ORF ) , 811 bp upstream and 427 bp downstream of the PST2 ORF , 1681 bp upstream and 427 bp downstream of the PST3 ORF , or 1526 bp upstream and 310 bp downstream of the YCP4 ORF . The DNA fragments were then inserted between the SacI and SacII sites in a derivative of plasmid pDDB57 [67] in which the URA3 gene was replaced with ARG4 . The resulting plasmids were then linearized by restriction digestion in the promoter region , and then transformed into the corresponding homozygous deletion mutant strains using ARG4 for selection . The complementing plasmids were also transformed into the Δ/Δ/Δ/Δ mutant to create strains that express only a single FLP gene . A fully complemented quadruple mutant strain was constructed essentially as described above , except that both PST1 and PST2 genes were cloned in tandem into the ARG4 plasmid . The plasmid was digested with BspEI to linearize it in the promoter region of the PST1 gene , and then it was transformed into the quadruple mutant strain LLF054 . The PST3 and YCP4 genes were cloned between the SacI and ApaI restriction sites of a derivative of plasmid pDDB57 in which the URA3 selectable marker was changed to the SAT1 gene to confer nourseothricin resistance . Note that the PST3 and YCP4 genes are adjacent in the genome in a head to head manner , such that the corresponding open reading frames are transcribed in a divergent manner . A PCR fragment that contains sequences between 1157 bp downstream of the YCP4 ORF and 466 bp downstream of the PST3 ORF was used to create the PST3-YCP4 complementing plasmid . The resulting plasmid was digested with SnaBI to linearize it about 400 bp downstream of the YCP4 open reading frame , and then the DNA was transformed into strain LLF078 , a version of the Δ/Δ/Δ/Δ quadruple mutant in which the PST1 and PST2 genes were already introduced as described above . The open reading frames for E . coli wrbA and rat NQO1 were synthesized by GeneWiz Corp . so that the codons could be optimized and to avoid CUG codons that are translated differently in C . albicans . The wrbA and NQO1 open reading frames were amplified by PCR and introduced downstream of the ADH1 promoter and GFP in plasmid pND391 that carries the ARG4 selectable marker . The resulting plasmids were then transformed into the Δ/Δ/Δ/Δ mutant strain LLF054 to create strains expressing wrbA ( LLF074 ) , NQO1 ( LLF076 ) , or GFP ( LLF080 ) as a control . Spot assays to test growth in the presence of oxidizing agents were carried out essentially as described previously [68 , 69] . C . albicans mutant or wild type strains were grown overnight and then adjusted to 107 cells/ml . Serial 10-fold dilutions of cells were prepared , and three μl of each dilution was then spotted onto solid agar SD medium containing the indicated chemical . The plates were incubated at 37°C for 2 days and then photographed . Each assay was done at least three independent times . Cells were also tested in liquid culture for sensitivity to oxidizing agents by assaying colony-forming units ( CFUs ) . C . albicans cells were grown in synthetic medium with 2% dextrose and without amino acids at 37°C overnight . Cells were harvested at about 6–10 x 107 cells per ml , washed once , and resuspended in phosphate buffer ( 0 . 1M sodium phosphate , pH 6 . 2 , 0 . 2% dextrose ) at 107 cells per ml . Three ml was transferred to a 15 ml glass test tube and then fatty acids were added . Cells were then incubated at 37°C on a tube rotator for the designated period of time . At the end of each treatment , cells were harvested by centrifugation and samples were plated to determine the number of viable CFUs . Results represent the average of 3–6 independent assays . The level of TBARS in yeast whole cell lysates was determined by a modification of a previously described procedure [70] . At the end of the fatty acid treatment described above , 1 . 5 x 107 cells were harvested by centrifugation at 17 , 000 x g for 5 min , washed once with distilled water , and resuspended in 100 μl distilled water in a screw cap tube . 1ml of a freshly prepared solution of 0 . 375% thiobarbituric acid dissolved in 12% trichloroacetic acid and 0 . 5 M hydrochloric acid was added to each tube . After a 20-minute incubation at 90°C , samples were allowed to cool down , and then the insoluble material was sedimented by centrifugation at 17 , 000 x g for 5 min . The absorbance of the supernatant was measured at 535 nm , and corrected by subtracting the nonspecific absorbance at 600 nm . The corrected absorbance was then compared with a standard curve created using 1 , 1 , 3 , 3-tetramethoxypropane treated under the same conditions , which generates malondialdehyde ( MDA ) . Results represent the average of 3–4 independent experiments . The GFPγ variant was fused to the 3’ ends of the open reading frames for PST1 and PST3 using HIS1 selection , in LLF018 , as described previously [71] . Strains were verified by PCR analysis and microscopic examination of GFP fluorescence . To add GFP at the 5’ end of the open reading frames to create N-terminal fusions , GFPγ was introduced downstream of the ADH1 promoter followed by the FLP gene and then the ADH1 terminator in pND397 , which carries an URA3 selectable marker gene [72] . The plasmids of pADH1-GFPγ-PST1 , pADH1-GFPγ-PST2 , or pADH1-GFPγ-PST3 were also linearized with Not I , before being individually transformed into LLF089 using URA3 as the selectable marker to create the strains LLF091 , LLF092 , and LLF093 . The plasmid pADH1-GFPγ-YCP4 was linearized with NotI and transformed into LLF018 using URA3 as the selectable marker to construct the strain LLF071 . GFP fluorescence was analyzed directly in live cells without further processing using a Zeiss Axiovert 200M microscope equipped with an AxioCam HRm camera and Zeiss AxioVision software for deconvolving images . The survival of C . albicans cells in the presence of macrophages was assayed essentially as described previously [73] . Bone marrow was isolated from femurs of 6- to 8-week-old female C57BL/6 mice ( Jackson Laboratories ) and then macrophages were derived from them as previously described [74] . At 18 h prior to infection , bone marrow derived macrophages were seeded into multiwell trays in Dulbecco’s modified Eagle medium ( Invitrogen ) supplemented with 10% fetal bovine serum ( HyClone ) , 15% L-cell-conditioned medium , 1 mM sodium pyruvate , 2 mM glutamate , and 100 ng/ml E . coli LPS ( Sigma ) . Dilutions of C . albicans cells were then plated in multiwell trays in the presence or absence of the macrophages and incubated for 48 h [73] . Microcolonies of growth in each well were then counted to determine the reduction in C . albicans viability due to the presence of macrophages . The results represent the average of three different experiments in which different batches of bone marrow-derived macrophages were used . C . albicans strains were tested for virulence in a mouse model of hematogenously disseminated candidiasis similar to previous studies [45 , 75] . C . albicans cells were cultured by growing overnight at 30°C in YPD medium with 80 μg/ml uridine , reinoculating into fresh medium , and incubating again overnight at 30°C . Cells were prepared for infection assays by washing twice in phosphate-buffered saline ( PBS ) , counting in a hemocytometer , and then diluting to 1 . 25 x 106 cells/ml with PBS . Female BALB/c mice were injected via the lateral tail vein with 2 . 5 x 105 cells , and then monitored at least twice a day for 28 days . Statistical analyses of the results for the survival studies were carried out using a log rank test ( Mantel-Haenszel ) with the Prism 6 software program ( GraphPad Software , Inc . , La Jolla , CA ) . To assess fungal burden , kidneys were excised , weighed , and then homogenized in 5 ml PBS for 30 s with a tissue homogenizer ( Pro Scientific Inc . ) . The CFU per gram of kidney was determined by plating dilutions of the homogenates on YPD agar medium plates , and incubating for 2 days at 30°C . Statistical analysis of the CFU data was carried out with Prism 6 software using one-way analysis of variance with a nonparametric Kruskal-Wallis test and Dunn’s post-hoc test . For histological analysis , kidneys were excised from mice 2 d post infection , fixed with formaldehyde , and then stained with Hematoxylin and Eosin ( H&E ) to detect leukocytes or with Gomori-Methenamine Silver ( GMS ) to detect fungal cells by McClain Laboratories , Smithtown , NY . The accession numbers for the C . albicans genes used in this study are as follows: Standard NameSystematic NameOrf19NameGenBank DesignationPST1C2_06870Corf19 . 2241XP_714771 . 1PST2C2_08640Corf19 . 3612XP_714456 . 1PST3CR_05390Worf19 . 5285XP_710366 . 1YCP4CR_05380Corf19 . 5286XP_710367 . 1COQ3C6_01840Corf19 . 3400XP_716710 . 1 | Oxidative damage is a fundamental problem for cells and a particular challenge for microbial pathogens , which require special mechanisms to resist the oxidative attack by the host immune system . We identified four proteins in the human fungal pathogen Candida albicans that belong to a large family of enzymes in bacteria and plants that reduce quinone molecules to detoxify them . Interestingly , mutational studies in C . albicans showed that these enzymes also confer resistance to a wide range of oxidants , suggesting they may have broader impact by reducing the major quinone present in cells ( ubiquinone or coenzyme Q ) . In support of this , we found that mutating the COQ3 gene to block ubiquinone synthesis rendered cells highly sensitive to oxidative stress , revealing that it plays a very important antioxidant function in addition to its well known role in energy metabolism . These quinone reductases play a critical role in vivo , as they were required for virulence in mouse infections studies . Since mammalian cells lack this type of quinone reductase , this difference could be exploited to develop much needed novel therapeutic approaches for fungal and bacterial pathogens . | [
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] | [] | 2015 | Flavodoxin-Like Proteins Protect Candida albicans from Oxidative Stress and Promote Virulence |
The nematode Caenorhabditis elegans has emerged as a genetically tractable animal host in which to study evolutionarily conserved mechanisms of innate immune signaling . We previously showed that the PMK-1 p38 mitogen-activated protein kinase ( MAPK ) pathway regulates innate immunity of C . elegans through phosphorylation of the CREB/ATF bZIP transcription factor , ATF-7 . Here , we have undertaken a genomic analysis of the transcriptional response of C . elegans to infection by Pseudomonas aeruginosa , combining genome-wide expression analysis by RNA-seq with ATF-7 chromatin immunoprecipitation followed by sequencing ( ChIP-Seq ) . We observe that PMK-1-ATF-7 activity regulates a majority of all genes induced by pathogen infection , and observe ATF-7 occupancy in regulatory regions of pathogen-induced genes in a PMK-1-dependent manner . Moreover , functional analysis of a subset of these ATF-7-regulated pathogen-induced target genes supports a direct role for this transcriptional response in host defense . The genome-wide regulation through PMK-1– ATF-7 signaling reveals a striking level of control over the innate immune response to infection through a single transcriptional regulator .
Convergent genetic studies of host defense of Drosophila melanogaster and mammalian innate immune signaling revealed a commonality in signaling pathways of innate immunity that has helped motivate the study of pathogen resistance mechanisms in genetically tractable host organisms such as Caenorhabditis elegans [1] . The simple C . elegans host has enabled the genetic dissection of integrative stress physiology orchestrating host defense of C . elegans[2–5] . Genetic analysis of resistance of C . elegans to infection by pathogenic Pseudomonas aeruginosa has defined an essential role for a conserved p38 mitogen-activated protein kinase pathway that acts on a CREB/ATF family bZIP transcription factor , ATF-7 , in immune responses [6 , 7] . A complementary approach to characterizing the host response has been organismal transcriptome-wide characterization of genes induced upon infection by a number of different bacterial pathogens [8–15] . Putative effector genes encoding lysozymes and C-type lectin domain ( CTLD ) -containing proteins have been identified that have also served as useful markers of immune induction . Here , we report the genome-level characterization of the C . elegans response to P . aeruginosa that is mediated by ATF-7 activity downstream of PMK-1 activation , combining RNA-seq analysis of pathogen-induced gene expression with ChIP-seq analysis of ATF-7 binding , which suggests global regulation of the immune response of C . elegans through a single MAPK- transcription factor pathway .
We performed RNA-seq on wild-type ( N2 ) , pmk-1 mutant , or atf-7 mutant animals exposed to E . coli OP50 or P . aeruginosa PA14 to identify genes that are differentially regulated upon infection that also require PMK-1 or ATF-7 for induction ( Fig 1A ) . We found that in wild-type animals , 890 genes were two-fold upregulated ( adjusted p-value <0 . 05 ) , and 803 genes were two-fold downregulated upon P . aeruginosa exposure , compared to animals exposed in parallel to E . coli ( Fig 1B; S1 Table ) . Many of these upregulated genes have been previously implicated in the C . elegans immune response , including genes encoding C-type lectin domain ( CTLD ) -containing genes and lysozymes , corroborating prior microarray-based gene expression studies ( Fig 1C ) [8–10 , 12–14] . In contrast , gene ontology analysis of genes that are decreased in expression upon P . aeruginosa exposure shows enrichment for genes associated with homeostasis with significant ontology terms consistent with growth , development and reproduction ( S1A Fig ) . Of note , many of the genes upregulated in response to P . aeruginosa exposure exhibit relatively low expression when animals are propagated on E . coli , whereas genes that are decreased in expression upon P . aeruginosa exposure are expressed at a higher basal level during normal growth conditions on E . coli ( Fig 1D , S1B Fig ) . In parallel , we analyzed P . aeruginosa-mediated gene expression changes in pmk-1 and atf-7 mutants to identify the proportion of gene changes induced by P . aeruginosa exposure that required PMK-1 and/or ATF-7 for induction ( S2 Fig ) . We classified genes that failed to significantly meet a two-fold expression change cutoff upon exposure to P . aeruginosa in the pmk-1 or atf-7 mutant background as being PMK-1- or ATF-7-dependent , respectively . We observed that 70% of genes significantly induced two-fold or greater by P . aeruginosa exposure were no longer fully induced upon loss of pmk-1 , and that 53% of upregulated genes were no longer fully induced upon loss of atf-7 ( Fig 1E , S1 Table ) . We also found that 41% of genes reduced two-fold or more by P . aeruginosa required PMK-1 , and 50% required ATF-7 for reduction of expression ( S2B Fig , S1 Table ) . These data suggest a high degree of involvement of PMK-1-ATF-7 signaling in the majority of changes in gene expression induced in response to infection by P . aeruginosa . To evaluate the role of ATF-7 in the direct regulation of genes induced by P . aeruginosa infection , we performed chromatin immunoprecipitation followed by sequencing ( ChIP-seq ) of animals carrying a GFP-tag fused to the C-terminal end of the endogenous atf-7 locus . Using a GFP polyclonal antibody for immunoprecipitation , we generated ChIP binding profiles for animals in either the wild-type background ( atf-7 ( qd328[atf-7::2xTY1::GFP] ) ) or the pmk-1 mutant background ( pmk-1 ( km25 ) ;atf-7 ( qd328[atf-7::2xTY1::GFP] ) ) after a four hour exposure to either E . coli OP50 or P . aeruginosa PA14 , for a total of four conditions , similar to the treatment described in Fig 1A . In all conditions analyzed , ATF-7 exhibited abundant association throughout the genome , with 8 , 962 total peaks identified as enriched by MACS2 , which map to a +/- 1 . 5 kB region corresponding to 23 . 7% of genes and 24% of transcription start sites ( TSSs ) in the C . elegans genome ( WS258 ) ( S2 Table ) . Analysis of the ATF-7 binding profile across all genes associated with enriched TSSs , as well as the subset altered in expression by P . aeruginosa in wild-type animals according to our RNA-seq data , revealed that ATF-7 is preferentially located at the promoter regions of genes that are increased in expression by P . aeruginosa , and that this enrichment for ATF-7 is lessened by pmk-1 loss ( Fig 2B , S3A Fig ) . Strikingly , this dependence of ATF-7 promoter occupancy on PMK-1 exists on P . aeruginosa , but not on E . coli . This finding is consistent with our previously published model , whereby ATF-7 occupancy of its target promoters is not simply controlled by pathogen-dependent PMK-1 activity [7] . MEME analysis of the most enriched loci identified significant enrichment for the motif GACgTCA , which corresponds to the Jun D bZIP motif expected for ATF-7 ( Fig 2A , S3B Fig ) . This motif is present in as many as 80% of the most highly enriched regions of the genome and its abundance is positively correlated with enrichment levels . To identify the most likely immediate downstream targets of ATF-7 , we set a peak threshold based on the fraction of peaks containing the bZIP motif after ranking ATF-7 peaks by enrichment ( S3C Fig ) . This resulted in ~1500–4000 highly enriched locations per experiment . Overlap of the of the retained ATF-7 binding profile compared to the RNA-seq data from P . aeruginosa infection revealed consistent 2–4 fold enrichment of ATF-7 binding in genes that depend on ATF-7 for expression changes stimulated by P . aeruginosa exposure ( S4A and S4B Fig ) . We further assessed the significance of these enrichments using a Gene Set Enrichment Analysis ( GSEA ) , which showed that ChIP peaks were enriched for association with transcripts that are upregulated upon pathogen exposure in both E . coli and P . aeruginosa ChIP conditions ( Fig 2C , S4C Fig ) . This association remains in the pmk-1 mutant , although at a weaker significance level ( Fig 2D , S4D Fig ) . Moreover , we also evaluated ATF-7 binding at individual genomic loci induced by P . aeruginosa infection that were dependent on ATF-7 for full upregulation . Examinations of distinct genetic loci further support the conclusions drawn from the metagene analyses described above ( Fig 3 ) . These observations suggest a direct transcriptional regulatory role for ATF-7 in the induction of broad transcriptional changes upon immune challenge involving activation of p38/PMK-1 MAPK signaling in response to P . aeruginosa infection . For functional validation of putative ATF-7-regulated immune response target genes , we focused on transcripts that were upregulated at least two-fold by P . aeruginosa exposure in an ATF-7-dependent manner and that were also bound by ATF-7 in any of our four ChIP-seq conditions . We chose to concentrate on genes upregulated in response to pathogen exposure ( Fig 1C ) in contrast to downregulated genes , which appear to reflect a reduction in general growth and metabolism as is reflected in our RNA-seq data ( S1A Fig ) [10] . Included among these putative ATF-7 targets were genes encoding antimicrobial effector molecules , such as CTLD-containing proteins and lysozymes ( S3 Table ) . We determined whether RNAi-mediated knockdown of these genes resulted in enhanced susceptibility to killing by P . aeruginosa and observed that RNAi of 13 of 43 genes conferred enhanced sensitivity to killing by P . aeruginosa , without affecting survival on non-pathogenic E . coli ( S3 Table , S4 Fig ) . Our data suggest that ATF-7 is a direct regulator of immune effector genes that is regulated by PMK-1 p38 MAPK . We previously proposed a model in which PMK-1 phosphorylates ATF-7 in response to pathogen infection , switching the activity of ATF-7 from that of a transcriptional repressor to that of an activator , allowing the induction of immune response genes [7] . Our data here are consistent with this model , showing a strong dependence of pathogen-induced gene induction on PMK-1 and ATF-7 , and a high degree of occupancy of regulatory regions of pathogen-induced genes by ATF-7 under basal and pathogen-induced conditions , with ATF-7 occupancy of pathogen-induced genes being strongly dependent on PMK-1 . Moreover , our data reveal that PMK-1-ATF-7 signaling regulates over half of all pathogen-induced genes at the genome-wide level . PMK-1 signaling has also been implicated in a number of non-infection contexts in C . elegans [2 , 16 , 17] . Interestingly , we observed that ATF-7 binds quite strongly to several key regulators of stress response pathways . We found that ATF-7 exhibits binding affinity to regulators of autophagy ( lgg-1 ) , the Unfolded Protein Response ( xbp-1 ) , and the oxidative stress response ( skn-1 ) , as well as several immunity regulators ( hlh-30 , zip-2 , and interestingly , atf-7 ) ( Fig 4 ) . These observations suggest that initiation of other stress responses may be integrated with the immune response . For example , we have previously shown that immune response activation in developing larva is lethal without compensatory XBP-1 activity , establishing an essential role for XBP-1 during activation of innate immunity during infection of C . elegans [2] . Comparison of ATF-7 target genes identified by ChIP-seq with published target gene lists inferred from transcriptional profiling studies indeed suggests statistically significant overlap of ATF-7 regulation with SKN-1 ( p<0 . 001 , hypergeometric test with Bonferroni correction ) and ZIP-2 ( p<0 . 001 , hypergeometric test with Bonferroni correction ) ( S6 Fig ) [18] . However , caution should be taken when comparing these datasets , as they were collected by methods distinct from our intersection of ChIP-seq and RNA-seq results , and consequently additional experimental evidence is needed to corroborate these associations . We speculate that ATF-7 may function to activate anticipatory stress responses that can be activated in concert with innate immunity to promote host survival during microbial infection in a context-dependent manner . Our genomic and genetic findings in the simple , genetically tractable C . elegans host reveal a striking degree of global regulation of the organismal response to pathogenic bacteria through a single p38 MAPK-regulated transcriptional regulator . Our data support the idea that host defense , on a genome-wide and organism-wide level , is under the control of a limited number of stress-activated signaling pathways that regulate global regulators of gene transcription .
Strains used: N2 , ZD386 ( atf-7 ( qd22 qd130 ) ) , KU25 ( pmk-1 ( km25 ) ) , ZD1807 ( atf-7 ( qd328[atf-7::2xTY1::GFP] ) ) , ZD1976 ( atf-7 ( qd328[atf-7::2xTY1::GFP] ) ;pmk-1 ( km25 ) ) . C . elegans were maintained at 16°C on E . coli OP50 as described [19] . The atf-7 ( qd328 ) allele was generated by the CRISPR-Cas9 system as described [19 , 20] and verified by Sanger sequencing . GFP expression in ZD1807 ( atf-7 ( qd328 ) ) was verified by immunobloting , and pull-down was assessed by IP-IB . The atf-7 ( qd238 ) allele was confirmed to function as wild-type , as assayed by susceptibility to P . aeruginosa strain PA14 in a slow kill assay , and then crossed into the pmk-1 ( km25 ) mutant background . Slow Kill Assay ( SKA ) plates were prepared as previously described [20] . P . aeruginosa strain PA14 or E . coli OP50 was grown overnight in Luria Broth ( LB ) , seeded onto SKA media and then grown overnight at 37°C , followed by an additional day at room temperature as previously described [21] . Large populations of animals were synchronized by egg-preparation of gravid adult worms in bleach , followed by L1 arrest overnight in M9 buffer . L1 animals were dropped onto concentrated OP50 lawns seeded onto Nematode Growth Media ( NGM ) and raised to the L4 larval stage at 20°C ( about 40 h ) . Upon reaching L4 , worms were washed off growth plates with M9 and placed on SKA plates prepared as described above , seeded with either PA14 or OP50 and incubated at 25°C for four hours . At this time , worms were harvested by washing for downstream applications . After three washes in M9 buffer , animal pellets were resuspended in an equal volume of PBS + complete ULTRA protease inhibitor tablets ( Roche ) , flash frozen in liquid nitrogen , and stored at -80°C until chromatin immunoprecipitation ( ChIP ) . ChIP was preformed as described [22 , 23] using Ab290 , a ChIP-grade polyclonal GFP antibody ( Abcam ) . Libraries were prepared using the SPRIworks Fragment Library System ( Beckman Coulter ) and single-end sequenced on an Illumina HiSeq2000 sequencer . Three biological replicates of at least 15 , 000 animals were prepared and sequenced for each condition , with the exception of only two replicates for atf-7 ( qd328 ) on PA14 , as one of the samples failed to pass quality control . ChIP-seq reads were aligned against the C . elegans WBPS9 assembly using bwa v . 0 . 7 . 12-r1039 [24] and the resulting bam files were sorted and indexed using samtools v . 1 . 3 [25] . Sorted bam files were pooled by strain and microbial treatment , and peaks were called using MACS2 ( v . 2 . 1 . 1 . 20160309 ) , as follows: callpeak on specific strain bam file ( “-t” flag ) against the N2_PA14 control sample bam file ( “-c” flag ) callpeak -c N2_PA14_control . sorted . bam -g ce—keep-dup all—call-summits—extsize 150 -p 1e-3—nomodel -B . Peak locations were intersected with regions +/-0 . 5kb around annotated TSS based on the WBPS9/WS258 annotation using bedtools intersect ( v2 . 26 . 0 ) [26] , and in cases of multiple peaks associated with a given TSS , peaks with maximal enrichment over N2 control were retained . For the purpose of motif identification , peaks were ranked by fold-enrichment over N2 control in descending order and the top 400 peaks were retained , regions +/- 200 bps around the summit were retrieved and sequences were obtained with bedtools getfasta . MEME-ChIP v . 4 . 12 . 0 [27] was used to call motifs using the following parameters: meme-chip -oc . -time 300 -order 1 -db db/JASPAR/JASPAR2018_CORE_nematodes_non-redundant . meme -meme-mod anr -meme-minw 5 -meme-maxw 30 -meme-nmotifs 8 -dreme-e 0 . 05 -centrimo-local -centrimo-score 5 . 0 -centrimo-ethresh 10 . 0 . Presence of the top motifs under each peak called by macs2 was assessed using Mast v . 5 . 0 . 1 [28] on the same +/- 200bp region around the summit of each peak . The number of peaks with one or more occurrences of the motif was tallied using a 200-peak window , and plotted across all peaks ranked either by log-fold enrichment over N2 or–log-transformed p-values . Inflection points in the motif density function were used to narrow down the number of peaks retained for downstream analyses . After three washes in M9 buffer , TRIzol Reagent ( Invitrogen ) was added to worm pellets and flash frozen in liquid nitrogen . Following an additional round of freeze-thaw , RNA was isolated using the Direct-zol RNA MiniPrep kit ( Zymo Research ) . Libraries were prepared using the Kapa mRNA Hyperprep kit and paired end reads were sequenced on the Illumina NextSeq500 sequencer . Three biological replicates of at least 1 , 000 animals were prepared and sequenced for each condition , with the exception of only two replicates for atf-7 ( qd22 qd130 ) on PA14 , as one of the samples failed to pass quality control . Reads were aligned against the C . elegans WBPS9 assembly/ WS258 annotation using STAR v . 2 . 5 . 3a [29] with the following flags: -runThreadN 16—runMode alignReads—outFilterType BySJout—outFilterMultimapNmax 20—alignSJoverhangMin 8—alignSJDBoverhangMin 1—outFilterMismatchNmax 999—alignIntronMin 10—alignIntronMax 1000000—alignMatesGapMax 1000000—outSAMtype BAM SortedByCoordinate—quantMode TranscriptomeSAM . with—genomeDir pointing to a low-memory footprint , 75nt-junction WBPS9/WS258 STAR suffix array . Gene expression was quantitated using RSEM v . 1 . 3 . 0 [30] with the following flags for all libraries: rsem-calculate-expression—calc-pme—alignments -p 8 against an annotation matching the STAR SA reference . Posterior mean estimates ( pme ) of counts and estimated “transcript-TPMs” were retrieved for genes and isoforms . Subsequently , counts of isoforms sharing a transcription start site ( TSS ) were summed , and differential-expression analysis was carried out using DESeq2 [31] in the R v3 . 4 . 0 statistical environment , building pairwise models of conditions to be compared ( microbial exposures within each genotype ) . Sequencing library size factors were estimated for each library to account for differences in sequencing depth and complexity among libraries , as well as gene-specific count dispersion parameters ( reflecting the relationship between the variance in a given gene’s counts and that gene’s mean expression across samples ) . Differences in gene expression between conditions ( expressed as log2-transformed fold-changes in expression levels ) were estimated under a general linear model ( GLM ) framework fitted on the read counts . In this model , read counts of each gene in each sample were modeled under a negative binomial distribution , based on the fitted mean of the counts and aforementioned dispersion parameters . Differential expression significance was assessed using a Wald test on the fitted count data ( all these steps were performed using the DESeq ( ) function in DESeq2 ) [31] . P-values were adjusted for multiple-comparison testing using the Benjamini-Hochberg procedure [32] . Raw data presented in this manuscript have been deposited in NCBI’s Gene Expression Omnibus [33] and are accessible through GEO SuperSeries accession number GSE119294 , which contains SubSeries GSE119292 ( RNA-seq data , including count files ) and SubSeries GSE119293 ( ChIP-seq data , including wig files and peak calls ) . Metagene analyses of gene expression and ATF-7 binding enrichment were generated by ngs . plot as described [34] , using ChIP . bam files from each condition normalized to N2 control as input . Genes considered two-fold upregulated or downregulated are listed in the “N2_up” and “N2_down” tabs of S1 Table , respectively . Correlations between ATF-7 binding and regulation of gene expression were interrogated using the gene set enrichment analysis ( GSEA ) framework [35] . Briefly , all transcription start sites ( TSSs ) associated with a protein-coding transcript were ranked based on differential expression results from DESeq2 ( log2 fold-changes ) , which is a measure of the correlation between their expression and the host response to infectious agents . Biases in expression of ATF-7-bound TSSs were assessed using a walk down the list tallying a running-sum statistic , which increases each time a TSS is part of the list and decreases otherwise . The maximum of this metrics ( i . e . where the distribution if furthest away from the background ) is called the enrichment score ( ES ) . Significance is estimated using random permutations of the TSSs to generate p-values gauging how often the observed ES can be seen in randomized gene sets , for each direction of the expression biases independently . Multiple-testing correction is addressed using a false-discovery rate calculation on permuted datasets . Genes with adjusted p-values <0 . 05 were considered for Gene Ontology enrichment analysis using the DAVID online webtool , considering as a background the union of all genes with a non-zero baseMean value across any of the DE comparison , based on unique WormBase IDs . PA14 plates were prepared as described as above . N2 animals were grown on NGM , supplemented with 25 ug/mL carbenicillin and 2mM isopropyl b-D-1 thiogalactopyranoside ( IPTG ) , that was seeded with either the E . coli HT115 expressing plasmids targeting the gene of interest or the empty L4440 vector backbone for two generations prior to each experiment . Animal populations were synchronized by egg lay . At the L4 larval stage , approximately 30 worms were transferred to prepared SKA plates and incubated at 25°C . Animals were scored for killing twice daily until the majority of animals had died . Within each experiment , three plates were prepared and scored per RNAi treatment . All clones were obtained from the Ahringer [36] or Vidal [37] RNAi libraries and were verified by sequencing . For a list of all RNAi clones used , see S4 Table . | Innate immunity is the first line of defense against invading microbes across metazoans . Caenorhabditis elegans lacks adaptive immunity and is therefore particularly dependent on mounting an innate immune response against pathogens . A major component of this response is the conserved PMK-1/p38 MAPK signaling cascade , the activation of which results in phosphorylation of the bZIP transcription factor ATF-7 . Signaling via PMK-1 and ATF-7 causes broad transcriptional changes including the induction of many genes that are predicted to have antimicrobial activity including C-type lectins and lysozymes . In this study , we show that ATF-7 directly regulates the majority of innate immune response genes upon pathogen infection of C . elegans , and demonstrate that many ATF-7 targets function to promote pathogen resistance . | [
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] | 2019 | Global transcriptional regulation of innate immunity by ATF-7 in C. elegans |
Single base substitutions constitute the most frequent type of human gene mutation and are a leading cause of cancer and inherited disease . These alterations occur non-randomly in DNA , being strongly influenced by the local nucleotide sequence context . However , the molecular mechanisms underlying such sequence context-dependent mutagenesis are not fully understood . Using bioinformatics , computational and molecular modeling analyses , we have determined the frequencies of mutation at G•C bp in the context of all 64 5′-NGNN-3′ motifs that contain the mutation at the second position . Twenty-four datasets were employed , comprising >530 , 000 somatic single base substitutions from 21 cancer genomes , >77 , 000 germline single-base substitutions causing or associated with human inherited disease and 16 . 7 million benign germline single-nucleotide variants . In several cancer types , the number of mutated motifs correlated both with the free energies of base stacking and the energies required for abstracting an electron from the target guanines ( ionization potentials ) . Similar correlations were also evident for the pathological missense and nonsense germline mutations , but only when the target guanines were located on the non-transcribed DNA strand . Likewise , pathogenic splicing mutations predominantly affected positions in which a purine was located on the non-transcribed DNA strand . Novel candidate driver mutations and tissue-specific mutational patterns were also identified in the cancer datasets . We conclude that electron transfer reactions within the DNA molecule contribute to sequence context-dependent mutagenesis , involving both somatic driver and passenger mutations in cancer , as well as germline alterations causing or associated with inherited disease .
At least fifteen cancer genome sequencing projects were reported between 2007 and 2011 [1]–[15] , and this number is now increasing very rapidly . These studies have been critical for addressing mechanisms of somatic mutation , such as those associated with single base substitutions ( SBSs ) , which not only represent the vast majority of lesions in most patients , but also ( in the case of some driver mutations ) alter gene function , thereby initiating tumor development . Such investigations have demonstrated that SBSs do not occur randomly throughout the genome . Indeed , frequent C→T transitions have been noted at CpG dinucleotides [4] , [9] , [16]–[18] , which are attributable to the high rate of spontaneous 5-methylcytosine ( 5mC ) deamination at methylated 5mCpG sites [19] , [20] . In individuals with a history of exposure to cigarette smoke or radio/chemotherapy , high proportions of G→T transversions , G→A and G→C substitutions at GpA and CpG dinucleotides , and A→T and A→G substitutions at TpA dinucleotides have also been reported [6] , [8] , [9] , [21] , suggestive of DNA damage through exogenous mechanisms [22] , [23] . Likewise , large numbers of C→T transitions at YpC ( Y = C/T ) dinucleotides in melanoma in sun-exposed areas of the skin [11] , [21] , [24] have been attributed to cyclobutane pyrimidine dimer ( CPD ) formation following UV photoexcitation [25] . For less common types of substitution , such as T→C at ApT dinucleotides in hepatocellular carcinoma [16] , underlying mutational mechanisms have yet to be proposed . Studies aimed at identifying the mechanisms underlying the sequence context dependency of SBSs observed in inherited disease [26] , cancer and phylogenetic analyses [20] , [27]–[31] are few in number . A recent analysis of breast cancer genomes identified five types of trinucleotide motif enriched in SBSs , all of which contained either a CpG or a GpA motif [18] . Substitutions at CpG were attributed to 5mCpG deamination , whereas mutations at the GpA motif , which displayed sporadic clustering , were linked to enzymatic deamination of C at TpC by TC-specific cytosine deaminases [18] . Cluster analyses in other types of cancer led Roberts et al . to propose a similar mechanism for mutations at GpA sequences [32] . Indeed , recent work has identified APOBEC3B as a likely enzymatic source of C→T transitions in breast cancer [33] . In melanoma , Krauthammer et al . reported an enrichment of mutations at C in the context of 5′-TTTCGT-3′ motifs , a finding which was attributed to energy transfer along the pyrimidine-rich strand upon UV exposure [34] . Thus , although the influence of flanking bases on SBSs appears to extend beyond dinucleotide units , substantial gaps remain in our understanding of the mutational mechanisms involved . Elucidating these mechanisms is crucial , not only because they provide critical information on the earliest steps of cancer-associated mutagenesis , but also because they may account for inter-individual genetic variation as well as somatic age-related changes within the same individual . Herein , we analyzed the frequencies of mutation at G•C bp in the context of all possible 4-bp 5′-NGNN-3′ units from >530 , 000 SBSs representing 21 cancer genomes , >77 , 000 germline mutations causing or associated with human inherited disease and 16 . 7 million benign germline single nucleotide variants ( SNVs ) . The 64 combinations of 5′-NGNN-3′ motifs provided a suitable set size that was not too large to hamper sequence representation while doubling the length of base interactions relative to the commonly employed dinucleotide sequences [35] . In several cancer mutation datasets , but also in the germline mutations , the frequencies of substitutions correlated with the free energy of base stacking along the G-containing strand , as well as with ionization potentials , i . e . the energy required for abstracting an electron from the target guanines . Such behavior is consistent with an electron transfer mechanism as a consequence of one-electron oxidation reactions between the DNA molecule and radical species in the cell [22] , [36] , [37] . We conclude that electron transfer contributes to sequence context-dependent SBSs , not only in the context of cancer genomes but also in pathogenic germline mutations .
We collected the publicly available data from cancer genome studies reported in PubMed between 2007 and 2011 , together with the 5 largest datasets from the International Cancer Genome Consortium ( ICGC ) ( Table 1 ) . Twenty-one datasets , 13 from exome-wide ( EWS ) and 8 from genome-wide ( GWS ) sequencing scans , together represented cancers from 14 different tissues comprising 1 , 149 patient samples ( 1 to 393 samples per dataset ) and 2 cell lines , for a total of 533 , 482 SBSs ( Table S1 ) . Additional SBSs from 3 other datasets were included in the analysis: a subset of nonsense and missense germline mutations of pathological significance in the context of inherited disease derived from the Human Gene Mutation Database ( HGMD ) ; a subset of splicing mutations from HGMD causing human inherited disease; and the set of SNVs from the “1000 Genomes Project” included in dbSNP build 129 ( “rs” set ) , to allow direct comparison of the cancer-associated somatic mutations with germline mutations and polymorphisms present in the general population . The median fractions of SBSs that occurred at G•C bp were 0 . 78 ( mean ± SD , 0 . 75±0 . 11 ) for the EWS datasets and 0 . 56 ( mean ± SD , 0 . 60±0 . 13 ) for the GWS datasets ( Fig . 1A ) , significantly higher than the average GC-content exome-wide ( 0 . 55 ) [38] and genome-wide ( 0 . 41 ) , respectively [39] ( both P values were ∼2 . 2×10−16 , the smallest computable number by implementation of the binomial exact test in R ) . Thus , SBSs occurred more frequently at G•C bp , as compared to A•T bp , than expected by chance alone , both in cancer genomes and in the germline as a cause of inherited disease . We addressed the sequence-dependent occurrence of SBSs at G•C bp by retrieving the 5′-NGNN-3′ and their complementary 5′-NNCN-3′ sequences ( henceforth referred to as NGNN ) , either genome-wide or exome-wide , and calculating the fractions of mutated motifs , f ( NGNN ) , for each of the 64 sequence combinations . There were two confounding factors in computing f ( NGNN ) : the first related to the fact that only certain portions of the human genome may be effectively mapped by next-generation sequencing; the second originating from the various methodologies used during base-variant mapping and calling ( see Materials and Methods ) . Therefore , we first assessed the relative representation of NGNN sequences in the homologous portions ( Segmental Duplications , repetitive elements and simple repeats ) as compared with the unique portions of the human genome ( Text S1 and Table S1 ) . These analyses indicated that CGNN motifs are significantly overrepresented in Segmental Duplications ( Text S1 and Table S2 ) . We then used the genomic mutation sites ( Table S3 ) to compute f ( NGNN ) according to three methods ( Duke35 , CRG50 and T_hg19 ) for the GWS datasets and three methods ( AgilentV2 , CGR50_exons and T_exons ) for the EWS datasets ( Text S1 ) , and used these fractions to assess the extent to which base composition at positions 1 , 3 and 4 ( P1 , P3 and P4 ) would influence G•C mutations at position 2 ( P2 ) for different classes of NGNN sequences . Mutations were observed at each NGNN sequence combination with the exception of the two small colorectal cancer datasets ( Table 1; Table S4 , Panel A ) . Thus , for these two datasets , z-tests , rather than t-tests , were used to assess statistical significance . Irrespective of the mapping method used , f ( CGNN ) mean values were significantly greater than f ( DGNN ) ( D = A/G/T ) mean values in all cancer ( 2–11 fold , depending on cancer type with gastric and colorectal cancers displaying the largest differences ) and germline mutation datasets ( Table S4 , Panels B–D ) , with −logP values ranging from 8 ( Breast; 9 . 3±3 . 3×10−5 for CGNN vs . 4 . 0±2 . 6×10−5 for DGNN , AgilentV2 counts ) to 41 ( 1000GP; 6 . 9±1 . 3×10−2 for CGNN vs . 7 . 5±1 . 5×10−3 for DGNN , CRG50 counts ) ( Table 1 ) . Exceptions were the two Melanoma datasets , for which such differences were modest due to considerable variability in the data ( 2 . 2±3 . 4×10−4 for CGNN vs . 0 . 9±1 . 2×10−4 for DGNN , P-value 0 . 02 for Melanoma_ews , AgilentV2 counts; 4 . 9±7 . 5×10−5 for CGNN vs . 2 . 4±2 . 8×10−5 for DGNN , P-value 0 . 06 for Melanoma_gws , Duke35 counts ) . Thus , with the notable exception of the melanomas , the CpG dinucleotide , a substrate for cytosine methylation , represents a strong mutation hotspot , both in the soma and the germline . For the DGNN sequences , a P3-purine significantly increased the proportions of P2 SBSs , f ( DGRN ) , compared to a pyrimidine , f ( DGYN ) in 7 cancer types ( −logP values 3–9 ) , including lung , head and neck , and melanoma ( Table 1 ) , for which associations with exposure to either cigarette smoke or sunlight have been documented . An additional dataset , Lung_sc , from the established cell-line NCI-H209 displayed modest f ( DGRN ) >f ( DGYN ) ( P-value ∼0 . 04 ) . Again , in no cases were P-values contradictory based upon the mapping method used ( Table S4 , Panels B–D ) . The data for the melanomas were particularly striking since P3-A increased the fractions of mutation at P2-G by ∼10-fold relative to DGBN ( B = C/G/T ) ( 14 . 3 vs . 1 . 7×10−5 for Melanoma_gws , P-value 9 . 1×10−4; and 68 . 4 vs . 6 . 4×10−5 for Melanoma_ews , P-value 2 . 4×10−5; according to Duke35 and AgilentV2 , respectively , Table S5 ) . In addition , the CGAN motifs displayed ∼3-fold higher mutation fractions than the DGAN motifs ( 14 . 3 vs . 4 . 9×10−5 , P-value 0 . 019; and 68 . 4 vs . 20 . 5×10−5 , P-value 2 . 8×10−3 ) in Melanoma_gws and Melanoma_ews , respectively , although a mutagenic role for CpG methylation was not apparent ( i . e . the CGBN and DGBN fractions were indistinguishable , P-values ∼0 . 6–0 . 7 ) . In addition , for the two melanoma datasets , P4-A significantly increased ( 2 . 2±0 . 2 fold ) mutation at P2-G ( Fig . 1B ) when P2 and P4 were separated by a purine ( P-value 5 . 6×10−7 ) . Additional analyses in four melanoma datasets [17] , [21] , [34] , [40] confirmed this finding ( ratio for the eight possible NGRA/NGRB groups in these four datasets was 2 . 2±0 . 5 and 2 . 2±0 . 3 for the combined six datasets , Table S6 ) . The increase in mutation at P2-G , relative to P4-C and P4-T , was also observed for P4-G; however , this effect was less consistent than P4-A and was observed more frequently when P3 was occupied by a guanine ( 18/23 cases ) rather than an adenine ( 5/23 cases ) , Table S6 . In summary , SBSs at P2-G were dependent upon the sequence composition of the 3′-nearest neighbor in a number of different cancer types; in melanoma , this effect extended to the next 3′ base when bridged by a purine . Thus , GpR and GpRpA sequences constitute mutational hotspots that render the 5′ G sensitive to mutation . Further analyses performed in individual cancer samples ( Text S1 , Figure S1 and Table S4 , Panels B–E ) indicated that biological mechanisms , rather than differences in variant-calling algorithms or variability between individual samples , were the likely causes of such mutational patterns . Guanine is the most readily oxidized base [41] and its ionization energy , i . e . the energy required to abstract an electron , depends upon the identity of the flanking nucleotides [42]–[45] . Substantial work performed with model DNA sequences in vitro has shown that , following one-electron oxidation reactions , the sites of electron loss ( hole ) migrate efficiently ( rate constants ∼107 s−1 ) from the original locations to distant sites , where they become trapped in troughs of low ionization energy , most often at GG and GGG sequences [36] , [44] , [46]–[48] . Because oxidative DNA damage occurs spontaneously in the cell , we tested whether the sequence-dependent SBS patterns were consistent with a mutagenesis model that included: a ) loss of an electron within the NGNN sequences; b ) hole migration to the P2-guanine; and c ) chemical modification of the P2-guanines leading to base substitutions [22] . The binding energy of single-stranded stacked bases is presumed to be dependent upon the affinity of interactions , or the extent of electron sharing , between π orbitals across bases [49] . The free energy of base stacking , rather than hydrogen bonding , has been reported to be the major source of stability in duplex DNA [50] . Hence , we expected that strongly interacting bases would be more prone to one-electron oxidation and , hence , to higher SBS rates than weakly interacting bases . To this end , we used the absolute free-energy values of base stacking between non-bonded bases , ΔG ( ν ) , derived from a theoretical study [51] using a continuum solvation model and Amber force field to assess the relationships with f ( DGNN ) values , as we previously employed [52] . For 5/7 datasets with f ( DGRN ) >f ( DGYN ) ( Table 1 ) , i . e . two melanomas , Lung_nsc , Liver_riken and Mixed , a significant positive correlation existed between the fraction of mutated DGNN sequences and free energies of base stacking ( Table S7 , Panel A; r2 0 . 10–0 . 71; P-values <0 . 001–0 . 031 ) . The normalized mutation fractions for the combined 7 datasets also displayed significant correlation ( r2 0 . 47; P<0 . 001; P ( α ) 0 . 05 = 1 . 000; Fig . 1C and Table S7 , Panel A; f ( DGNN ) were according to Duke35 and AgilentV2 mappability ) . VIP , the minimum energy required to abstract an electron , is commonly used as a measure of one-electron oxidation reactivity [41] . We modeled the susceptibilities of G-centered DGN double-stranded trimers to oxidation via quantum chemical computations of VIPs . These analyses are expected to compare favorably with data obtained from the computationally more demanding tetramers; the VIPs for tetramers would be expected to be lower than for trimers while maintaining similar sequence-dependent rankings for P2 [53]–[56] . The VIP values for the 12 trimers were ∼30–40% lower than that of an isolated guanine ( Table 2 ) whose VIP estimate was close to the experimentally determined lowest band maximun [57] . The trimer with the lowest VIP was GGG , in agreement with prior calculations [42]–[45] and all trimers containing a GG doublet had lower VIPs than those with a single G . In addition , a purine at the 3′ position was consistently associated with lower VIPs than a pyrimidine at the 3′ position . Thus , DNA sequence context affects VIPs , in accordance with guanine reactivities to oxidative reactions in vitro [42] , [44] . Inspection of the lowest unoccupied beta molecular orbital ( LUBMO ) for each DNA trimer cation , [DGN]+ , in which the ionization state was modeled by removing an electron , showed that the electron hole invariably had π character with high densities at the central guanine ( Fig . 1D ) , or at the 5′G in the GGH ( H = A , C , T ) sequences , consistent with previous work [42] , [55] , [56] , [58] , [59] , implying that P2G was a frequent site for one-electron oxidation reactions . Analyses between f ( DGN ) and VIP values displayed significant correlations for 4/5 datasets that also revealed a correlation with ΔG ( ν ) , i . e . melanomas , Lung_nsc and Liver_riken ( as per Duke35 and AgilentV2 mappability; Figure 1E , Figure S2 and Table S7 , Panel B; r2 0 . 54–0 . 75; P-values<0 . 001–0 . 007 ) . Notably , robust correlation was also evident when the f ( DGN ) data from all 18 cancer datasets were normalized and then computed as average values ( Table S7 , Panel B and Figure S2 , Panel D , r2 0 . 40; P-value 0 . 026; P ( α ) 0 . 05 0 . 615 ) . The regression coefficients obtained using the T_hg19 and T_exons mappability data for the datasets with >2 , 000 SBSs were also used to perform hierarchical clustering based on absolute Manhattan distances ( Fig . 1F ) . At a >90% confidence interval , this yielded three clusters , the largest of which contained the same cancer datasets , with the exception of Ovarian carcinoma , that also displayed f ( DGRN ) >f ( DGYN ) ratios ( Table 1 ) . In summary , both base stacking and VIP data support the conclusion that electron transfer in DNA represents a significant mechanism for sequence context-dependent mutagenesis in cancer . Driver mutations include non-synonymous ( NS ) substitutions that play a key role in cancer initiation and progression . To assess whether bona fide driver mutations also occurred in a sequence context-dependent manner , we examined the NS substitutions that altered the same genomic coordinate in more than one patient sample , and the genes affected ( Text S1 , Figure S3 and Table S8 ) . For the 224 recurrent NS substitutions at G•C bps , we calculated the relative enrichment E for each of the 64 NGNN motifs , a value which is expected to approximate to 1 if the base substitutions are completely independent of flanking sequence . E values were greater for the CGNN than for the DGNN ( D = A/G/T ) sequences ( Figure 2 , Panel A ) . Among the DGNN sequences , P3-purines were associated with significantly more mutations than P3-pyrimidines , a difference that was attributable to the presence of P3-A ( DGAN>DGBN , B = C/G/T ) . This trend remained unaffected after the 45 entries from the two melanoma datasets [which showed f ( DGAN ) >f ( DGBN ) in the respective EWS and GWS screens ( Figure 1 , Panel B ) ] were removed ( E ( DGAN ) = 0 . 92±0 . 52; E ( DGBN ) = 0 . 39±0 . 43; P = 0 . 001 ) . Thus , recurrent NS substitutions occurred preferentially at CpG and GpA dinucleotides in cancer genomes , mirroring the sequence context-dependent pattern of SBSs observed both genome-wide and exome-wide ( Figure 1 and Table 1 ) . Of the 18 codon changes that recurred >12 times , 7/10 affected NGNN sequences and 5/10 occurred at CGNN sequences , all in well-established cancer genes ( Table S9 , Panel A ) . Likewise , the most commonly mutated CGNN ( Table S9 , Panel B ) and DGAN ( Table S9 , Panel C ) motifs affected known driver mutations , alongside several novel candidate genes and driver mutations ( Table S9 ) , including p53R248G , which has been reported to alter protein function ( http://www-p53 . iarc . fr ) , and GRHL3 , WNK3 , EPHB1 , ADCY2 , GSK3B and LRRN3 , which are not currently listed in the cancer gene census ( http://www . sanger . ac . uk/genetics/CGP/Census ) . To examine whether recurrent NS substitutions occurred equally in all tumor tissue types , we determined the relative distributions of the most frequently mutated genomic coordinates after normalizing for both tissue representation and the total number of SBSs per dataset; in the absence of any bias , each tissue would contribute 12 . 5% . The four genes with ≥12 recurrently mutated genomic coordinates ( TP53 , KRAS , PIK3CA and BRAF ) ( Table S9 , Panel A ) were predominantly of breast ( 34% ) , intestine ( 24% ) and lung ( 18% ) origin ( Figure 2 , Panel B ) . The three most commonly mutated CGNN sequences ( CGTC , CGGA and CGTG; S = 9 . 5 , 6 . 0 and 5 . 7 , respectively ) were found in genes mutationally altered in the intestine ( 26% ) , ovary ( 19% ) and breast ( 15% ) , whereas the most commonly mutated DGAN sequences ( TGAT , GGAA and TGAA; S = 2 . 8 , 2 . 0 and 2 . 0 , respectively ) were found predominantly in genes altered in melanoma ( 37% ) , breast and lung ( 17% each ) ( Table S9 ) . By contrast , these mutated motifs were underrepresented in the liver ( ≤2% ) . Of the 64 codons affected , 6 ( 3 in TP53 , 2 in PIK3CA and 1 in GNAS ) are known driver mutations , 4 introduced stop codons into TP53 , and 26 occurred within genes whose involvement in cancer is strongly suspected ( Table S9 and http://www . sanger . ac . uk/genetics/CGP/Census ) . Thus , although high-confidence driver mutations occurred preferentially at CGNN and DGAN motifs , their occurrence between tissues was highly asymmetrical , with DGAN mutations occurring predominantly in tumors of the skin . Finally , we used pathway analyses to survey the 150 recurrently mutated genes ( Figure 2 , Panel C ) . In all tumor tissues , 18 pathways/networks related to cell-cycle checkpoints and the DNA damage response were found to be compromised in all types of tumor , the sole exception being melanomas in which only the MAP kinase signaling pathway was consistently altered . A similar pattern was revealed when all NS substitutions were analyzed , irrespective of whether the data from all patients were merged ( Figure S4 , Panel A ) or plotted separately ( Figure S4 , Panels B and C ) . The highest-ranking pathways were dominated by TP53 mutations in most tumor types ( Figure S4 , Panels B and C ) , with the exception of pancreatic cancers in which KRAS mutations dominated . Both p53 and KRAS proteins are known to act on parallel signaling cascades that regulate TERT , the active reverse transcriptase component of telomerase that controls the stability of chromosome ends ( http://www . biocarta . com/pathfiles/h_telPathway . asp ) . Hence , although critical pathways represent common targets for oncogenic transformation , the altered genes may vary between different patients or organ/tissue types . In summary , a distinction emerged between melanoma and the other types of cancer , both with regard to the sequence contexts targeted by driver mutations , DGAN vs . CGNN sequences , and to the pathways that hosted these mutations , MAP kinase vs . p53-associated signaling pathways . In the HGMD missense/nonsense mutation dataset , approximately 68% of SBSs occurred at G•C bps , a proportion similar to the EWS cancer datasets , although correlations with ΔG ( ν ) or VIPs were absent ( Table S7 , Panel B ) and no enrichment for DGRN sequences was apparent ( Table 1 ) . However , r ( DGNN ) , which measured the fraction of mutated DGNN motifs relative to the direction of transcription , revealed that the P2 position was more likely to contain a guanine on the non-transcribed strand , relative to the transcribed strand , when stacking interactions with neighboring bases were high ( r2 0 . 32; P-value<0 . 001; P ( α ) 0 . 05 0 . 991; Figure 3 , Panel A ) . No such behavior was evident in the cancer datasets ( not shown ) , whereas limited bias was observed in the 1000 Genomes Project dataset ( Figure 3 , Panel A ) . Thus , transcription led to a pattern of sequence context-dependent SBSs among pathogenic germline mutations , which mirrored that observed in several cancer genomes . The HGMD dataset of inherited splicing mutations contained 9 , 907 SBSs that may be assumed to adversely affect RNA processing; 8 , 308 ( 84% ) of these mapped to within 5 bases of donor and acceptor splice junctions ( Figure 3 , Panel B ) . Strikingly , although the canonical GT and AG intronic splice junctions at the donor AG∧GTAAGT and acceptor CAG∧GT sequences were found to be >99 . 9% conserved in the RefSeq dataset of human genes ( Figure S5 top ) , only three positions , i . e . AG∧GTAAGT and CAG∧GT , were frequently mutated ( 1 , 297–2 , 601 SBSs , 65% ) , the T-containing donor position being only modestly affected ( 836 SBSs ) . We also used the 1000 Genomes Project dataset to assess the extent of splicing variation in the general population ( Figure S5 middle ) ; the smallest number of SNVs occurred at all 4 highly conserved positions , as expected . By contrast , in the cancer datasets , the number of SBSs around splice junctions was found to be independent of sequence conservation ( Figure S5 bottom ) . In summary , pathological germline splicing mutations preferentially targeted those positions that exposed a purine base to the non-transcribed strand during DNA transcription .
Large-scale next-generation sequencing projects of cancer genomes are providing an unprecedented opportunity to address the key issue of the nature of the underlying mechanisms of base substitution in tumorigenesis . This issue is generally approached by analyzing the types of base substitution specific to the cancer tissue , i . e . mutation spectra , based on the assumption that different types of base substitution originate via different mutational mechanisms , as assessed by animal model systems [4] , [6] , [8] , [9] , [16]–[18] , [21] , [60]–[63] . We have chosen the alternative approach of addressing the sequence-context dependency of single base substitution , with the expectation of shedding light on the earliest step ( s ) in the process of mutagenesis , i . e . the susceptibility of DNA to base modification . Once modified , a base would then undergo various types of substitution based upon the type of modification it incurred , its interactions with DNA repair and replication systems and , possibly , the tissue of origin [62] . As revealed by correlations with VIPs and absolute free energies of base stacking , we uncovered a direct correlation between electronic coupling along the DNA chain , leading to electron transfer , and sequence-dependent SBSs , both in human cancers and as a cause of inherited disease . Thus , charge transfer appears to be the earliest event in the mutational mechanism acting along the path leading to base substitution in cancer . Electron transfer is the simplest chemical reaction and is known to underlie a number of fundamental biological processes such as cellular respiration and photosynthesis . By establishing the relevance of charge transfer to mutational changes in the DNA molecule , our study enables improved predictions of the relative contribution of individual mutagenic processes and DNA repair activities to cancer ( Donohue et al . , unpublished data ) . The somatic and germline settings studied here are qualitatively and quantitatively different and quite distinct from one another . In the former , very large numbers of somatic mutations occur as a consequence of the disease state whereas in the latter , only one or two germline mutations are generally involved in disease etiology . Despite these fundamental differences , similar sequence context-dependencies are evident , which are explicable in terms of the intrinsic physical properties of DNA , i . e . , free energy of co-axial base stacking and electronic coupling among flanking bases . We propose a model for SBSs that includes one-electron oxidation reactions ( Figure 3 , Panel C ) . In the first step , abstraction of an electron from DNA ( base or sugar ) by a radical species , either endogenous or exogenous , creates an electron hole . In the second step , the electron hole migrates reversibly to various competing sites , including flanking or more distant bases , as well as other molecules and contacting chromatin-associated amino acids , causing in some instances DNA-protein crosslinks [64] . Guanines with the lowest ionization potentials , as determined by neighboring bases , are the strongest hole-attracting sites . The resulting radical cations ( G•+ ) are then expected to undergo a number of chemical modifications , leading to a variety of stably modified bases , including 8-oxoG , a key toxicological lesion , oxazolone , imidazolone and others , some of which can result in base changes during DNA replication if left unrepaired [22] , [65]–[67] . Guanine-protein crosslinks may also lead to SBSs [68] . GpA , which we confirmed to be a key mutation hotspot [6] , [8] , [9] , [21] , [61] , [63] and found to be enriched in sporadically clustered non-synonymous substitutions ( Table S10 ) , would therefore yield mutations through electron transfer [36] , [37] , [42] , [69] , tissue-specific deamination [33] and photoexcitation , leading to cyclobutane pyrimidine dimers ( CPDs ) in melanoma [10] , [11] . We also identified NGRA , and to a lesser extent NGRG , sequences as mutation hotspots specific to melanoma . Attempts to determine whether mutations at NGRA might have been caused by UV-photosensitization or electron transfer , based on mutation spectra analyses ( see Introduction ) , were uninformative since base substitution patterns were heavily sequence-context dependent . For example , in the context of these melanoma datasets , P2-G in the TGTT motifs underwent G→A:G→T substitutions in the relative proportions 17∶75 , whereas for the TGCC motifs this ratio was 81∶13 . Hence , sequence context determines the outcome of single base substitution in a manner that still eludes complete understanding . Nevertheless , if electron transfer reactions were involved , then P4-A would be expected to exert stronger effects than P4-G on mutations at P2-G , since hole trapping is much weaker on adenine than on guanine bases [47] . The CpG dinucleotide was found to be a consistent mutational hotspot , both in cancer and the germline , a result that generalizes the conclusions drawn from previous studies [4] , [9] , [16]–[18] , [63] . The high frequency of C→T transitions at CpG dinucleotides is generally attributed to high rates of deamination of 5-methylcytosine resulting from methylation of CpG sites [9] , [19] , [20] , [62] . However , other mechanisms have been proposed [60] , [62] , such as enhanced susceptibility of methylated CpG sites to damage by physical and chemical genotoxic agents [62] . This latter interpretation would be consistent with our finding that electronic coupling is an important factor in establishing the hierarchy for base modification in DNA . During the course of normalizing mutation fractions by genome mappability , we noted an enrichment of CGNN sequences in Segmental Duplications . Nakken et al . [70] reported a higher density of CpG islands in Segmental Duplications than in unique chromosomal regions , whereas Xie et al . found methylation-associated SNP clusters to be more prevalent in Segmental Duplications than in unique regions [71] . Thus , the prevalence of CGNN-associated SBSs may well be greater than our study indicates . A confounding factor in our analyses is the relatively small number of SBSs , particularly in EWS datasets , which caused large variations in fi values . Indeed , three of the four datasets that displayed high-confidence ( i . e . P<0 . 05 and P ( α ) 0 . 05>0 . 800 ) correlations between SBSs and ΔG ( ν ) or VIPs were obtained from genome-wide studies . Combining all fi values into a single group ( Figure S2 , Panel D ) only alleviates the problem , since the fi values for each dataset are given the same weight . Nevertheless , the ensuing “cautiously significant correlation” is consistent with a role for electronic coupling in cancer-related mutagenesis . A second confounding factor is the multiple roles that the GpA dinucleotide plays in mutagenesis , as eluded to earlier . In the case of melanomas , if the numbers of mutated NGAN ( and NGA ) sequences were dominant , this might cause chance correlation with ΔG ( ν ) or VIP values , when in fact most mutations could arise from CPDs on the complementary strand . Correlations for both the Melanoma_gws and Melanoma_ews datasets remained highly significant ( P<0 . 002; P ( α ) 0 . 05 0 . 920–1 . 000 ) when the fi values for the NGAN ( or NGA ) sequences were excluded from the analyses , thereby confirming a role for charge transfer . This conclusion is further supported by the observation that electronic coupling and photo-induced energy transfer reactions at pyrimidine dimers occur simultaneously and impinge on one another [72]–[74] . In cancer , the subset of mutational changes resulting from NS substitutions that recurred in different patient samples displayed the same enrichment of mutations at CpG and GpA sequences as the exome-wide and genome-wide sequence alterations , supporting the notion of common underlying causes , i . e . cytosine methylation , electron transfer ( this study ) , enzymatic cytosine deamination and CPD formation ( in melanoma ) [10] , [11] , [17] . These commonalities suggest that the mechanisms involved in generating “driver” tumor initiating mutations are likely to be similar to those involved in generating the bulk of subsequent “passenger” mutations . Hodis et al . [75] reached a similar conclusion using a quite different approach . Thus , electron transfer appears to be involved in both the early ( driver mutations ) and late ( passenger mutations ) phases of tumorigenesis , particularly in tissues of epithelial origin . Recurrent NS substitutions were observed predominantly in gene networks associated with p53 function in all tumor types , the exception being melanoma where a preponderance of mutations at GpA segregated with genes of the MAP kinase signaling pathway . The reason for this distinction remains unclear; however , the critical role played by the MAP kinase signaling pathway in melanocyte proliferation in response to UV damage [76] suggests that positive selection may have been a contributory factor . The results of the HGMD data analysis support the occurrence of electron transfer in germline mutagenesis associated with human inherited disease , although sequence context-dependent mutagenesis was evident only when mutations were mapped onto the non-transcribed strands of genes . Guanines modified by oxidative DNA damage are repaired predominantly by base excision repair ( BER ) [77] , [78] . Since oxidative DNA damage occurs more efficiently in single-stranded DNA than in double-stranded DNA [79] , [80] , oxidative guanine lesions may have formed more frequently on the single-stranded , non-transcribed , strand than on the DNA:RNA duplex during transcription . Thus , a greater number of lesions would be expected to escape BER on the non-transcribed strand than on the transcribed strand . In cancer cells , the large number of mutations that generally accumulate during tumor growth could have masked this bias . An alternative or additional possibility is that transcription-coupled nucleotide excision repair , a mechanism that processes bulky DNA adducts and which selectively corrects errors on the transcribed DNA strand [81] , might have contributed to the strand asymmetric mutations observed in the HGMD dataset [9] , [11] , [12] , [82] , [83] . In similar vein , we interpret the selectivity of mutations at purines on the non-transcribed strands of splice junctions as a consequence of oxidative damage , whose effect could have been prolonged by the pausing of transcription-coupled splicing at splice junctions [84] . With the number of sequenced genomes rapidly increasing , it will be of great interest to ascertain whether electron transfer constitutes a general mutational mechanism that is common to all forms of life .
We collected the publicly available data from cancer genome studies reported in PubMed from 2007 through December 2011 [1]–[15] together with the 5 largest datasets available from the International Cancer Genome Consortium ( ICGC ) . The cancer genome datasets varied widely in terms of sequencing strategies , mapping techniques and variant-calling algorithms , implying that the power to detect SBSs may differ depending upon the datasets and methodologies used [85] . However , all studies excluded base variants present in matched-control tissues , such that the reported SBSs were changes attributed to somatic mutations in the tumor tissue . Matched controls were used for all patient samples . On average , between 6 [15] and 1834 [1] tumor-specific SBSs were reported in the EWS studies ( between 1012 [3] and >50 , 000 [8] in the GWS studies ) ( Table 1 ) , which is ∼1–3 orders of magnitude lower than the numbers of non-synonymous and splice-site variants noted on average in whole-exome studies [86] . In addition to normal-tumor matched samples , single nucleotide polymorphisms present in dbSNP databases or in the Venter and Watson genomes [1] , [10] , [11] were also used to exclude common base variants . Differences in variant-calling power were mitigated in our study since we examined relative proportions of mutated sequences , rather than absolute mutation fractions . A second source of variation in detecting SBSs among the cancer genome studies was the sequencing instrument used . Illumina sequencers have been reported to yield systematic base-call errors , especially at the last base of context-specific GGC and GGT sequences , which affect either the forward or reverse strand , and at inverted repeats [87] , [88] . The sequencing technologies employed included Illumina genome analyzers , SOLiD next-generation DNA sequencing , ion semiconductor sequencing , dubbed cPAL ( combinatorial probe-anchor ligation ) nanoballs , capillary electrophoresis , 454 pyrosequencing and mass spectrometry , often used in combination to verify variant calling . Illumina sequencers were the most commonly instruments employed in the studies whose data we used [1] , [3] , [4] , [6] , [7] , [10]–[14] . The frequency of such base-call errors has been estimated at ∼0 . 1–0 . 3% before filtering , and even lower after filtering ( SAMtools ) [87] . Considering that sequencing errors tend to occur over long simple repeat tracts , which have low mappability , and that systematic errors at GGT were ignored ( we analyzed mutations at G•C bps only ) , it seems unlikely that base-call errors have biased our analyses by >0 . 1% , an acceptable limit . Approximately half of the human genome sequence comprises highly homologous repetitive DNA elements ( Alu repeats , LINE elements etc . ) and simple repeats , and an additional ∼3 . 6% contains Segmental Duplications , i . e . segments of >1 kb in length that are present at multiple loci and which share ∼90–98% sequence similarity ( http://genome . ucsc . edu ) . Thus , because only the mappable genome may be scored for mutations , we used various methods to estimate the total number of mappable NGNN sequences to use as denominators in the fi fractions ( see below ) . Three methods were used for the GWS studies: 1 ) the entries with a mappability index of 1 ( representing unique sequences ) from file wgEncodeDukeMapabilityUniqueness35bp . bigWig ( http://hgdownload . soe . ucsc . edu/goldenPath/hg19/database/ ) generated for the ENCODE project by the Duke University Institute for Genome Sciences and Policy ( IGSP ) and at the European Bioinformatics Institute ( EBI ) , which we refer to as Duke35; 2 ) we selected sequences from the mappability file wgEncodeCrgMapabilityAlign50mer . bw ( ( http://hgdownload . soe . ucsc . edu/goldenPath/hg19/database/ ) [89] ( Donohue et al . , unpublished data ) , referred to as CRG50; and 3 ) we retrieved all NGNN sequences in the GRCh37/hg19 release of the human genome assembly ( chromFa . tar . gz file at http://hgdownload . soe . ucsc . edu/goldenPath/hg19/bigZips/ ) ( T_hg19 ) . For the EWS studies , the SureSelect Human All Exon Kit ( http://www . genomics . agilent . com ) was the most common platform reported [2] , [4] , [7] , [13] . A custom RefSeq CCDS PCR primer library was used to generate the Glioblastoma dataset [5] and a set of 1 , 507 genes ( oncogenes , tumor suppressors , “druggable targets” ) were targeted in the Mixed cancer dataset [15] . Hence , three methods were used to estimate the number of mappable NGNN sequences in the EWS studies: i . e . the NGNN counts from 1 ) the file S0293689_Covered . bed ( http://www . Agilent . com ) , listing the coordinates of exons targeted by the SureSelect Human All Exon Kit ( AgilentV2 ) ; 2 ) the RefSeq exons sequences of CGR50 ( CRG50_exon ) ; and 3 ) the total RefSeq exons from file seq_gene . md at ftp://ftp . ncbi . nih . gov/genomes/H_sapiens/mapview/seq_gene . md . gz ( T_exons ) . We defined fi = mi/ti , where mi was the number of mutations at a specific NGNN•NNCN sequence ( henceforth designated as NGNN ) and ti was the total number of that sequence in one of the six “mappable” sets described above . The total number of NGNN sequences was doubled for the self-complementary AGCT , CGCG , GGCC and TGCA sequences since , like all NGCN sequences , they contain two mutations sites , one on the forward and one on the reverse strand . In relation to the counts of mutated NGNN motifs , if the . G . . occurred at the same genomic coordinate more than once within a cancer dataset , or if it was a homozygous mutation , it was considered as one count . Custom shell and FORTRAN scripts were used to obtain the total numbers of mappable NGNN and fi fractions ( see Text S1 for sample scripts ) . The normalized fractions of mutated DGNN sequences were defined as Fi = fi/∑fi , thus , ∑Fi scaled to 1 . N indicates any base ( A/C/G/T ) ; D indicates A/G/T; B indicates C/G/T . As mentioned , sequence designation implies double-stranded DNA ( i . e . AGTC = ( 5′-AGTC-3′ ) • ( 5′-GACT-3′ ) ) . The average base stacking free energies <ΔG ( ν ) > were obtained from Friedman and Honig [51] by using the ΔG ( ν ) ( εi = 2 ) values for the three base steps ( DpG + GpN + NpN ) /3 . The free energy of base stacking ΔG ( ν ) is an estimate of the absolute contribution of base stacking to nucleic acid stability in the absence of hydrogen bonding interactions , and contains a contribution from nonpolar plus electrostatic forces , as assessed from a theoretical approach using the Amber force field and a continuum solvation model of water . The largest contribution to ΔG ( ν ) was found to arise from nonpolar [51] , as opposed to electrostatic , interactions . Nonpolar interactions were contributed for the most part by enhancement in the Lennard-Jones component as a result of close packing , and to a smaller extent from hydrophobic interactions . Thus , the ΔG ( ν ) values follow the same trend as the nonpolar contributions to free energies of base stacking ΔGnp ( ν ) , i . e . purine-purine >> purine-pyrimidine > pyrimidine-purine > pyrimidine-pyrimidine , in qualitative agreement with experimental determinations [51] . The relative enrichment E of sequence i , Ei , was defined as the ratio Di/Ti , where Di = di/∑di , di being the number of times sequence i was mutated at least twice at the same hg19 coordinate and Ti = ti/∑ti , ti being the total number of occurrences of sequence i exome-wide ( T_exons ) . Finally , S = sn/∑sn and sn = tn/cn , tn being the number of times the combined ( top ) sequences were recurrently mutated and cn being the total number of NS substitutions in a particular type of cancer . Three-dimensional structures of the 12 possible double-stranded DGN trinucleotides were constructed using w3DNA [90] . Hydrogen atoms , atomic charges and four neutralizing Na+ counterions were assigned to each sequence according to the amber99 force field [91] , using UCSF CHIMERA [92] . Na+ counterions were positioned next to the four DNA backbone phosphates . Each trinucleotide was energy minimized in vacuo using the 10 , 000 steps steepest descent algorithm and the amber99 force field in GROMACS 4 . 5 . 1 [93] . Ten and 14 Å cutoffs were used for Coulomb and van der Waals interactions , respectively . VIPs were computed using Kohn-Sham density functional theory ( DFT ) [94] employing the Minnesota M06-2× functional [95] , [96] with all-electron 6–31G ( d ) basis sets [97] , [98] , as implemented in the GAMESS electronic structure package [99] , and including backbone phosphate groups and sodium counter ions in addition to the DGN double-stranded bases . The M06-2× functional was used since this method provides accurate descriptions of hydrogen bonding and stacking interactions between base-pairs . We reasoned that the DGN set would provide the same type of information as the computationally more demanding NDGN set . Molecular orbitals were depicted using the MacMolPlt graphics program [100] . For individual patient samples , mutations were collated and sorted into lists of genes carrying mutations using customized R scripts ( http://www . r-project . org/ ) . The gene lists for each sample were entered into our pattern extraction pipeline analysis ( PPEP ) [101] , as implemented in the WPS package [102] , to obtain the ListHit of genes ( number of genes from each list that are annotated to each pathway ) for each of the BioCarta pathways . For each tumor type , each pathway was ranked on the basis of how frequently it was “hit” by individual patient samples and the ranking scores were obtained as the percentages of patient samples that had at least one hit in the corresponding pathway , using customized R scripts . The tumor type ranking scores for each pathway were combined and used to rank the pathways for all tumor types . The highest ranked pathways represent the most “popularly” hit pathways amongst all types of tumors . For each highly ranked pathway , the genes carrying the mutations were retrieved from each patient sample , ranked and displayed as gene-level heatmaps . For the pathway analysis of recurrent NS substitutions , the relevant genes for each tumor type were collated into lists and subjected to PPEP analysis , as described above . Agglomerative hierarchical clustering dendrograms [103] were built using either the regression coefficients , r , between the fractions of mutated DGN sequences , f ( DGN ) , and the VIP values , or the absolute orthogonal distances ( Manhattan distances ) between each f ( NGNN ) data point for all datasets . All-to-all comparisons were performed , allowing the relative estimation of all components of the systems , including the reference VIP branch . | A large number of DNA mutations identified in cells from patients with cancer or human inherited disease were analyzed to address a fundamental issue in human pathology , viz , the mutational mechanisms that cause irreversible changes to DNA . By using bioinformatics and computational methods , we found that mutations do not occur randomly , but instead affect specific bases , most often guanines flanked by other guanines or adenines . We attribute this effect to electron transfer , a chemical reaction known to underlie basic biological processes such as cellular respiration and photosynthesis . Certain types of carcinogens , oxidants or radiation can interact with DNA and abstract an electron . Our results imply that the ensuing sites of electron loss can migrate from their original position in the DNA to neighboring guanines where they become trapped , leading to further chemical modifications that may eventually result in mutations . Many of the mutations known to be important for tumor growth ( driver mutations ) , as well as passenger mutations and mutations associated with inherited disease , appear to be caused by electron transfer . Beyond pathological mutations , electron transfer may represent a universal mechanism by which genetic changes occur in all life forms to drive population fitness over evolutionary time . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Guanine Holes Are Prominent Targets for Mutation in Cancer and Inherited Disease |
Type 2 diabetes mellitus ( T2DM ) is a disorder characterized by both insulin resistance and impaired insulin secretion . Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues . Identification of the molecular mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network . In this study we integrate skeletal muscle gene expression datasets with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM . These features include reporter metabolites—metabolites with significant collective transcriptional response in the associated enzyme-coding genes , and transcription factors with significant enrichment of binding sites in the promoter regions of these genes . In addition to metabolites from TCA cycle , oxidative phosphorylation , and lipid metabolism ( known to be associated with T2DM ) , we identified several reporter metabolites representing novel biomarker candidates . For example , the highly connected metabolites NAD+/NADH and ATP/ADP were also identified as reporter metabolites that are potentially contributing to the widespread gene expression changes observed in T2DM . An algorithm based on the analysis of the promoter regions of the genes associated with reporter metabolites revealed a transcription factor regulatory network connecting several parts of metabolism . The identified transcription factors include members of the CREB , NRF1 and PPAR family , among others , and represent regulatory targets for further experimental analysis . Overall , our results provide a holistic picture of key metabolic and regulatory nodes potentially involved in the pathogenesis of T2DM .
Metabolic phenotypes at a cellular level are essentially characterized by concentrations of metabolites and fluxes through the reactions that make up the metabolic network . Fluxes , in turn , are dependent on metabolite levels , enzyme activities , abundance of effectors and possibly other variables . Measurement of fluxes and metabolite concentrations at the entire metabolic network-scale is , however , a difficult task in humans due to a variety of technological and experimental limitations . By contrast , methods for measurement of expression of genes encoding metabolic enzymes are relatively well-established . Thus , the primary goal of this study is to use informatics approaches to integrate available gene expression data with metabolic networks , in order to predict metabolic phenotypes of skeletal muscle linked to the pathogenesis of type 2 diabetes . Such an approach will help not only to gain insight into the organization of transcriptional regulation in human tissues , but also provide guidance for improved design of experimental strategies for obtaining metabolite and flux data , which can be further integrated into metabolic models . To achieve these goals , we applied an extension of the algorithm described in [14] ( for various applications of this algorithm see [14]–[18] ) , which enables identification of so-called reporter metabolites , or metabolic hot spots around which transcriptional regulation is centered ( Figure 1A ) . This analysis is based on the assumption that under most conditions of physiological interest , fluxes through enzymes connected to a metabolite are coordinated in order to maintain physiological homeostasis , or to eventually reach a new ( pseudo- ) steady state . Moreover , transcriptional regulation of expression of genes encoding critical enzymes in metabolic flux pathways facilitates concordance with the metabolic demands of the cell and corresponding stoichiometric and thermodynamic constraints on fluxes . For this analysis , we used two recently published human metabolic network models: i ) Homo sapiens Recon1 [19] , and ii ) Edinburgh Human Metabolic Network ( EHMN ) [20] . We further hypothesized that the observed coordinated changes around reporter metabolites can be , at least in some cases , attributed to common transcriptional regulatory mechanisms . Specifically , we hypothesize that the neighbor enzymes of reporter metabolites may share one or more transcription factor binding sites in the promoter regions of the corresponding genes . In order to identify such potential regulatory players , we tested promoter sequences of the genes associated with the reporter metabolites for enrichment of known transcription factor binding motifs ( Figure 1B ) . Transcription factors identified in this fashion provide clues to the regulatory mechanisms that lead to observed gene expression changes in the metabolic network . Since our goal is to identify reporter metabolites and transcription factors potentially involved in diabetes pathogenesis and progression , we analyzed two independent studies of skeletal muscle transcriptomics in individuals with established type 2 diabetes or insulin resistance [8] , [9] ( Text S1 ) . In the first study [8] , biopsies were obtained following insulin stimulation from a cohort of 43 Swedish men of Caucasian ethnicity with a spectrum of glucose tolerance , including 17 with normal glucose tolerance ( NGT ) , 8 with impaired glucose tolerance ( IGT ) , and 18 with established T2DM . The second dataset [9] was derived from a cohort of 15 subjects of Mexican American ethnicity , in whom muscle biopsies were performed in the fasting state . Importantly , this cohort included individuals with not only established diabetes ( 5 subjects , T2DM ) , but also individuals with completely normal glucose tolerance but a spectrum of insulin resistance; normal glucose tolerant subjects were subdivided by family history-linked diabetes risk ( 4 family history positive , more insulin resistant subjects , FH+; and 6 family history negative , more insulin sensitive subjects , FH− ) . With this approach , the individual contributions of isolated insulin resistance and diabetes risk ( in the setting of normoglycemia , FH+ ) , mild elevations in postprandial glucose ( IGT ) , and established diabetes can be individually assessed . Moreover , the possible contribution of family history , potentially mediated by genetics or shared environment , can be assessed . Thus , we predict that analysis of the common patterns resulting from the two datasets will identify regulatory signatures potentially independent of study cohort and design variation but common to the pathophysiology of insulin resistance and diabetes .
In order to link the identified reporter metabolites to regulatory pathways controlling gene expression , we hypothesized that enzymes associated with reporter metabolites would be regulated by common transcription factors . As potential candidates subjected to such regulation , we selected all reporter metabolites with at least 5 up- or down-regulated neighboring genes ( Materials and Methods ) . Up- and down-regulated gene sets were then analyzed separately in order to assess whether their promoter regions were enriched for known transcription factor binding sequence motifs . P-values for enrichment were estimated by using a hypergeometric test , which compared the proportion of promoters from a given gene set containing a particular motif with the frequency of occurrence of that motif in promoter regions of all other metabolic genes . Correction for multiple-testing was done by using q-value [29] and motifs with q-value≤0 . 05 were considered as significantly enriched . In accord with our hypothesis , several transcription factor binding sites were overrepresented in the promoter regions of the enzymes associated with reporter metabolites . A summary of the main results from this analysis is illustrated in Figure 3A . Many transcription factors were found to be common across the two case studies ( Figure 3B ) , albeit in connection with different reporter metabolites . PPAR family motifs ( PPARγ and PPARα:RXRα ) were enriched in seven downregulated enzyme sets including ATP . Tax/CREB motifs were enriched in promoters of downregulated enzymes associated with ATP , ADP and phosphate . Additional down-regulated neighbors of ATP were enriched for the binding sites of NF-κB , MEF-2 , UF1-H3β , Pax-9 and NKX6 . 2 , while the NRF-1 motif was enriched in the set of up-regulated enzymes neighboring ADP . Another potential regulatory signature was identified around the down-regulated neighbors of phosphatidylinositol and phosphatidylinositol 4 , 5-bisphospate ( important phospholipids which participate in insulin and other signaling reactions ) , which were significantly enriched for binding sites of p53 , PPARγ , SRF , SEF-1 , v-Jun , GCNF , AR and many others ( Table S7 ) . These and other highly connected reporter metabolites in the metabolite-TF network ( Figure 3A ) demonstrate the concept that associated metabolic pathways can be transcriptionally regulated in multiple ways in response to environmental stimuli or metabolic perturbation .
The identification of reporter metabolites from glycolysis and energy-generation pathways suggests that there may be regulation of certain physiological parameters , such as glucose uptake , at the transcriptional level of the corresponding metabolic pathways . To investigate the extent of such possible regulation , we calculated Pearson correlation coefficients between insulin sensitivity ( as measured by either whole-body glucose uptake during the hyperinsulinemic euglycemic clamp or insulin levels achieved during the OGTT ) and mean centroid expression levels of genes surrounding reporter metabolites ( Swedish dataset ) ( Materials and methods ) . A significant linear correlation with whole-body glucose uptake was observed for several reporter metabolites . In most cases , the correlation was significant only for one of the conditions ( NGT , IGT or T2DM ) . For example , significant correlation of transcriptional regulation around dUDP with glucose uptake was found only for NGT samples ( Figure 5A ) . It appears that this potential connection is de-linked under IGT and T2DM conditions . Another example is 1-Phosphatidyl-1D-myo-inositol 3-phosphate ( Figure 5B ) , where significant correlation is observed with insulin level only for IGT . Further investigation of the causal mechanisms behind these observed correlation patterns may help in elucidating the regulatory role of the reporter metabolites in diabetes pathogenesis . A key scientific and clinical challenge is to identify molecular markers of diabetes risk , not only to better understand disease pathophysiology , but also to develop novel therapies for prevention and treatment of established diabetes . In this context , it is interesting that our analysis identified both PPARγ and its potential lipid ligands as regulatory molecules , since PPARγ ligand thiazolidinediones are currently employed as effective therapy for diabetes . We hypothesize that some transcriptional pathways identified in the current analysis , including CREB , NRF-1 and SRF , may be additional novel molecular mediators of the transcriptomic phenotype associated with insulin resistance , and thus potential targets for future intervention strategies . Of course , the potential roles of these pathways will require additional testing in cultured cells and animal models , where their impact on metabolic flux and insulin sensitivity can be fully assessed . Similarly , reporter metabolites identified in our analysis represent molecules likely to be involved in human skeletal muscle insulin resistance phenoytpes and also novel candidate biomarkers of insulin resistance and diabetes risk . In support of this hypothesis , several of the identified metabolites have known physiological roles in T2DM ( Table S8 and Discussion above ) . Additional molecules have been analyzed either in rodents and/or in other tissues ( Table S8 ) and thus , their appearance as reporter metabolites also strongly implicates their involvement in insulin resistance in human skeletal muscle . Some of the novel metabolites identified in our analysis , including glycolytic and fatty acid oxidation intermediates , are known targets of metformin , a compound effective for diabetes therapy and prevention ( Figure 4 ) . We also identified an interesting link between DAG , a reporter metabolite for T2DM , and the CDP-choline branch of the Kennedy pathway of phospholipid metabolism ( Figure 4 ) . This pathway has been implicated in cancer development and is being established as anti-tumor drug target [56] , [57] . Changes in phospholipid metabolism are known to affect the properties of cellular membranes , and subsequently signaling through membrane proteins . Further investigation of the role of phospholipids in T2DM pathogenesis may provide clues to some of the missing links that connect metabolic flux changes with insulin signaling in skeletal muscle cells . Supplementary tables S1 , S2 , S3 , S4 list additional reporter metabolites which are , to our knowledge , not ( directly ) linked with any of the known metabolic players in T2DM . Our analysis nevertheless suggests them as potential nodes of disruption or as biomarkers . Measurement of the intramyocellular concentration of the reporter metabolites in patients with diabetes risk may help to confirm the role of these metabolites in insulin resistance . A particularly interesting finding from our analysis is the identification of highly connected metabolites as reporters , including ATP/ADP and NAD+/NADH . We hypothesize that diverse environmental and genetic risk factors result in insulin resistance when individuals are unable to mediate appropriate compensatory transcriptional and metabolic responses in other parts of the network connected by these hubs . Our results also suggest that alterations in gene expression linked to the highly connected co-factors are likely to be acquired features of established T2DM . Analysis of the transcriptional activity of CREB in the context of ATP concentrations and TCA cycle activity in skeletal muscle may help to elucidate regulatory mechanisms leading to these changes . Reconstructed human metabolic network models are still evolving , incomplete , and subject to error . Well-annotated pathways such as central carbon metabolism are thereby likely to be over-represented in the reporter analysis . In order to partially compensate for this limitation , we used two reconstructions – Recon1 and EHMN . As network reconstructions will become more complete , it will be possible to better assess the extent of this limitation . Another essential input to our algorithm , in addition to metabolic network , is gene expression data for the genes represented in the network . We would like to note that neither EHMN nor Recon1 network genes were fully represented by the microarray chips used in the two case studies ( Text S1 ) . Only 54% and 39% genes from the Recon1 and EHMN , respectively , were represented on the chips used in Mexican-American case study , while this coverage was 85% and 60% in Swedish case study . Interestingly , re-analysis of the Swedish Male dataset by using only a subset of genes from the HG-U133A chip that were represented also on the HuGeneFL ( used in Mexican-American case study ) showed a large overlap between the two reporter metabolite sets thus obtained ( 86% for T2DM vs NGT comparison and 69% for the rest ) . The details of this analysis , together with relevant statistical considerations , can be found in Text S1 . Although the present analysis identified common metabolic and regulatory signatures across the two studies , there are several differences in the study designs , and therefore the results must be regarded with certain caution . In addition to relatively low number of subjects in Mexican-American study , the differences include fasting state biopsies in Mexican-American study vs post insulin stimulation biopsies in Swedish study . Furthermore , the age and BMI ( Body Mass Index ) of the individuals participating in the two studies were different and may contribute to the differences in the observed gene expression patterns . To our knowledge , these two case studies represent the only human skeletal muscle transcriptome datasets that were available at the time of here reported computational analysis . Analysis of new datasets which may become available in the future will be useful in obtaining further insight into molecular physiology of skeletal muscle in the context of T2DM . Moreover , emergence of better or new gene expression analysis tools will help to cover parts of metabolic network that are currently inaccessible due to the lack of data . Extension of the analysis to discover more global regulatory patterns by using additional bio-molecular interaction data [58] such as protein-DNA and protein-protein interactions will definitely be an important step in obtaining a higher resolution picture of T2DM metabolic phenotypes . Availability of such interaction data at the high confidence level of metabolic interactions is the current major bottleneck . Another essential extension of the methodology will require the use of thermodynamic data for metabolic reactions [59]–[61] . Moreover , since mRNA levels do not necessarily correlate with the protein levels , incorporation of the proteomics data together with the thermodynamic data will allow more accurate interpretation of the reporter metabolites in terms of implications for flux and concentration changes . We demonstrate the use of a network-guided data integration approach to discover key , physiologically relevant metabolic and regulatory nodes in T2DM pathogenesis . The methodology does not require the use of a priori disease-specific knowledge regarding the involvement of specific pathways or metabolites , thereby making it a robust and unbiased analytical framework for studying diseases linked to perturbations in the cellular metabolic network . Our results identify the highly connected metabolites ATP and NAD+ as reporters and potential mediators of the widespread changes in gene expression linked to insulin resistance in muscle . Moreover , our results extend previous knowledge about T2DM pathogenesis at the gene expression level – by reporting additional potential sites of disruption , e . g . , TCA cycle and Kennedy pathway of phospholipid metabolism . Several metabolites from other pathways were also found to display significant differential gene expression of the genes around them and we suggest putative regulatory mechanisms behind these alterations . Our results suggest a framework of metabolic disruption observed with insulin resistance and diabetes , which can be used to test the role of specific pathways in mediating disease pathophysiology , and more practically , for the identification of potential biomarkers for preventive and therapeutic monitoring .
Two datasets used in the study were obtained from the Diabetes Genome Anatomy Project website ( http://www . diabetesgenome . org ) . Brief comparison of microarray platforms from the experimental studies [8] , [9] used in the current work is presented in the Text S1 . Promoter sequences for all genes were obtained from the Ensembl Biomart ( http://www . ensembl . org/biomart ) . The transcriptional start sites ( TSSs ) were identified based on the annotation of the Ensembl Biomart sequences . Sequences in the −800 to 200 base pairs region of the TSS were deemed as promoter regions for the analysis . Interspersed repeats and low complexity DNA sequences were masked out . Two reconstructions of human metabolic network , viz . , Recon1 [19] and EHMN [62] were used in this study . The Homo Sapiens Recon1 is a comprehensive literature-based metabolic network reconstruction that accounts for the functions of 1496 ORFs , 2004 proteins , 2766 metabolites and 3311 metabolic and transport reactions . The ENMN ( Edinburgh Human Metabolic Model ) is a network reconstructed by integrating genome annotation information from different databases and metabolic reaction information from the literature . The network contains nearly 3000 metabolic reactions , which were reorganized into about 70 human-specific pathways according to their functional relationships . The two models mainly differ in the coverage of reactions and in the accounting of compartmentalization and inter-organelle transport reactions . Preprocessing of the gene expression data was carried out by using the statistical software environment – R ( www . r-project . org ) . The probe intensities were obtained and corrected for background by using robust multi-array average method ( RMA ) [63] with only perfect-match ( PM ) probes . Normalization was performed by using the quantiles algorithm . Gene expression values were calculated from the PM probes with the median polish summarization method [63] . All data preprocessing methods were used by invoking them through the affy package [64] by using rma function . Significance of the differential expression was calculated by using the empirical Bayes test [65] . The probe-sets were grouped into genes , and to each gene the differential expression was defined by choosing the value from the top level probe-set ( using the probe-set rank defined by Affymetrix ) . In case of more than one probe-set present at the top level , the median value was used . Each metabolite in the metabolic network was scored based on the scores of its k neighbor enzymes ( i . e . enzymes catalyzing reactions involving that metabolite , either as a substrate or as a product ) . Each enzyme was assigned with a p-value for differential expression based on the p-value of the gene encoding for that enzyme . In case of isozymes and enzyme-complexes , genes with most significant expression change were used to score the enzyme ( Figure 1 ) . P-values of genes pi , indicating the significance of differential expression , were converted to Z-scores Zi by using the inverse normal cumulative distribution function ( CDF ) ( ) : . All metabolite nodes were assigned a Z-score , Zmetabolite , calculated as aggregated Z scores of the k neighbor enzymes: . Zmetabolite scores were then corrected for the background distribution by subtracting the mean ( μk ) and dividing by the standard deviation ( σk ) of the aggregated Z scores derived by sampling 10000 sets of k enzymes from the network: . Corrected Z-scores were then transformed to p-values by using CDF . Metabolites with p-values less than 0 . 05 were deemed as reporter metabolites . Detailed information on the reporter scoring can be found in the Text S1 and [14] . For all reporter metabolites , we assessed enrichment of known protein-binding sequence motifs in the promoter regions ( −800 to 200 base pairs relative to the transcription start site ) of the corresponding neighbor genes . In order to obtain robust results , we only considered sets consisting of at least 5 up- or down-regulated genes . For each reporter metabolite , the sequences of all enzyme neighbors were used as the positive sequence set , whereas all other enzymes in the network model were used as the negative ( background ) set . Known motifs were identified by using position frequency matrices of all known motifs stored in the TRANSFAC database [66] . The motif enrichment analysis tool ASAP [67] was used to scan all TRANSFAC motif matrices against the positive sequence sets of each reporter metabolite . The negative sequence sets were used together with 2nd order background model . A one-tailed Fisher's exact test was used to assess per-sequence over-representation of any known motif , and the threshold used to calculate significance for each TRANSFAC matrix was set to 70% of the highest-scoring sequence motif . The q-value cut-off criteria [29] was used as a post-data measure of statistical significance of all motifs found to be significantly enriched . | Type 2 diabetes mellitus is a complex metabolic disease recognized as one of the main threats to human health in the 21st century . Recent studies of gene expression levels in human tissue samples have indicated that multiple metabolic pathways are dysregulated in diabetes and in individuals at risk for diabetes; which of these are primary , or central to disease pathogenesis , remains a key question . Cellular metabolic networks are highly interconnected and often tightly regulated; any perturbations at a single node can thus rapidly diffuse to the rest of the network . Such complexity presents a considerable challenge in pinpointing key molecular mechanisms and biomarkers associated with insulin resistance and type 2 diabetes . In this study , we address this problem by using a methodology that integrates gene expression data with the human cellular metabolic network . We demonstrate our approach by analyzing gene expression patterns in skeletal muscle . The analysis identified transcription factors and metabolites that represent potential targets for therapeutic agents and future clinical diagnostics for type 2 diabetes and impaired glucose metabolism . In a broader perspective , the study provides a framework for analysis of gene expression datasets from complex diseases in the context of changes in cellular metabolism . | [
"Abstract",
"Introduction",
"Results",
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] | 2010 | Metabolic Network Topology Reveals Transcriptional Regulatory Signatures of Type 2 Diabetes |
Intestinal protozoan infections are confirmed as major causes of diarrhea , particularly in children , and represent a significant , but often neglected , threat to public health . No recent data were available in Lebanon concerning the molecular epidemiology of protozoan infections in children , a vulnerable population at high risk of infection . In order to improve our understanding of the epidemiology of intestinal pathogenic protozoa , a cross-sectional study was conducted in a general pediatric population including both symptomatic and asymptomatic subjects . After obtaining informed consent from the parents or legal guardians , stool samples were collected in January 2013 from 249 children in 2 schools in Tripoli , Lebanon . Information obtained from a standard questionnaire included demographic characteristics , current symptoms , socioeconomic status , source of drinking water , and personal hygiene habits . After fecal examination by both microscopy and molecular tools , the overall prevalence of parasitic infections was recorded as 85% . Blastocystis spp . presented the highest infection rate ( 63% ) , followed by Dientamoeba fragilis ( 60 . 6% ) , Giardia duodenalis ( 28 . 5% ) and Cryptosporidium spp . ( 10 . 4% ) . PCR was also performed to identify species and genotypes of Cryptosporidium , subtypes of Blastocystis , and assemblages of Giardia . Statistical analysis using a logistic regression model showed that contact with family members presenting gastrointestinal disorders was the primary risk factor for transmission of these protozoa . This is the first study performed in Lebanon reporting the prevalence and the clinical and molecular epidemiological data associated with intestinal protozoan infections among schoolchildren in Tripoli . A high prevalence of protozoan parasites was found , with Blastocystis spp . being the most predominant protozoans . Although only 50% of children reported digestive symptoms , asymptomatic infection was observed , and these children may act as unidentified carriers . This survey provides necessary information for designing prevention and control strategies to reduce the burden of these protozoan infections , especially in children .
Parasitic infections , and in particular those caused by protozoa , are a major public health problem worldwide . They are among the most widespread human infections in developing countries , with children being the most vulnerable population [1] . In particular , intestinal protozoans , such as Cryptosporidium spp . and Giardia duodenalis ( syn . G . intestinalis and G . lamblia ) , are major causes of diarrhea in children . Transmission of these protozoa is through the oral-fecal route following direct or indirect contact with the infectious stages , including human-to-human , zoonotic , waterborne , and foodborne transmission of both parasites [2] , and airborne transmission for Cryptosporidium only [2 , 3] . Additionally , recent data from the Global Enteric Multicenter Study ( GEMS ) on the burden and etiology of childhood diarrhea in developing countries has shown that the apicomplexan protists Cryptosporidium spp . are nowadays one of the leading causes of moderate to severe diarrhea in children aged under 2 years [4 , 5] . In addition , Giardia duodenalis infects approximately 200 million individuals worldwide , and is particularly common among schoolchildren and in daycare centers [6] . In children under 5 years , G . duodenalis infection may produce severe acute diarrhea . Several studies have also suggested that long-term growth retardation can be a consequence of chronic Giardiasis [7] . Because of their significant public health and socioeconomic implications , both parasites Cryptosporidium spp . and G . duodenalis were included in the WHO’s “Neglected disease initiative” in 2004 [8] . Other parasites , such as Blastocystis spp . and Dientamoeba fragilis , are cosmopolitan protozoans found in the gastrointestinal tract of humans . Nevertheless , the exact contribution of Blastocystis spp . and D . fragilis to pathogenicity has been controversial . The prevalence of Blastocystis spp . in humans varies , from 0 . 5%–24% in industrialized countries to 30%–76% in developing countries [9] . Recently , a Blastocystis spp . prevalence of 100% was found in a Senegalese population of children , being the highest prevalence ever reported worldwide for this parasite [10] . All cases were caused by subtypes ( STs ) 1 , 2 , 3 and 4 , with a predominance of ST3 . The prevalence of D . fragilis ranges from 1% to 52% , according to different geographic regions [11] . Recent studies support the pathogenic nature of both parasites . More than half of the children infected by Blastocystis spp . in Senegal presented various gastrointestinal disorders [10] , and it is now accepted that the classic clinical features of infection with this parasite include gastrointestinal symptoms such as nausea , anorexia , flatulence , and acute or chronic diarrhea [12] . An association of Blastocystis spp . with irritable bowel syndrome ( IBS ) [13] and extraintestinal manifestations , such as urticaria , has also been suggested [14] . Moreover , invasive and inflammatory potential of the parasite has been reported [15] . Regarding D . fragilis , infection can be acute or chronic , and symptomatic patients exhibit abdominal pain , persistent diarrhea , loss of appetite , weight loss and flatulence , as well as IBS-like symptoms [16] . Symptoms are observed in 20–58% of infected cases . It has been proposed that D . fragilis could be a heterogeneous species , with variants having similar morphology but different virulence [17] . In Lebanon , as in other developing countries , intestinal parasitic infections remain responsible for significant morbidity [18 , 19] . A previous Lebanese study based on microscopic analysis comparing findings for intestinal parasite prevalence at a major tertiary care center between 1997–1998 and 2007–2008 reported the following prevalences: 0% for Blastocystis spp . , 0 . 1% for Cryptosporidium spp . and 16% for G . duodenalis in the first period , versus 17% for Blastocystis spp . , 0% for Cryptosporidium spp . and 6% for G . duodenalis in the second period [20] . Recently , concerning Blastocystis spp . and Cryptosporidium spp . , a prevalence of 19% and 11% respectively , was reported among hospitalized patients after molecular analysis of stool samples [21 , 22] . Concerning D . fragilis , no epidemiological data are available to our knowledge . In addition , little information is available in this country on the potential risk factors associated with these protozoan infections in children . Therefore , the aim of this study was to identify potential risk factors for transmission and to collect molecular epidemiological data on the prevalence and genetic diversity of Cryptosporidium spp . , G . duodenalis , Blastocystis spp . and D . fragilis in a population of children attending two schools of different socioeconomic levels in Tripoli , Lebanon .
The authorization to conduct this study was obtained from the Lebanese Minister of Public Health ( reference number 4–39716 ) . Written informed consents were obtained from the parents or legal guardians of the children , after a clear explanation of the research objectives . This study was conducted in accordance with the Code of Ethics of the World Medical Association ( Declaration of Helsinki ) . A standard questionnaire was completed by interviewing the child’s parents or legal guardians , who had given informed consent , in order to obtain a socioeconomic and demographic description including the age , gender , education , residence , occupation and estimated monthly income of the parents , behavioral habits ( intake of fruits , vegetables and fast food ) , health conditions , presence of symptoms ( i . e . abdominal pain , diarrhea , vomiting , fever , nausea , headache and discomfort ) , family members with gastrointestinal disorders , history of previous hospitalizations and medical treatments . Environmental conditions , such as type of water supply , sewage disposal system and presence of domestic animals , were also investigated . This cross-sectional study was conducted in Tripoli ( latitude 34° 26' 12 N , longitude 35° 50' 58 E ) , the largest city in northern Lebanon , and the second largest city in the country in terms of demographic and economic importance . The city , situated 85 kilometers ( 53 miles ) north of the capital Beirut , has a Mediterranean climate with mild winters and moderately hot summers . Tripoli’s population is estimated at 500 , 000 people . Stool samples were collected in 2 nearly schools of different socioeconomic status in Tripoli ( Al Zahra’ School and Jil Alwa’ed School ) ( Fig 1 ) from two hundred and forty-nine children ( 149 boys and 100 girls aged between 3 and 16 years ) in January 2013 . The sample size corresponded to the total number of samples that could be collected for logistical reasons during a specific period of time . The participants were categorized into three groups according to age: under 5 years , between 5 and 9 years and over 9 years , and into two groups according to socioeconomic status: low socioeconomic status ( LSES ) and high socioeconomic status ( HSES ) . The measure of SES was based on the income , education and occupation of the parents . One fresh stool sample per child was collected in a sterile container and transported immediately to the Department of Microbiology of the AZM Center in Tripoli . All stool samples were examined macroscopically , and their characteristics , such as color , consistency , presence of blood , and presence of helminths were recorded . These specimens were also examined by direct-light microscopy ( DLM ) of wet mounts . For the detection of Cryptosporidium spp . oocysts , modified Ziehl-Neelsen ( MZN ) staining was performed [23] , and the slides were examined at 1 , 000× magnification . For quality control , all examinations were repeated twice by two experienced microscopists . No information was available about potential viral or bacterial infections in these stool samples . All stool specimens were used for molecular detection of Blastocystis spp . , Cryptosporidium spp . , D . fragilis and G . duodenalis . DNA was extracted from approximately 250 mg of stool samples using the QIAmp DNA Stool Mini Kit ( Qiagen GmbH , Hilden , Germany ) , according to the manufacturer’s recommended procedures . The DNA was eluted in 100 μl of elution buffer ( Qiagen ) and stored at −20°C until use . The 18S rRNA detection was performed by nested PCR for Cryptosporidium spp . [24] and by real-time PCR for Blastocystis spp . [25] , D . fragilis [26] and G . duodenalis [27] , as previously described . To further identify Giardia assemblages , the triose-phosphate isomerase ( TPI ) gene was amplified by nested PCR as previously described [28] . Blastocystis spp . , Cryptosporidium spp . and G . duodenalis-positive PCR products were purified and directly sequenced on both strands by Genoscreen ( Lille , France ) or Beckman Coulter Genomics ( Essex , United Kingdom ) . The sequences obtained were aligned using the BioEdit v7 . 0 . 1 package ( http://www . mbio . ncsu . edu/BioEdit/bioedit . html ) , then compared with gene sequences of these parasites available from the NCBI server ( http://www . ncbi . nlm . nih . gov/BLAST/ ) , using the basic local alignment search tool ( BLAST ) . Blastocystis spp . STs were identified by determining the exact match or closest similarity against all known STs , according to the updated classification of Alfellani et al . [29] . Specimens genotyped as C . parvum or C . hominis were further subtyped using nested PCR in order to amplify a fragment of the 60 kDa glycoprotein ( gp60 ) gene , as described previously [30] . The amplified DNA fragments were purified and sequenced on both strands , then analyzed by alignment of gp60 sequences with reference sequences retrieved from GenBank using the ClustalX program ( http://www . clustal . org/ ) . C . parvum and C . hominis gp60 subtypes were named by counting the number of trinucleotide repeats of TCA ( A ) , TCG ( G ) , and TCT ( T ) , and the ACATCA repeat ( R ) after the trinucleotide repeats [31] . All sequences were uploaded to NCBI GenBank ( accession numbers KU311720-KU311975 ) . Statistical analyses were performed using Stata software , version 13 ( StataCorp , College Station , TX , US ) . The tests were two-sided , with a type I error set at α = 0 . 05 . Quantitative data was presented as the mean ± standard deviation or the median [interquartile range] . The categorical data was presented as frequency and associated proportions . The differences across groups were compared using ( 1 ) the Student’s t-test or Mann-Whitney U-test when the conditions of the t-test were not met for continuous variables ( assumption of normality studied using the Shapiro-Wilk test and homoscedasticity by the Fisher-Snedecor test ) , and ( 2 ) the chi-squared test or Fisher’s exact test for categorical parameters . Logistic regression models were created to calculate the odds ratios ( OR ) and 95% confidence interval considering parasite infections as the main outcome . Analyses were based on parasite detection using molecular tools .
A total of 249 schoolchildren ( 149 male , 100 female ) were included in this study . Among them , 157 belonged to the LSES group ( mostly children from the Al-Zahra’ School ) and the remaining 92 to the HSES group ( mostly children from the Jil Alwa’ed School ) . The age of the participants was between 3 and 16 years ( mean age: 10 . 3 ± 2 . 7 ) ( Table 1 ) . Overall , based on PCR and light microscopy examination , 85% ( 212/249 ) of the children were found to be positive for at least one intestinal parasitic infection . Out of a total of 212 infected schoolchildren , the distribution of parasitic infections in males and females was 61% ( 129/212 ) and 39% ( 83/212 ) , respectively . When socioeconomic status was considered , the prevalence was as follows: 65% ( 138/212 ) of children in the LSES group and 35% ( 74/212 ) in the HSES group . No significant statistical differences regarding parasitic infections related to gender or socioeconomic status were observed . The demographic characteristics of the study population are shown in Table 1 . After molecular analysis of the samples , Blastocystis spp . had the highest infection rate ( 63% ) , followed by D . fragilis ( 60 . 6% ) , G . duodenalis ( 28 . 5% ) and Cryptosporidium spp . ( 10 . 4% ) . As expected , the prevalence of these protozoans was lower in microscopic examination of wet mounts ( 51 . 6% , 0% , 14 . 4% , and 5 . 6% respectively ) . Other intestinal parasites were also detected by DLM , as follows: Entamoeba histolytica/dispar ( 5 . 6% ) , Entamoeba coli ( 2 . 4% ) , Ascaris lumbricoides ( 0 . 4% ) , and Hymenolepis nana ( 0 . 4% ) . Mixed infections with two parasites were found in 35 . 7% of children ( 89/249 ) . The most common dual infection was with Blastocystis spp . and D . fragilis , with a prevalence of 68 . 5% ( 61/89 ) . In addition , 11 . 6% ( 29/249 ) of children exhibited triple parasitic infections with Blastocystis spp . , D . fragilis and G . duodenalis . Other cases of mixed infections are shown in Fig 2 . In total , 125 out of 249 children had symptoms at the time of the survey . Among parasitized children , gastrointestinal symptoms were common ( 55% ) . Abdominal pain , diarrhea , vomiting , and fever were reported in 51% ( 108/212 ) , 28% ( 60/212 ) , 11% ( 23/212 ) , and 6% ( 12/212 ) of children , respectively . Of the total of 157 Blastocystis spp . , 151 D . fragilis , 71 G . duodenalis and 26 Cryptosporidium spp . -infected children , 45% , 47% , 69% , and 27% respectively , were asymptomatic . A logistic regression model was created to identify the risk factors for transmission of these intestinal parasitic infections . The overall presence of abdominal pain ( OR: 5 . 4 , CI: 2 . 1–13 . 4 , P<0 . 001 ) and diarrhea ( OR: 4 . 5 , CI: 1 . 3–15 . 1 , P: 0 . 009 ) , and having members of the same household with gastrointestinal symptoms ( OR: 9 . 6 , CI: 2 . 2–40 . 9 , P<0 . 001 ) were significantly predictive of the risk of intestinal parasitic infections in children . Distribution of protozoan infections among children according to risk factors is shown in Table 2 . Univariate logistic regression analysis showed the presence of abdominal pain ( OR: 1 . 9 , CI: 1 . 1–3 . 2 , P: 0 . 02 ) and contact with parents having gastrointestinal symptoms ( OR: 1 . 9 , CI: 1 . 0–3 . 4 , P: 0 . 03 ) to be the main factors significantly associated with Blastocystis spp . infection . In the group composed of 151 D . fragilis-infected children , univariate logistic regression analysis showed that contact with members of the same household having gastrointestinal symptoms ( OR: 2 . 2 , CI: 1 . 2–3 . 9 P: 0 . 01 ) was the only risk factor associated with the presence of this parasite ( Table 2 ) . D . fragilis-infected children were 4 times more likely to be infected with Blastocystis spp . ( OR: 3 . 6 CI: 2 . 1–6 . 3 , P<0 . 001 ) . The logistic regression analysis found significant associations between G . duodenalis infection and eating raw vegetables and fruits ( OR: 2 . 7 , CI: 1 . 2–6 . 2 , P: 0 . 01 ) , contact with members of the same household having gastrointestinal symptoms ( OR: 4 . 9 , CI: 2 . 7–8 . 9 , P <0 . 001 ) , and presence of gastrointestinal symptoms ( OR:4 . 3 , CI: 2 . 8–8 . 0 , P <0 . 001 ) , such as abdominal pain ( OR:4 . 7 , CI:2 . 6–8 . 5 , P <0 . 001 ) and diarrhea ( OR:2 . 4 , CI:1 . 3–4 . 4 , P: 0 . 004 ) . On the other hand , HSES ( OR: 0 . 3 , CI: 0 . 2–0 . 6 , P<0 . 001 ) , eating outside of the home ( OR = 0 . 3 , CI: 0 . 1–0 . 7 , P: 0 . 003 ) , and drinking treated water ( OR: 0 . 3 , CI: 0 . 1–07 , P: 0 . 003 ) were protective factors against G . duodenalis infection ( Table 2 ) . The univariate logistic regression analysis showed that children aged under 5 years had a 6 times higher risk of Cryptosporidium spp . infection compared with older children ( OR: 6 . 4 , CI: 1 . 9–21 . 3 , P: 0 . 006 ) . Eating outside of the home ( OR: 2 . 4 , CI: 1 . 1–5 . 6 , P: 0 . 04 ) and presence of gastrointestinal symptoms ( OR: 3 . 1 , CI: 1 . 2–7 . 6 , P: 0 . 01 ) , especially diarrhea ( OR: 4 . 1 , CI: 1 . 8–9 . 5 , P <0 . 001 ) or fever ( OR: 6 . 4 , CI: 1 . 9–21 . 3 , P: 0 . 006 ) , were other factors significantly associated with this infection ( Table 2 ) . The real-time PCR products of the 157 samples positive for Blastocystis spp . were all sequenced on both strands . With 99% to 100% sequence identity to the reference sequences , 138 isolates corresponded to single infections by one ST , and 3 different STs were identified as follows: ST3 ( 46 . 3% of isolates ) , ST2 ( 28 . 3% ) and ST1 ( 25 . 4% ) . For the remaining 19 samples , sequence chromatogram analysis revealed the presence of double traces , suggesting mixed infection by different STs that were not identified . In addition , the PCR products of the 26 samples positive for Cryptosporidium spp . were successfully sequenced on both strands . Among them , 20 isolates ( 77% ) were identified as C . hominis , while 6 isolates ( 23% ) were identified as C . parvum , all with more than 99% sequence identity to homologous sequences . Cryptosporidium spp . other than C . parvum and C . hominis were not found . Sequence analysis of the gp60 gene identified the C . hominis isolates as belonging to two subtypes: IaA18R3 ( 4/20 ) and IbA10G2 ( 16/20 ) . All of the C . parvum isolates were identified as the IIaA15G1R1 subtype . The Giardia assemblage was successfully determined by sequencing of the TPI gene from 67 of the 71 isolates previously identified by 18 rRNA PCR . DNA sequencing of the TPI gene failed for the 4 others samples . Assemblage B was found in the majority of the samples ( 64/67 ) , followed by assemblage A ( 2/67 ) and a mixed-assemblage infection ( 1/67 ) .
This study demonstrates that protozoan parasitic infections are very common among a community of children living in Tripoli , independent of their socioeconomic status . Such a prevalence is high , considering that the study was performed in an urban area and relied on the collection of a single stool sample per child , instead of the ideal three consecutive samples . A recent study among schoolchildren primarily in rural Malaysia reported a prevalence of parasitic infections of 98% [32] . The most frequent intestinal parasites detected were Blastocystis spp . and D . fragilis , followed by G . duodenalis and Cryptosporidium spp . These 4 protozoans were detected by molecular tools , which are advantageous due to their high sensitivity and specificity . DLM was performed in order to detect co-infection with additional parasites such as helminths , which were identified with a lower prevalence . Although microscopic detection of helminths is widely used as a diagnostic method , microscopy is not very sensitive when infections are light , especially in asymptomatic persons . In addition , specific techniques for the diagnosis of certain nematodes such as Enterobius vermicularis were not used . In the present study , 63% of children were found to be infected with Blastocystis spp . after molecular identification . In a previous survey of our group , a lower prevalence of 19% was found in a population of Lebanese symptomatic and asymptomatic patients after microscopic examination of stools [22] . Today , Blastocystis spp . is considered an under-reported parasite , with a worldwide distribution and a prevalence far exceeding that of other intestinal parasites in the human population [33 , 34] . Indeed , its prevalence can reach 100% in developing countries and has been reported at between 1 . 5% and 20% in industrialized countries [10 , 33] . The current prevalence of Blastocystis spp . among schoolchildren was high , as observed in other countries such as Senegal ( 100% ) [7] , Egypt ( 33% ) [35] , Syria ( 28% ) [36] , the USA ( 23% ) [37] , and Pakistan ( 17% ) [38] , even if detection methods in these studies are not the same . Using PCR tools , the prevalence of D . fragilis reached 61% . A previous study using microscopic techniques reported a prevalence of 38% of D . fragilis in adult workers in the food sector , in the same geographic area of Lebanon [19] . In addition , in our study , we found a significant association between Blastocystis spp . and D . fragilis co-infection in children ( P<0 . 001 ) . An association between these two protozoans has recently been reported in children presenting gastrointestinal symptoms in the Netherlands [39] and in asymptomatic people in two poor communities in Brazil [40] . G . duodenalis is one of the most common causes of waterborne disease outbreaks associated with drinking water [41 , 42] . The prevalence found in our study ( 29% ) is considerably higher than that in other Middle Eastern countries with similar standards of living or in European countries ( e . g . Italy , Germany , the UK , Portugal ) [43] . In addition , the current prevalence of giardiasis in Lebanon is six times higher than that observed in 2004 ( 5% ) [18] . Nevertheless , the higher sensitivity of molecular tools for the detection of this parasite could likely explain this difference . Even if diagnostic tools were different , recent studies in asymptomatic children around the world reported giardiasis prevalence of 1% in the USA [37] , 1% in Italy [44] , 1% in the United Kingdom [45] , 2% in Germany [46] , 7% in Portugal [47] , 7% in Pakistan [38] , 15% in Syria [36] , 16% in Spain [48] , 18% in Yemen [49] , 32% in Russia [50] , and 57% in Cuba [51] . Regarding Cryptosporidium spp . , this apicomplexan protozoan is one of the most common intestinal parasitic pathogens in the world [52] . Cryptosporidiosis rates are higher in children and immunocompromised patients than in the healthy adult population [53] . However , cryptosporidiosis prevalence varies in different countries: between 1% and 5% in children with diarrhea in developed countries , reaching 49% in developing countries [53 , 54 , 55] . Although varying in technical diagnostic tools , the prevalence that we found in children in Lebanon ( 10% ) was in the same range as that observed in Yemen ( 10% ) [31] , but lower than that found in others Middle Eastern countries such as Jordan ( 19% ) [56] and Egypt ( 49% ) [55] . Our results based on conventional microscopy showed that infection with E . histolytica/dispar is prevalent in Lebanon at the present time . Previous studies among presumably older healthy subjects in 2004 reported a prevalence of 2% [57] . It is also more prevalent than in other Middle Eastern countries , such as Syria ( 0 . 01% ) [36] , Qatar ( 0 . 3% ) [58] and Iran ( 0 . 4–2% ) [59 , 60] , and in other developed [37] and developing countries [61] . Nevertheless , the parasite is less common than in other developing countries like Pakistan ( 14% ) [38] , Yemen ( 17% ) [49] , and India ( 18% ) [62] . In a recent study to assess the prevalence and genetic diversity of E . histolytica in individuals with gastrointestinal symptoms in a rural area of southern Ethiopia , a prevalence of 3 . 3% was found [63] . The fact that we did not use PCR to detect this parasite strongly suggests that the actual prevalence of these enteric species is likely to be an underestimate . In a case-control study investigating the prevalence of Cryptosporidium spp . , E . histolytica and G . duodenalis among children < 2 years of age , with and without diarrhea , in Dar es Salaam , Tanzania , an overall high prevalence of these parasites was observed . Cryptosporidium spp . infection was more commonly found among young Tanzanian children with diarrhea and G . duodenalis infection was frequently asymptomatic [64] . Concerning the high prevalence of co-infections of pathogenic and nonpathogenic parasites , our results are comparable to those of other studies [65 , 66] . The observed polyparasitism could be explained by shared risk factors for parasite infection , such as poor sanitation and hygiene behavior and the fact that the transmission route of these parasites is mainly through the fecal-oral pathway [66] . In total , 125 children out of 249 had symptoms at the time of the survey . In relation to the main clinical features of infections , it was found , as expected , that diarrhea was significantly common among G . duodenalis and Cryptosporidium spp . -infected children , but no significant association with this symptom was observed regarding Blastocystis spp . or D . fragilis infections . The interactions and confounding effects that are not evident in a simple comparison of the two groups could also explain the absence of significant associations . Nevertheless , a positive association regarding Blastocystis spp . and abdominal pain suggests a pathogenic role for this parasite of controversial clinical significance [67] . Even if children harboring D . fragilis presented more gastrointestinal symptoms , no significant association was found between this parasite and gastrointestinal disorders in children . Recent studies described that D . fragilis has struggled to gain recognition as a pathogen , despite the evidence supporting its pathogenic nature [68] . Interestingly , the 124 other children were asymptomatic for protozoan infection and may be carriers responsible for transmission . Consistently , a study among Spanish children attending day care facilities showed that both G . duodenalis and Cryptosporidium spp . infections were asymptomatic in 82% of cases [48] . Concerning the risk factors for protozoan infections , our data analysis found that protozoan parasites could infect both genders in all age groups . However , an age of less than 5 years was significantly associated only with Cryptosporidium spp . infection . The reason for this high prevalence is likely due to the immature immunity of young children exposed to this opportunistic parasite [69] . As reported by other authors , no association was found between either gender or age and prevalence of G . duodenalis infection [47] . It is not yet fully understood why age plays a role in the frequency of Cryptosporidium spp . infection , but is not associated with the frequency of giardiasis [70] . Intestinal parasites are usually considered poverty-related diseases [71] . However , no significant association was identified between socioeconomic status and the overall rate of parasitic infections in our study population . Nevertheless , the prevalence of G . duodenalis was significantly higher in LSES infected children . Interestingly , in a previous study conducted in Peru , Giardia spp . and microsporidia were the predominant intestinal parasites among the poorest population , and infections with Cryptosporidium spp . were independent of wealth [70] . Furthermore , in our study , only LSES children were infected with helminths ( Ascaris lumbricoides and Hymenolepis nana ) . In addition , children who drank untreated water had a 3 times higher risk of infection with G . duodenalis than those who drank treated water ( P: 0 . 003 ) . Two meta-analyses , including 84 studies in 28 countries , concluded that the quantity of water available to the population in developing countries has more impact on endemic diarrhea cases than water purity itself [72 , 73] . For the study population in Lebanon , the accessibility of the water supply was not a problem . However , a majority of households did not have a proper sanitary system , favoring fecal contamination via ground seepage , as previously described [74] . The findings of the present study showed that children who had contact with family members presenting gastrointestinal symptoms had a higher risk of infection with these parasites , confirming the direct human-to-human transmission of these protozoans . Thus , the screening and treatment of family members of infected children should be considered for the prevention and control of these infections . Additionally , indirect transmission through contaminated food ( raw vegetables and fruits ) was found to be a risk factor for giardiasis . In fact , this association is likely due to the fact that fresh vegetables and fruits may be eaten without washing them or with contaminated hands , and it is well known that contaminated hands can play a major role in fecal-oral transmitted diseases [44] . On the other hand , meals outside of the home were significantly associated with Cryptosporidium spp . infection . The genotyping/subtyping of Blastocystis spp . , Cryptosporidium spp . and G . duodenalis isolates allows an elucidation of the transmission of these parasites . The majority of Blastocystis spp . -positive samples included in this study represented monoinfections ( 88% ) by one ST . Among these positive isolates , three STs were detected as follows: ST3 was the most abundant , followed by ST2 and ST1 ( 35/138 ) . Our previous study in the Lebanese population also identified the same three STs , with a predominance of ST3 and ST2 [22] . The majority of human Blastocystis spp . infections around the world are attributed to ST3 isolates , followed by ST1 and ST2 , which is consistent with spread directly from person to person [75] . Interestingly , ST4 was not found in our study . Overall , this ST is common in Europe , but much less frequent in Lebanon as well as in Middle Eastern , African , American and Asian countries [75] . In our cohort of schoolchildren , molecular characterization of Cryptosporidium spp . isolates allowed the identification of C . parvum and C . hominis , with a predominance of the latter species . It is well known that human cryptosporidiosis is mainly caused by these two species , with C . parvum considered a zoonotic species while C . hominis has been mainly associated with anthroponotic transmission [52] . Consistently , a potential secondary transmission of infection among family members was significantly associated with this infection . These results are consistent with our recent study describing the predominance of C . hominis in Lebanese hospitalized patients [21] . However , we found different subtypes than those reported in the previous study from our group [21] . Two subtypes belonging to the subtype families Ia and Ib , IaA18R3 and IbA10G2 , were identified . The subtype IdA19 , which has been described as the predominant subtype in Lebanese hospitalized patients [21] , was not found in schoolchildren . The subtype family IbA10G2 has been commonly reported around the world , and is the predominant cause of waterborne outbreaks due to C . hominis [76] . However , IaA18R3 is a rare subtype recently reported in India and Spain [77 , 78] . All subtyped C . parvum isolates were identified as the IIaA15G1R1 subtype . This zoonotic subtype has been reported in both humans and animals in many geographic areas of the world [79] . Moreover , the C . parvum IIa subtype family has a high genetic diversity , and is responsible for the majority of cryptosporidiosis outbreaks due to C . parvum [76] . However , the IIc and IId subtype families , which are reported mostly in developing countries , had not been described in Lebanon [55 , 56 , 80 , 81] . Molecular characterization of G . duodenalis isolates according to TPI sequence analysis allowed the identification of assemblages A and B with a large predominance of assemblage B ( 97% ) . Both assemblages have been described as zoonotic . However , assemblage B seems to be more human specific [43] . Our results are consistent with other studies among children in other countries such as Brazil , Nepal , and Iran reporting a predominance of assemblage B [82] . Additionally , the association between assemblage occurrence and the age of patients showing higher risk of assemblage B infection in children under 12 years old has been described [83] . To our knowledge , this is the first study reporting epidemiological data on intestinal protozoan infections among schoolchildren in Lebanon , independent of socioeconomic status . Our results showed a high prevalence of protozoan parasites among this population , Blastocystis spp . being the most predominant protozoan . In addition , although 50% of children reported symptoms , many of them were asymptomatic , and these children could serve as unidentified carriers . Contact with family members with gastrointestinal disorders was found to be the main risk factor associated with the presence of protozoan infections . The role of person-to-person contact in the specific transmission of Blastocystis spp . and Cryptosporidium spp . isolates was consistent with the results of subtyping . The findings of this study provide useful information for the design of prevention strategies , and interventions in target communities at risk . | Intestinal parasites can infect the gastrointestinal tract of humans . Means of exposure include ingestion of contaminated fruits and vegetables , consumption of infected water and personal contact . Protozoa are considered one of the major groups of parasites . Children are particularly susceptible to infection by these microorganisms , and when they are infected , diarrhea can be the main clinical manifestation . In developing countries , people are at particular risk of infection . However , intestinal parasites , and in particular protozoans , have been taken into account only in a few epidemiological studies . Thus , we conducted an investigation to determine the prevalence , risk factors , and epidemiological information associated with 4 intestinal protozoan infections: Cryptosporidium , Giardia , Blastocystis and Dientamoeba , among children attending two schools of Tripoli , Lebanon . A high prevalence of protozoan parasites was found . Although only 50% of children reported digestive symptoms , asymptomatic infection was observed very often , suggesting that these children may act as unknown carriers . In addition , we found that personal contact plays an important role as a risk factor associated with protozoan infection . This epidemiological survey shows the burden of parasitic infections in Lebanese children and provides necessary information to public health authorities for creating prevention and control strategies . | [
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] | 2016 | Prevalence and Risk Factors for Intestinal Protozoan Infections with Cryptosporidium, Giardia, Blastocystis and Dientamoeba among Schoolchildren in Tripoli, Lebanon |
Dengue viruses ( DENVs ) and Japanese encephalitis virus ( JEV ) have significant cross-reactivity in serological assays; the clinical implications of this remain undefined . An improved understanding of whether and how JEV immunity modulates the clinical outcome of DENV infection is important as large-scale DENV vaccine trials will commence in areas where JEV is co-endemic and/or JEV immunization is routine . The association between preexisting JEV neutralizing antibodies ( NAbs ) and the clinical severity of DENV infection was evaluated in a prospective school-based cohort in Thailand that captured asymptomatic , non-hospitalized , and hospitalized DENV infections . Covariates considered included age , baseline DENV antibody status , school of attendance , epidemic year , and infecting DENV serotype . 942 children experienced at least one DENV infection between 1998 and 2002 , out of 3 , 687 children who were enrolled for at least one full year . In crude analysis , the presence of JEV NAbs was associated with an increased occurrence of symptomatic versus asymptomatic infection ( odds ratio [OR] = 1 . 55 , 95% CI: 1 . 08–2 . 23 ) but not hospitalized illness or dengue hemorrhagic fever ( DHF ) . The association was strongest in children with negative DENV serology ( DENV-naive ) ( OR = 2 . 75 , 95% CI: 1 . 12–6 . 72 ) , for whom the presence of JEV NAbs was also associated with a symptomatic illness of longer duration ( 5 . 4 days for JEV NAb+ versus 2 . 6 days for JEV NAb- , p = 0 . 048 ) . JEV NAbs were associated with increased DHF in younger children with multitypic DENV NAb profiles ( OR = 4 . 05 , 95% CI: 1 . 18 to 13 . 87 ) . Among those with JEV NAbs , the association with symptomatic illness did not vary by antibody titer . The prior existence of JEV NAbs was associated with an increased probability of symptomatic as compared to asymptomatic DENV illness . These findings are in contrast to previous studies suggesting an attenuating effect of heterologous flavivirus immunity on DENV disease severity .
The dengue viruses ( DENV ) and Japanese encephalitis virus ( JEV ) are closely-related members of the virus family Flaviviridae . DENV and JEV co-circulate in the Indian subcontinent and in Southeast Asia , where they are important causes of human disease and mortality . The co-occurrence of JEV and DENV has been documented in Thailand since 1969 , when severe epidemics of each were observed in the Chiang Mai valley region [1] . There is no licensed DENV vaccine and vector control efforts have been largely ineffective in containing transmission . While inactivated and live-attenuated JEV vaccines are licensed for use in humans , vaccination does not interrupt the primary JEV transmission cycle involving pigs , waterfowl , and Culicine mosquitoes [2] . Despite reported high levels of JEV vaccination ( estimated to be 84% in 1998 and 98% in 2008 ) , infections continue to be detected in Thailand each year [3] . JEV and DENV exhibit significant serological cross-reactivity , which can complicate assessment of the relative burdens of each in co-endemic areas and their possible interactions [4] , [5] . There exists limited , inconclusive evidence regarding the clinical implications of prior JEV exposure or JEV vaccination and the severity of subsequent DENV infection . Using observed interactions between DENV serotypes as an analogy , JEV/DENV cross-reactive immunity may possibly be protective [6] , detrimental [7] , or inconsequential . Hoke et al . reported that recipients of inactivated JEV vaccine ( JEVAX ) experienced a non-significant decrease in the occurrence of DHF relative to placebo during the first two years after vaccination and that , among DHF patients , vaccinees experienced milder disease [8] . There was no evidence of an association between JEV vaccination and the occurrence of DHF in a study of hospitalized DENV patients in Bangkok [9] . There has been no reported increase in adverse events following live-attenuated DENV vaccination of JEV-immune volunteers [10] , [11] . However , DENV vaccine recipients have demonstrated heightened and broadened DENV antibody responses and antibody responses of longer duration in the setting of preexisting heterologous flavivirus immunity [12]–[14] . Two human studies have provided evidence of a possible protective effect of the opposite sequence; i . e . , DENV exposure followed by JEV infection . Burke et al found decreased clinical severity in JEV-infected hospitalized patients with higher levels of flavivirus-cross-reactive IgG in Thailand , presumed to be attributable to prior DENV infection [15] . Hammon observed that following the eradication of DENV from Guam in 1945 , a subsequent large JEV epidemic in 1947 caused illness in those who were less likely to have been exposed to DENV previously , namely young children and adult expatriates[16] , [17] . Animal and in vitro studies of cross-reactivity between various combinations of flaviviruses suggest that the nature of the interactions need not be bidirectional and that the influence of a given virus may vary by serotype and even strain [18] . There exists ample evidence that DENV infection may be enhanced in vitro with heterotypic DENV antibodies [19] and also with antibodies to non-DENV flaviviruses , including JEV [20] . However , a study using sera from JEV-immune Thai individuals found no evidence of enhancement of DENV-2 infection in vitro [21] . Animal studies have suggested a protective role of DENV immunity upon JEV challenge in mice [22] and a protective role of WNV immunity upon subsequent challenge with another member of the JEV antigen complex in a variety of animal models [23]–[26] . In summary , there has been evidence of both protective and detrimental interactions between heterologous flaviviruses; the mechanisms and epidemiological implications of these associations remain unclear . Potential interactions between flaviviruses are important for public health because wild-type JEV continues to co-circulate with DENV in Southeast Asia , the area with the highest burden of DENV illness , and JEV vaccination coverage in this region is high . As DENV vaccines advance toward licensure and implementation in co-endemic regions , an improved understanding of what constitutes protective immunity with DENV exposure is necessary . Given ambiguous findings from prior studies , we examined how preexisting JEV immunity influenced the clinical severity of subsequent DENV infection using data from a prospective school-based cohort study in Thailand [27] , [28] . The availability of information on asymptomatic DENV infections as well as outpatient and hospitalized dengue cases provided a unique opportunity to assess these interactions .
The prospective cohort study that generated these data was co-administered by the University of Massachusetts and the Armed Forces Research Institute of Medical Sciences ( AFRIMS ) for the years 1998–2002 . TE , AR , DL , and AN were involved in the design and conduct of the cohort study , for which all subjects provided written informed consent . ST and RG are among the lead scientists presently managing the Department of Virology at AFRIMS , where specimens and data from this study are maintained . The lead author , KA , used only preexisting demographic and laboratory data for the purposes of this retrospective analysis . The cohort study was approved by the Human Use Review and Regulatory Agency of the Office of the Army Surgeon General , the Institutional Review Board of the University of the Massachusetts Medical School , and the Ethical Review Board of the Ministry of Public Health , Thailand . Secondary data analysis for the purpose of this publication was approved by the Institutional Review Board at Emory University . Data were collected during a five-year , school-based , prospective cohort study for DENV infections in children in Northern Thailand . The study design and methods have been described previously [27] , [28] . Briefly , the study was conducted in Kamphaeng Phet Province from 1998–2002 . In January 1998 , 2 , 214 children were recruited from grades 1 through 5 at twelve primary schools . New participants were enrolled from the 1st grade class in January of each year and eligible participants were re-enrolled . The numbers of children enrolled at the start of the active surveillance period each year were 2 , 044 in 1998; 1 , 915 in 1999; 2 , 203 in 2000; 2 , 011 in 2001; and 1 , 759 in 2002 . A total of 3 , 687 children were enrolled for at least one year during the study period , with an average of 2 . 9 years of follow-up per child . At enrollment , the children or their parents were asked about prior JEV vaccination and age of vaccination . Serum samples were collected for dengue serology four times each year ( January , June , August , and November ) . All serological and virological testing was performed at AFRIMS in Bangkok , Thailand . Active case surveillance of the participants was conducted from June 1 to November 1 , with potential illnesses identified based on absence from school , visit to a school nurse or public health clinic , or admission to the hospital . Absent students were visited by village health workers and evaluated with a symptom questionnaire and an oral temperature . Acute blood samples were obtained for students with a history of fever within 7 days of fever onset , as were 14-day convalescent samples . Acute and convalescent blood specimens from incident febrile illnesses were tested using immunoglobulin M ( IgM ) and G ( IgG ) enzyme immunoassays for DENV and JEV . Acute DENV infections were defined serologically as a DENV-specific IgM level ≥ 40 units and with DENV-IgM > JEV-IgM . The infecting DENV serotype was identified from acute blood specimens using serotype-specific reverse-transcriptase polymerase chain reaction ( RT-PCR ) or virus isolation . Symptomatic infections were defined as a documented history of febrile illness with virologic or serologic evidence of acute DENV infection . Charts of hospitalized children were independently reviewed and classified as DF or DHF and assigned a severity grade following WHO criteria [29] . If a child experienced a febrile DENV illness but did not meet the criteria for DHF , they were characterized as having DF . The duration of illness was derived from home visit data and hospitalization records . For non-hospitalized illnesses , the duration of illness was the length of time from the date of school absence or clinic visit to the date of the last home visit at which the child exhibited symptoms consistent with DENV infection . For hospitalized illnesses , the duration of illness was the pre-hospitalization time plus the time in the hospital . Routine specimens were tested for the presence of hemagglutination inhibiting ( HI ) antibodies against DENV-1 – DENV-4 and JEV using standard methods [30] . The reference virus strains were DENV-1 ( Hawaii ) , DENV-2 ( New Guinea ‘C’ ) , DENV-3 ( H87 ) , DENV-4 ( H241 ) , and JEV ( Nakayama ) . Asymptomatic DENV infections were defined as a four-fold or greater rise in hemagglutination inhibition ( HI ) titers for any of the four DENV serotypes between two consecutive routine serum samples and without a concurrent four-fold rise in JEV HI titers , or with a concurrent four-fold rise in JEV HI titers but with higher HI titers for any DENV serotype than for JEV . Plaque reduction neutralization titers ( PRNTs ) were obtained for pre-infection samples using standard methods [31] . Briefly , LLC-MK2 cell monolayers were infected with DENV1 – DENV4 and JEV in the presence of serial dilutions of heat-inactivated patient plasma . The reference virus strains were: DENV-1 ( 16007 ) , DENV-2 ( 16681 ) , DENV-3 ( 16562 ) , DENV-4 ( 1036 ) , and JEV ( Nakayama ) , for which the sources and passage histories have been previously described [32] . The lowest dilution of serum tested was 1∶10 ( corresponding to a final dilution of 1∶20 when combined with an equal volume of DENV ) ; the dilutions and titers reported herein refer to the initial serum dilution . The concentration of patient plasma that resulted in a 50% reduction in plaque formation was calculated using log probit regression . The reciprocal titer of this dilution was defined as the PRNT50 . A PRNT50 <10 was defined as undetectable or ‘negative’ and a titer ≥10 as ‘positive . ’ PRNT assays were performed for asymptomatic infections only if the child had not missed school during the observation period . This was done in an attempt to exclude missed symptomatic DENV illnesses that could have been misclassified as asymptomatic seroconversions . Children were grouped into three categories of DENV immunity based upon their pre-infection DENV antibody profiles: DENV-naïve ( pre-infection PRNT50<10 for all DENV serotypes ) , DENV-monotypic ( pre-infection PRNT50 ≥10 for a single DENV serotype ) , and DENV-multitypic ( pre-infection PRNT50 ≥10 for two or more DENV serotypes ) . To attempt to discern cross-reactive immunity from serotype-specific immunity , we further stratified DENV-multitypics according to mean age of infection , with the logic that older children would have had more time to experience multiple DENV infections and generate multiple serotype-specific responses , while younger children with multitypic profiles would , on average , reflect more cross-reactivity . Preexisting JEV immunity was dichotomized as JEV-positive ( pre-infection PRNT50≥10 ) and JEV-negative ( pre-infection PRNT50<10 ) . By definition , acute phase blood samples were not available from children with asymptomatic DENV infections for direct detection of the infecting virus . Approximately one-quarter of symptomatically infected individuals were RT-PCR negative , likely because the acute specimen was drawn when the child was no longer viremic . Based upon evidence that DENV transmission in this community is highly clustered , we assumed that the serotypes detected among RT-PCR positive cases attending a given school during a given epidemic year were representative of the serotypes causing asymptomatic and RT-PCR negative symptomatic infections at that school [33] , [34] . To minimize the possible biases inherent to this assumption , imputation of an individual's unknown infecting serotype using community-level data was performed only for children residing in communities that had a single serotype detected during that epidemic year . If more than one serotype was detected at a given school during a given year , the infecting serotype was left as missing . The JEV vaccine used in Thailand and throughout Southeast Asia , JEVAX , is a formalin-inactivated , mouse brain-derived JEV vaccine . The first dose of JEVAX in Thailand is administered concurrently with the diphtheria/tetanus/pertussis vaccine at 18 months of age . A second dose is administered 1 to 4 weeks later and a third ( booster ) dose is administered at 30 to 36 months of age . The seroconversion rates with JEVAX in Thai children have been estimated to be 50% following primary vaccination , 64–80% following secondary vaccination , and 100% following receipt of the third dose [35] . Based upon these observations of suboptimal seroconversion rates with the two dose regimen , the third dose was added to the Expanded Program for Immunization ( EPI ) regimen in Thailand in 2000 . A Phase III trial of JEVAX was conducted in 1984 in Kamphaeng Phet , Thailand , which demonstrated favorable efficacy [8] . JEVAX began to be slowly incorporated into the EPI in Thailand in 1988 and became part of Kamphaeng Phet's EPI in 1992 . Vaccination histories for the cohort study were obtained by self-report and subsequent verification with clinic or family documents was not possible . However , given the ages of the children enrolled ( five to fourteen years of age ) and the timing of the study ( 1998 to 2002 ) , it is likely that the majority of children enrolled in the cohort study would have received two doses of JEV vaccine . Younger children would likely have been vaccinated as infants , as part of the EPI program , and older children would likely have been vaccinated during the large “catch-up" vaccination programs taking place in the province in the 1990s . The JEVAX vaccine strain in use during the 1990s in Thailand was the Nakayama strain , the country has since switched to the Beijing strain . Bivariate analyses were performed using chi-square testing for categorical variables and ANOVA or nonparametric testing for continuous variables . Logistic regression models for symptomatic versus asymptomatic infection were constructed using SAS' GENMOD procedure , accounting for the clustering of observations by school . The best model was then chosen by backward regression . Analyses were performed using SAS software , version 8 ( SAS Institute , Cary , NC ) , SPSS for Windows version 10 . 0 ( SPSS Inc . , Chicago , IL ) , and R version 2 . 10 . 1 ( R Foundation for Statistical Computing , Vienna , Austria ) .
Children with JEV NAbs were more likely to experience symptomatic infection than children without JEV NAbs ( 57% versus 46% , p = 0 . 021 by χ2 testing , Table 1 ) . Of the 569 first detected DENV infections , 479 had pre-infection DENV and JEV neutralizing antibody titer information available for analysis ( 84 . 2% ) . 90% of the missing NAb data were associated with asymptomatic infections , for reasons described above . There were no differences by age , gender , or school in the proportions of infections that were symptomatic . The proportion of infections that were symptomatic varied by year , from 26% in 2000 to 51% in 2001 ( p = 0 . 032 , by χ2 testing across all strata of epidemic year ) . There were no differences in the proportion symptomatic by pre-infection DENV antibody status . There was no difference in the proportion symptomatic by reported JEV vaccination history and , among those reporting a history of vaccination , no difference in the time between vaccination and the first detected infection ( mean±SD 5 . 01±2 . 22 years for asymptomatics , 4 . 93±2 . 11 for symptomatics ) . Children developing DF were most likely to be JEV NAb positive , those experiencing asymptomatic infection the least ( 50% versus 39% , p = 0 . 017 , by χ2 testing across all strata of infection severities ) ( Table 2 ) . There were no differences in the proportions of children that were JEV NAb positive by age or gender . The proportion JEV NAb positive varied by school and year . DENV-naives were most likely to be JEV NAb positive ( 54% ) , then DENV-multitypics ( 47% ) , then DENV-monotypics ( 26% ) ( p = 0 . 001 by χ2 testing ) . Those reporting a history of JEV vaccination were more likely to be JEV NAb positive ( 46% versus 31% p = 0 . 012 by χ2 testing ) . Among those reporting a history of vaccination , there was no difference in time since vaccination ( mean±SD 5 . 01±2 . 27 years for JEV NAb positives , 4 . 96±2 . 19 for JEV NAb negatives ) . JEV NAb positivity was associated with an increase in the odds of symptomatic infection in unadjusted analysis ( OR = 1 . 55 , 95% CI: 1 . 08 to 2 . 23 ) ( Table 3 ) . There were no significant differences in the occurrence of hospitalized illness or the occurrence of DHF . Individuals with JEV NAbs were more likely to experience symptomatic DENV infection for all strata of preexisting DENV immunity , though this association was strongest and significant only for DENV-naives ( OR = 2 . 75 , 95% CI: 1 . 12 to 6 . 72 ) ( Figure 1a ) . The association was non-significant and weakest for children with DENV-monotypic immunity prior to infection . Younger and older children with DENV-multitypic immunity had approximately the same increased odds of symptomatic infection with JEV NAbs; neither association was significant . The directions of the associations between JEV NAbs and hospitalized illness ( Figure 1b ) and JEV NAbs and DHF ( Figure 1c ) were not consistent across strata of preexisting DENV immunity and the associations were largely non-significant . The association between JEV NAbs and DHF was significant only for younger DENV-multitypics , who had increased odds of DHF with JEV NAbs ( OR = 4 . 05 , 95% CI: 1 . 18 to 13 . 87 ) . The presence of JEV NAbs was associated with an increased duration of DENV illness in DENV-naives ( 5 . 70 versus 2 . 69 days , p = 0 . 045 ) ( Table 4 ) . For DENV-monotypics and multitypics , no difference in the duration of illness was observed . Among those with JEV NAbs prior to infection , there was no difference in the geometric mean titer between asymptomatically and symptomatically infected individuals ( p = 0 . 45 by Mann-Whitney U test , Figure 2 ) . The distributions of the titers were similar . The greatest number of RT-PCR-positive illnesses in the cohort was associated with DENV-2; the highest proportion of hospitalized infections was observed with DENV-3 ( Figure 3a ) . The infecting DENV serotype was unknown for all asymptomatic infections and for 25 . 2% of symptomatic infections . 40 . 0% of children missing serotype data resided in communities with a single serotype in circulation the year of their infection and were therefore eligible for imputation . The probability of symptomatic infection was increased with JEV NAbs for all DENV serotypes , but the association was not significant for any serotype in subgroup analysis ( Figure 3b ) . Limiting the comparisons to symptomatic , RT-PCR positive infections ( with no imputation ) , the direction of the association between JEV NAbs and hospitalized illness was highly variable ( Figure 3c ) . DENV-3 infection in the setting of preexisting JEV NAbs was associated with decreased hospitalized illness ( 15 . 0% hospitalized with JEV NAbs versus 46 . 7% hospitalized without , p = 0 . 004 by χ2 test ) . The positive , significant association between the presence of JEV NAbs and symptomatic infection remained after controlling for age , pre-infection DENV immunity , and epidemic year and accounting for clustering of observations by school ( data not shown ) . The adjusted odds ratio was 1 . 70 ( 95% CI 1 . 15 to 2 . 51 ) .
The serological cross-reactivity between DENV serotypes is well-documented and has been linked to both cross-protection and enhanced disease . In contrast , the clinical implications of serological cross-reactivity observed between DENV and other non-DENV flaviviruses remain unclear . In this study , we characterized the association between preexisting JEV antibodies and the clinical severity of subsequent DENV infection in a prospective study of school-children in Thailand . We report the novel finding that JEV NAbs were associated with the increased occurrence of symptomatic DENV infection . The increased occurrence of symptomatic illness with preexisting JEV NAbs was most pronounced in children who were DENV-naïve ( i . e . , presumably experiencing their first DENV infection ) , for whom JEV NAbs were also associated with a longer duration of illness . It is important to note that given the sensitive method of identifying febrile illnesses in this cohort , ‘symptomatic DENV illnesses’ ranged from a single day of fever to a prolonged and debilitating disease course . It is therefore notable that we observed an increase in the duration of illness in those with JEV NAbs , suggesting that their influence was to increase the occurrence and severity of clinically-meaningful DENV illness . The significant findings in DENV-naïve children are noteworthy because in this group , JEV NAbs are more likely to reflect a ‘true’ prior exposure to JEV or JEV vaccine in this group and less likely to have arisen as a cross reactive response to a prior DENV infection . The association between JEV NAbs and symptomatic illness was not as strong for DENV-monotypics and -multitypics , which could be due to cross-protection with increasing DENV immunity and/or a confounding effect with the inclusion of JEV NAbs as a result of cross-reactivity . Given the absence of confirmatory data from other human cohorts or animal models , we must place these findings in the context of prior in vitro investigations . This study may be most analogous and in accordance with Putvatana et al , who found no evidence of antibody-dependent enhancement of DENV-2 infection in vitro using sera of JEV-immune Thai individuals [21] . We report an increase in non-hospitalized DENV illnesses with preexisting JEV NAbs , but not DHF ( in unadjusted analysis and for nearly all subgroups ) , which may suggest that enhancement is less likely to be the mechanism of increased DENV illness . However , the number of DHF cases was low in this cohort study and therefore the power to detect an association between JEV antibodies and severe DENV illness was limited . It is also possible that studies of this association , both in vivo and in vitro , could have different conclusions based upon the serotypes and strains under consideration [18] . Indeed , while only limited serotype-specific analyses were possible with these data , the nature of the association between JEV NAbs and hospitalized illness did not appear to be consistent across serotypes . Finally , one important caveat with this in vivo study is that while an association was indeed detected between JEV NAbs and DENV illness , it is possible that JEV NAbs are in fact a marker of another underlying biological function that was not considered in this analysis , such as cell-mediated immunity . Notably , there was not a strong independent association in this study between preexisting DENV immunity and the clinical severity of a subsequent DENV infection; this may at first glance appear to be in contrast to prior studies linking secondary DENV infection with an increased occurrence of hospitalized illness or DHF [36] , [37] . However , the present study uniquely focused on predictors of symptomatic ( primarily non-hospitalized ) infection , for which the influence of prior DENV exposures and DENV immunity may be different . Second , while DENV-naives and DENV-monotypics may be somewhat reliably characterized as having no prior DENV infections and one prior DENV infection , respectively , the DENV-multitypic group likely comprises a mixture of children with multiple prior DENV infections as well as children with a single prior DENV infection and a persistent cross-reactive response . This ‘mixing of effects’ within the DENV-multitypic group may have clouded the association between DENV immunity and the clinical severity of DENV infection . It is possible that some children were misclassified with respect to pre-infection JEV serostatus in this study . The prevalence of JEV NAbs in children experiencing DENV infection in the cohort was 45% , remarkably low given that vaccine coverage was estimated to exceed 80% at the time of the study . This low seropositivity is likely due to waning of the antibody response , which is a well-documented phenomenon with JEV inactivated vaccines and particularly with the two-dose regimen [35] , [38] . 60% of children who lacked JEV NAbs ‘seroconverted’ to become JEV NAb positive in the post-season sample following a DENV infection , which may reflect an anamnestic response to prior JEV vaccination . Additionally , 76% of DENV-naïve/JEV-negatives exhibited a secondary-type response by ELISA during acute infection , further suggesting that negative JEV and DENV antibody titers do not preclude the possibility of a prior flavivirus infection or JEV vaccination . It is conceivable that different sources of JEV NAbs , which could not be discerned in this study , may modulate the severity of DENV infection in different ways . Given the cross-reactivity between JEV and DENV in serological assays , it is possible that JEV NAbs in some cases were present as a cross-reactive response to a prior DENV infection . Further , while JEV vaccination was widespread during the study period , wild-type JEV continued to circulate and cause human infections . In summary , the JEV antibodies detected in the cohort may have arisen as a result of JEV vaccination , JEV infection , cross-reactivity from DENV infection , or combinations of these . Future studies should seek to distinguish between vaccine-derived JEV immunity and immunity derived from natural exposure , perhaps by analysis of NS1-specific antibodies [39] . The identification of asymptomatic DENV seroconversions using sequential HI antibody data is a unique strength of this study . However , it should be noted that the sensitivity of this method to detect post-primary DENV infections , or DENV infections in JEV immunes , has not yet been validated against a gold standard . It is therefore possible that some DENV infections were missed or misclassified as JEV infections ( i . e . , false negatives ) , or that assay and/or biological variability caused an elevation of HI titers where no new infection had in fact occurred ( i . e . , false positives ) . This first report of an association between preexisting JEV NAbs and DENV illness warrants further study . Because this study was conducted on a relatively limited temporal , spatial , and demographic scale , similar analyses should be repeated in other cohorts . It would be of particular interest to compare DENV-endemic regions where live-attenuated and inactivated JEV vaccines are in use . Possible associations between other flaviviruses and DENV illness should be evaluated , and possible effect-modifying effects of the infecting DENV serotype , should be explored . It would be of great interest to investigate the association between JEV cell-mediated immunity induced by JEV vaccination and wild-type infection and the occurrence of DENV illness . The biological mechanism of this association remains to be elucidated . However , an intriguing mechanistic possibility may be found in the literature surrounding other inactivated virus vaccines . Some inactivated vaccines have been linked with increased disease , perhaps due to the generation of an epitope-restricted , lower titer immune response . Early efforts to develop inactivated vaccines against measles and respiratory syncytial virus were abandoned after they were linked with the occurrence of atypical , occasionally severe disease [40] , [41] There have been no reports to date of immuno-pathological responses following inactivated flavivirus vaccination in humans , though a recent study in mice reported that low doses of JEVAX were associated with increased viral load and death following subsequent Murray Valley encephalitis virus challenge , relative to placebo , high doses of JEVAX , and the live Chimerivax-JEV vaccine [42] . In summary , we report that the prior existence of JEV NAbs was associated with an increased probability of symptomatic DENV illness in a cohort of school-children in Thailand . These findings have public health importance in that DENVs co-circulate with other flaviviruses in much of their geographic range ( e . g . , JEV in Asia , yellow fever virus in Africa and South America , and West Nile virus in various locations in both hemispheres ) and JEV vaccination is common throughout South and Southeast Asia . We suggest that the issue of heterologous flavivirus immunity and DENV , usually considered to be inconsequential or perhaps protective , merits renewed interest and investigation . In particular , the findings indicate that DENV vaccine developers should include preexisting flavivirus immunity and vaccination histories in assessments of vaccine safety and efficacy . The results of these studies may be important for shaping DENV vaccine implementation strategies . | Dengue viruses ( DENVs ) and Japanese encephalitis virus ( JEV ) have significant cross-reactivity in serological assays , but the possible clinical implications of this remain poorly understood . Interactions between these flaviviruses are potentially important for public health because wild-type JEV continues to co-circulate with DENV in Southeast Asia , the area with the highest burden of DENV illness , and JEV vaccination coverage in this region is high . In this study , we examined how preexisting JEV neutralizing antibodies ( NAbs ) influenced the clinical severity of subsequent DENV infection using data from a prospective school-based cohort study in Thailand that captured a wide range of clinical severities , including asymptomatic , non-hospitalized , and hospitalized DENV infections . We found that the prior existence of JEV NAbs was associated with an increased occurrence of symptomatic versus asymptomatic DENV infection . This association was most notable in DENV-naives , in whom the presence of JEV NAbs was also associated with an illness of longer duration . These findings suggest that the issue of heterologous flavivirus immunity and DENV infection merits renewed attention and interest and that DENV vaccine developers might incorporate detailed assessments of preexisting immunity to non-DENV flaviviruses and histories of vaccination against non-DENV flaviviruses in evaluating DENV vaccine safety and efficacy . | [
"Abstract",
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] | 2011 | Preexisting Japanese Encephalitis Virus Neutralizing Antibodies and Increased Symptomatic Dengue Illness in a School-Based Cohort in Thailand |
PU . 1 is a hematopoietic transcription factor that is required for the development of myeloid and B cells . PU . 1 is also expressed in erythroid progenitors , where it blocks erythroid differentiation by binding to and inhibiting the main erythroid promoting factor , GATA-1 . However , other mechanisms by which PU . 1 affects the fate of erythroid progenitors have not been thoroughly explored . Here , we used ChIP-Seq analysis for PU . 1 and gene expression profiling in erythroid cells to show that PU . 1 regulates an extensive network of genes that constitute major pathways for controlling growth and survival of immature erythroid cells . By analyzing fetal liver erythroid progenitors from mice with low PU . 1 expression , we also show that the earliest erythroid committed cells are dramatically reduced in vivo . Furthermore , we find that PU . 1 also regulates many of the same genes and pathways in other blood cells , leading us to propose that PU . 1 is a multifaceted factor with overlapping , as well as distinct , functions in several hematopoietic lineages .
Cellular identities are established through the actions of master regulatory transcription factors . In addition to promoting their lineage-specific gene expression programs , these factors may also inhibit the transcriptional programs of alternative cell lineages [1] . These inhibitory functions may serve to ensure that genes of closely related lineages are not mis-expressed [2] , [3] . The mechanisms used by such transcription factors to inhibit alternative lineage-specific gene expression are not well understood . The mutual antagonism between hematopoietic master regulators PU . 1 and GATA-1 has served as an important paradigm , both for understanding lineage specification as well as these types of inhibitory interactions . PU . 1 is an Ets family transcription factor that is required for the development of myeloid and B-cells [4] , [5] . GATA-1 is a Zn-finger DNA binding protein that is required for the development of erythrocytes and megakaryocytes [6] . These two factors have a particularly close developmental relationship because they direct lineage commitment from common multipotential progenitors . Indeed , PU . 1 and GATA-1 physically interact and repress each other's transcriptional activation and lineage specification functions [7]–[9] . Although PU . 1 is highly expressed in myeloid and B-cells , it is also normally present in immature erythroid cells [10]–[12] . Down regulation of PU . 1 is required for erythroid terminal differentiation [11]–[13] . This property has been conserved throughout vertebrate evolution [7] , [14] , [15] . The Sfpi1 locus encoding PU . 1 is also a frequent target for integration by the spleen-focus-forming virus during Friend leukemia virus-induced murine erythroleukemia ( MEL ) [16] . The ability of PU . 1 to bind to and repress GATA-1 transcriptional activity accounts for some of its functions in erythroid progenitors . However , PU . 1 is also a DNA binding protein , with a number of well-established gene targets that it regulates in myeloid cells and B-cells [17]–[20] . Therefore , PU . 1 might also regulate important genes in erythroid progenitors . Several examples of PU . 1 gene targets with effects on erythroid cells have been described recently [11] , [21] , [22] . However , it is not known how many genes PU . 1 controls in erythroid cells . Here we report that PU . 1 does indeed direct an extensive transcriptional network in immature erythroid cells , a network consisting of pathways that are important for the growth and survival of erythroid progenitors . Moreover , we find that several of these pathways are also regulated by PU . 1 in other hematopoietic lineages , suggesting that PU . 1 has overlapping functions in several hematopoietic lineages .
As a first step in identifying the transcriptional network controlled by PU . 1 in immature erythroid cells , we performed ChIP-Seq in normal proliferating erythroid progenitors derived from embryonic stem cells ( ES-EP ) [23] and leukemic erythroblasts ( MEL cells ) . We obtained a total of 13 , 416 , 531 and 12 , 710 , 420 uniquely mapped reads in ES-EP and MEL cells , respectively . Using two peak calling programs , cisGenome and spp [24] , [25] , we identified a total of 16 , 241 peaks of PU . 1 occupancy in ES-EP and 16 , 599 peaks in MEL cells . We also compared the number of reads in a given peak in MEL cells and ES-EP and then assigned a peak as present in both cell types ( ≤5 fold difference in the number of reads ) or enriched in one cell type ( >5 fold difference ) ( Figure 1A ) . With this classification scheme , we identified 16 , 011 peaks that are shared between the two cell types and 230 and 588 peaks that are enriched in ES-EP and MEL cells , respectively ( Figure 1B left ) . Strikingly , more than 95% of the peaks are shared between the two cell types ( Figure 1B left ) . Statistical analysis of the data presented in Figure 1A also revealed a strong similarity between PU . 1 binding in the two cell types as evidenced by a correlation coefficient of 0 . 800 ( p-value<2 . 2×10−16 ) . This similarity is even visually evident from examination of the signal tracks of 500 kb windows , such as the one displayed in Figure 2A . We used qChIP to verify that some of the rare loci enriched in one cell type are indeed differentially occupied in the two cell types ( Figure S1 ) . Overall , our results indicate that the binding patterns of PU . 1 are highly similar in normal and leukemic erythroid cells . Since PU . 1 occupies a surprisingly large number ( >16 , 000 ) of sites in ES-EP and MEL cells ( Figure 1B ) , it was of interest to determine whether association of PU . 1 with such sites is directed by a PU . 1 consensus binding sequence or whether many of these sites do not contain such a sequence , which would suggest that PU . 1 occupancy is likely due to protein-protein interactions with other transcription factors . Analysis of the ChIP-Seq peaks in ES-EP and MEL cells with the MEME program [26] generated the position-weighted matrices shown in Figure 2B . The matrices , which are nearly identical for the two cell types , contain a purine-rich core sequence , GGAA , corresponding to the previously described core sequence present in PU . 1 binding sites [27] , [28] and matching the known binding sequence of the Ets family of transcription factors [19] . However , our analysis also identified a bias for an AAAGA sequence upstream of the purine-rich core , consistent with a recent report [17] . Of note , 13 of 14 positions in the matrices have a strong bias for purine residues . We found that 74% of peaks shared in ES-EP and MEL cells contain the consensus motif shown in Figure 2B , indicating that occupancy by PU . 1 at the majority of observed sites is due to direct interaction between PU . 1 and DNA . MEME analysis of the 26% of peaks that did not contain a PU . 1 consensus motif showed enrichment for highly repetitive sequences that did not match any known transcription factor binding sites in the TRANSFAC database ( data not shown ) . To better understand the network of genes that are potentially regulated by PU . 1 in immature erythroid cells , we sought to associate peaks with genes . We determined that 6 , 826 of the PU . 1 occupied sites shared by ES-EP and MEL cells lie within 2 kb of the transcription start site ( TSS ) of known genes ( referred to as the proximal promoter ) ( Figure 1B right ) . Moreover , we find that the greatest concentration of peaks within the proximal promoter is found at the TSS in both cell types ( Figure 2C left ) . Further analysis of the genome-wide distribution of peaks showed that ∼70% of peaks are found either within a gene structure or in the flanking 2 kb region , whereas ∼30% of peaks are located in intergenic regions , suggesting the main mechanism of PU . 1-mediated transcriptional regulation is through short-range interactions with the transcriptional machinery ( Figure 2C right ) . This is in contrast to recent reports on the distribution of PU . 1 in other hematopoietic cell types [17] , [18] , [29] and our unpublished data in macrophages and B-cells ( see Discussion ) . Interestingly , a larger percentage ( ∼40% ) of shared peaks lie within proximal promoters , whereas a much smaller percentage ( ∼10% ) of the peaks enriched in either ES-EP or MEL cells exhibit this characteristic ( Figure S2A ) . Moreover , MEME analysis showed that the weighted matrices of the enriched peaks are slightly different from the weighted matrices from all peaks ( compare Figure 2B and Figure S2B ) . Since PU . 1 was found to bind to many genes , it was important to understand the functional consequences of PU . 1 occupancy in immature erythroid cells . Therefore , we correlated the PU . 1 ChIP-Seq data with two types of mRNA profiling in these cells . First , we compared the transcriptomes of ES-EP and MEL cells . We found that the mRNA profiles of the two cell types are quite similar , despite the differences in their phenotypic properties ( Figure S3 ) . Nevertheless , we identified 758 genes that are occupied by PU . 1 near their TSS ( PU . 1 target genes ) and that are differentially expressed by 2-fold or more between the two cell types ( 440 genes upregulated in ES-EP and 318 genes upregulated in MEL ) . For these genes , we compared their PU . 1 occupancy ( represented as the number of reads in the PU . 1 peak ( s ) near their TSS ) with their relative levels of expression in the two cell types . This analysis showed that genes differentially expressed ≥2-fold more in one cell type have higher PU . 1 occupancy in that cell type ( Figure 3A ) . This result suggests that , for genes differentially expressed between the two cell types , PU . 1 occupancy often leads to upregulation of the gene's expression in that cell type , and hence PU . 1 is acting on such genes primarily as a transcriptional activator . Consistent with this view , we also find in both ES-EP and MEL cells that the average level of expression of PU . 1 target genes is significantly higher than that of non-PU . 1 target genes ( Figure 3B ) . To determine how PU . 1 affects the expression of its target genes , we combined the ChIP-Seq data with comparative gene expression analysis of fetal liver erythroid progenitors from wild-type E13 . 5–14 . 0 embryos and embryos that have a deletion in an Upstream Regulatory Element ( URE ) at the Sfpi1 locus encoding PU . 1 , resulting in reduced PU . 1 expression [30] . mRNA profiling was performed on very early committed erythroid progenitors ( CD71med TER119low ) isolated by flow cytometry [31] . The PU . 1 mRNA level was found to be reduced by about 70% in the PU . 1 low erythroid progenitors relative to wild-type progenitors ( Figure 3C ) , similar to reports in other cell types [30] , [32] , [33] . The gene expression analysis revealed that 617 genes are up regulated and 836 genes are down regulated by PU . 1 at least 1 . 5 fold in these cells . Of these 1453 genes exhibiting PU . 1 dependent expression in erythroid progenitors , 504 genes ( 35% ) have PU . 1 bound within 2 kb of their TSS . Therefore , 7 . 4% ( 504/6826 ) of PU . 1 bound genes are regulated by PU . 1 . Interestingly , when we correlated the gene expression changes in erythroid cells to the level of PU . 1 occupancy , we found that genes exhibiting the strongest PU . 1-dependent gene expression changes have a lower level of PU . 1 binding ( Figure 3C ) , a phenomenon that needs to be further explored . These findings reveal that a large number of genes are bound and regulated in a PU . 1-dependent manner in erythroid progenitors . Besides PU . 1 , a number of other factors have been shown to be involved in regulating erythroid differentiation and erythroid-specific gene expression . For example , the Ets protein Fli-1 [34] , [35] and c-Myb [36] , [37] , like PU . 1 , inhibit erythroid differentiation , whereas Gfi-1b [38] , the erythropoietin receptor ( EpoR ) [39] , and the erythroid kruppel-like factor Klf1 [40] , [41] promote erythroid differentiation . Comparative gene expression analysis in wild-type and PU . 1 low erythroid progenitors revealed that c-myb and Fli-1 are upregulated by PU . 1 , whereas Gfi-1b , Klf1 , and EpoR are significantly downregulated ( Figure 3C ) . Furthermore , our ChIP-Seq data shows PU . 1 occupancy either very near to the TSS and/or within the transcribed region of each of these genes in both normal ES-EP and MEL cells ( Figure 4A and 4B ) . These data were confirmed by qChIP in both cell types ( Figure 4C and 4D ) . Interestingly , we observed very high levels of PU . 1 occupancy in erythroid cells at the Upstream Regulatory Element ( URE ) lying ∼14 kb upstream of the PU . 1 ( Sfpi1 ) gene itself ( Figure 4AIII and 4C ) . The URE has been shown to have a strong positive effect on PU . 1 expression in myeloid cells [30] , [42] . Indeed , deletion of the URE element resulted in about a 70% reduction of PU . 1 in early fetal liver erythroid progenitors , similar to other cell types , suggesting PU . 1 upregulates its own expression in immature erythroid cells ( Figure 3C , [30] , [32] , [33] . The direct positive and negative effects of PU . 1 on expression of the aforementioned genes fit well with their observed roles in erythroid differentiation , further strengthening the idea that PU . 1 controls the differentiation decision in erythroid cells . To further understand the categories of genes and biological pathways regulated by PU . 1 in erythroid cells , we analyzed the ChIP-Seq PU . 1 targets with Ingenuity Pathway Analysis ( IPA ) software . Table S1 shows the ten most significantly over-represented categories of molecular and cellular functions that are represented in the PU . 1 target genes in both ES-EP and MEL cells . Interestingly , the two categories at the top are gene expression and cell cycle . To validate that PU . 1 does indeed bind within the proximal promoter of genes in these two categories , we carried out qChIP experiments on a total of 32 genes , in both ES-EP and MEL cells . 84% and 94% of the 32 genes in each cell type , respectively , were validated for PU . 1 occupancy by qChIP ( Figure 4 , Figure S1 , and Figure S4 ) . IPA analysis also revealed several important cellular pathways that are regulated by PU . 1 in erythroid cells . For example , 58% ( 79/137 ) of genes involved in the PI3K/Akt signaling pathway are occupied by PU . 1 in ES-EP and MEL cells ( Figure 5A ) . Gene expression analysis of wild-type and PU . 1 low erythroid progenitors indicates that many genes that stimulate this pathway are positively regulated by PU . 1 ( Figure 5A ) . Another pathway found to be regulated by PU . 1 in erythroid cells is the ERK/MAPK signaling pathway . 48% ( 92/192 ) of the genes in this IPA pathway are occupied by PU . 1 ( Figure 5B ) . As with the PI3K signaling pathway , gene expression analysis shows that PU . 1 upregulates many genes that stimulate ERK/MAPK signaling ( Figure 5B ) . IPA analysis also showed that PU . 1 regulates the Jak/Stat signaling pathway . 67% ( 43/64 ) of the genes in this pathway are occupied by PU . 1 , and many of these genes are upregulated by PU . 1 ( data not shown ) . As discussed below , the PI3K/Akt , ERK/MAPK , and Jak/Stat signaling pathways have all been shown to play important roles in erythroid cell proliferation , survival and differentiation . Therefore , these results indicate that PU . 1 regulates many genes and pathways that are crucial for erythroid cell function . The foregoing results indicate that PU . 1 regulates several pathways that control the proliferation , survival and differentiation of erythroid cells . Previous studies reported that ex vivo cultures of PU . 1-depleted fetal liver erythroblasts [10] or preleukemic erythroblasts [43] exhibit defects in proliferation and increased cell death . To determine whether PU . 1 levels regulate the number of erythroid progenitors in vivo and the precise stage of its effects , we analyzed the distribution of five distinct populations of erythroid cells in fetal livers of mutant PU . 1 low embryos and wild-type littermate embryos [30] . The five populations were identified by flow cytometry analysis for erythroid-specific TER119 and the transferrin receptor ( CD71 ) , representing populations of progressively more mature stages of erythroid cells from the earliest erythroid committed cells ( R1 - CD71med TER119low ) to late orthochromatophilic erythroblasts and reticulocytes ( R5 - CD71med TER119high ) , as described previously [31] . We observed a dose-dependent reduction in the percentage of R1 cells from fetal livers of heterozygous and homozygous mutant embryos compared to wild-type littermate embryos ( Figure 6A ) . Whereas , on average 8 . 6% of the fetal-liver of wild-type embryos are comprised of R1 cells , this compartment constitutes only 6 . 1% ( t-test p-value 0 . 045 compared to wild-type ) and 2 . 7% ( t-test p-value 0 . 009 compared to wild-type ) in heterozygous and homozygous mutant embryos , respectively . Given that PU . 1 promotes several critical pathways involved in promoting survival , we hypothesized that the reduction of the R1 population may be due to increased apoptosis of these cells . Indeed , Annexin-V staining showed that the reduction in R1 cells is due , at least in part , to increased cell death in this population ( Figure 6B ) . These results are consistent with a role for PU . 1 in promoting widely utilized pathways for maintaining cell survival in very early erythroid committed cells in vivo . Lineage determining transcription factors , like PU . 1 , are thought to function together with other general and cell type-specific transcription factors to modulate gene expression [17] , [29] . To identify candidate transcription factors that may cooperate with PU . 1 in immature erythroid cells , we scanned the DNA sequences within the shared PU . 1 peaks in ES-EP and MEL cells for potential transcription factor binding sites using the TRANSFAC database ( see Materials and Methods ) . The analysis revealed that there is a non-random , statistically significant ( p-value<10−5 ) association of PU . 1 occupied sites with consensus binding-site sequences for a number of well-studied and less well-studied transcription factors ( Figure S5A ) . For example , consistent with a potential role for PU . 1 in cell cycle regulation , consensus binding-site sequences for E2F factors are found within the PU . 1 ChIP-Seq peaks . Consensus binding site sequences for another factor , ETF , which has been implicated in control of organ size via effects on cell proliferation [44] , are also enriched within the PU . 1 ChIP-Seq peaks . To determine if E2F factors are indeed associated with PU . 1-occupied genes in erythroid cells , we performed qChIP studies of E2F2 and E2F4 , two factors that have been reported to be involved in erythropoiesis [45]–[48] . One or both factors were found to bind near the sites occupied by PU . 1 in the promoters of a number of genes in MEL cells ( Figure S5B , S5C , and Figure S4 ) . Occupancy of PU . 1 and E2F2 was more prevalent than PU . 1 and E2F4 . Interestingly , both E2F2 and E2F4 are present , along with PU . 1 , at the PU . 1 URE itself , while E2F4 and PU . 1 are bound to the PU . 1 promoter region . Although further studies are needed to understand how E2Fs may cooperate with PU . 1 to regulate gene expression in erythroid and possibly other hematopoietic cells , these results support the view that lineage-specific transcription factors work in concert with widely expressed factors . Since PU . 1 is an established transcriptional regulator in myeloid cells , we sought to determine if genes that we observed to be regulated by PU . 1 in erythroid cells are also regulated by PU . 1 in myeloid cells . We performed qChIP analysis in a myeloid cell line ( 32D ) at 15 gene targets bound by PU . 1 in erythroid cells . We found PU . 1 bound 100% of these genes in myeloid cells ( Figure 7A ) , consistent with a recent study in which 80% of PU . 1 peaks lying close to TSS were found to overlap in macrophages and B cells [17] . Since we also observed that PU . 1 regulates many genes in the PI3K/Akt and ERK/MAPK pathways in erythroid cells ( Figure 5 ) , we compared our findings with a published gene expression analysis of PU . 1 null cells undergoing differentiation to macrophages in response to restoration of PU . 1 [49] . We found that , just as we observed in erythroid cells , many of the genes in these two pathways are upregulated by PU . 1 in macrophages ( Figure S6 ) . Additionally , we also compared all of the genes regulated by PU . 1 in early erythroid committed ( R1 ) cells with the macrophage gene expression data , as well as with a gene expression analysis of HSC from PU . 1 knockdown mice [33] . Of the genes that we found to be occupied and upregulated by PU . 1 at least 1 . 2-fold in R1 cells , and also annotated in the two other data sets , 58% ( 94/162 ) are also upregulated in a PU . 1 dependent-manner during macrophage differentiation and 70% ( 113/162 ) are similarly regulated in HSC ( Figure 7B left ) . Similarly , of the genes that we found to be bound and repressed by PU . 1 in erythroid cells and also represented in the other data sets , 57% ( 150/265 ) are also downregulated by PU . 1 in macrophages and 51% ( 134/265 ) are repressed in a PU . 1-dependent manner in HSC ( Figure 7B right ) . We conclude that PU . 1 regulates many of the same genes and pathways in immature erythroid cells , myeloid cells and HSC ( see Discussion ) .
PU . 1 is a master regulatory transcription factor that plays an essential role in the development of the myeloid and B-cell lineages [4] , [5] . However , several lines of evidence suggest that PU . 1 also plays important roles in erythropoiesis ( see Introduction and Figure 6 ) . Some of its effects are likely attributable to its ability to bind to and repress GATA-1 [3] , [7] , [50] , but it is also very important to understand the global gene expression program controlled by PU . 1 in immature erythroid cells . To investigate this program , we first determined the genome-wide binding patterns of PU . 1 in normal erythroid progenitors and leukemic erythroblasts . We found that PU . 1 occupancy is highly similar in both cell types and that PU . 1 binds in close proximity to a large number of genes ( Figure 1 ) . Unexpectedly , PU . 1 appears to occupy more sites ( >16 , 000 ) in immature erythroid cells than three essential red blood cell transcription factors , including GATA-1 ( reported to occupy between 4 , 000–14 , 400 sites ) [51]–[54] , SCL ( <4 , 500 sites ) [55] , and Klf1 ( <2 , 000 sites ) [56] . Although some of these differences may be due to the use of different peak calling methods or other technical issues such as the use of different antibodies and different cells , our unpublished results on GATA-1 occupancy using the same methods and the same cells lead to a similar conclusion ( unpublished data ) . Interestingly , the genomic distributions of PU . 1 in erythroid cells are also dramatically different from that reported for GATA-1 and Klf1 . Whereas we found ∼40% of PU . 1 bound sites are within 2 kb of TSS ( Figure 2 ) , only ∼16% of Klf1 bound sites lie within 1 kb of TSS [56] , and only ∼13% of GATA-1 sites are found within −10 kb of TSS [51] , consistent with our unpublished results indicating that only 9% of GATA-1 bound sites are within 2 kb of TSS in ES-EP ( unpublished data ) . These results suggest that PU . 1 may act independently of these factors , at least at a subset of genes in erythroid progenitors . Recent work demonstrates that PU . 1 directly regulates certain genes that can themselves affect erythroid differentiation . For example , we reported that the CDK6 gene is directly upregulated by PU . 1 [11] . Furthermore , we also showed that CDK6 , like PU . 1 , is rapidly downregulated as erythroid cells enter terminal differentiation and that constitutive expression of CDK6 blocks erythroid differentiation [57] . Likewise , Elf-1 , another member of the Ets transcription factor family , was shown to be directly upregulated by PU . 1 and to negatively regulate erythroid differentiation [22] . Here we report that PU . 1 binds in close proximity to the genes that encode Fli1 , c-myb , and PU . 1 itself ( Figure 4 ) . We also showed that PU . 1 upregulates the expression of each of these genes ( Figure 3C ) . Like PU . 1 , Fli1 and c-myb have been shown to block erythroid differentiation [34] , [36] , [37] , [58]–[60] . On the other hand , we also found that PU . 1 binds in close proximity to and inhibits expression of genes that promote erythroid differentiation , such as Gfi-1b , EpoR , and Klf1 ( Figure 3C and Figure 4 ) [38]–[41] . Thus , our results show that PU . 1 regulates the expression of genes that both inhibit and promote erythroid differentiation . However , the results reported here also reveal that PU . 1 has a much broader role in these cells than simply regulating several factors that control erythroid differentiation . Indeed , the ChIP-Seq and transcriptome analyses show that PU . 1 regulates many genes involved in several signaling pathways that are critical for erythropoiesis . These pathways include the PI3K/Akt , ERK/MAPK , and Jak/Stat pathways ( Figure 5 and data not shown ) , all of which have been implicated in survival and proliferation of erythroid progenitors [61]–[64] . Interestingly , the PI3K/Akt and Jak/Stat pathways regulate PU . 1 levels in erythroid cells [65] , [66] . Taken together , these findings suggest the existence of a feedback loop in which PU . 1 controls the expression of many genes in these crucial signaling pathways and these pathways upregulate the expression of PU . 1 . Our finding that mice with ∼30% of normal PU . 1 levels exhibit a marked reduction in the number of the earliest committed fetal liver erythroid progenitors ( Figure 6 ) is consistent with an important role for PU . 1 in regulating these pathways in vivo . Thus , our studies further advance the concept of master regulatory transcription factors regulating both lineage-specific genes and more widely expressed genes . Several recent papers reported the genome-wide binding patterns of PU . 1 in macrophages [17] , [18] , [20] , neutrophilic precursors [19] , B-cells [17] , and multipotent progenitors cells [29] . Interestingly , the genomic distributions of PU . 1 in these cells are quite different from what we observed in erythroid cells . For example , whereas we found that ∼40% of PU . 1 occupied sites map within 2 kb of TSS ( the overwhelming majority of these being bound within 500 bp of TSS ) ( Figure 2 ) , one study found that only 12% and 18% of PU . 1 binding sites in macrophages and B-cells , respectively , were within 500 bp of TSS [17] , and another study in macrophages found only 20% of such sites to be within 2 . 5 kb of TSS [18] . Similarly , only 23% of PU . 1 peaks mapped within 1 kb of TSS in multipotential hematopoietic progenitor cells [29] . It is likely that part of the reason for these differences is the larger number of PU . 1 occupied sites in the other cell types compared with erythroid cells . In fact , comparison of ChIP-Seq data for PU . 1 in macrophages and B-cells that we have generated , which resembles published data , shows that most peaks observed in the erythroid cells are also present in macrophages and B-cells ( unpublished data ) . Consistent with this observation , we found that 100% ( 15/15 ) of genes occupied by PU . 1 in erythroid cells are also occupied by PU . 1 in 32D cells ( Figure 7 ) . Furthermore , most of the genes targeted for regulation by PU . 1 in erythroid cells are not lineage specific; rather they are genes that are widely expressed in many cell types . This observation begs the question: Does PU . 1 control common sets of genes in the several hematopoietic lineages in which it is expressed ? To answer this question , we made several different types of comparisons of PU . 1 gene targets in erythroid cells , myeloid cells and HSC . For example , we observed that many PU . 1 gene targets in the PI3K/Akt and ERK/MAPK pathways that we identified in erythroid cells are similarly regulated by PU . 1 in macrophages ( Figure 5 and Figure S6 ) . We also found that the majority of genes that are occupied and either positively or negatively regulated in erythroid cells are similarly regulated in macrophages and HSC ( Figure 7B ) . Taken together , these comparative analyses indicate a very significant overlap in PU . 1-dependent gene regulation in erythroid cells and other hematopoietic cells . Much evidence supports the view that PU . 1 plays a major role in establishing cellular identity in B-cells and myeloid cells . Finding such an extensive overlap among the PU . 1 gene targets in several related but quite distinct hematopoietic cell types raises the question as to how cellular identity is established by PU . 1 . We suggest that the answer lies , at least in part , in the differences in the levels of PU . 1 in different hematopoietic cells . Myeloid cells have the highest level of PU . 1 , B-cells have an intermediate level [67] , and immature erythroid cells have a much lower level [68] , [69] . Overexpression of PU . 1 in B-cells reprograms the cells to macrophages [70] , similar to what has been reported in MEL cells [71] , [72] . These observations , taken together with our finding that many widely expressed genes are occupied and similarly regulated by PU . 1 in immature erythroid cells , myeloid cells and HSC ( Figure 7 and Figure S6 ) , suggest that PU . 1 has both shared and lineage-specific functions in these lineages , depending on the level of the factor in each lineage . We suggest that even at low concentrations , PU . 1 can promote ubiquitous cellular functions such as proliferation and survival . Indeed , PU . 1 has been reported to promote proliferation of both erythroid progenitors [10] , [43] and bone marrow derived macrophages [73] . However , higher concentrations of PU . 1 are required to promote its lineage-specific functions [67] , [70]–[72] . It will be interesting to determine whether this principle of overlapping gene networks regulated by a transcription factor in several related lineages , proposed here for PU . 1 , is applicable to master transcriptional regulators in other developmental systems .
MEL cells ( clone DS19 ) stably expressing a GATA-1-estrogen receptor ligand-binding domain ( ER ) fusion protein ( GATA-1/ER ) was described previously [74] . ES-EP cells were cultured as previously described [23] . Further details can be found in Text S1 . qChIP was performed as previously described [11] . ChIP for ChIP-Seq analysis was performed similarly but using 5×107 cells and 60 µg of anti-PU . 1 antiserum . A list of antibodies used in this study , along with further information on processing of ChIP-Seq samples can be found in Text S1 . A summary of PU . 1 ChIP-Seq data can be found in Dataset S1 . Uniquely mapped reads from ChIP-Seq data were mapped to the mouse genome ( mm9 ) . Details on peak calling and subsequent statistical analysis can be found in Text S1 . ChIP-Seq data is deposited on GEO database under the accession number GSE21953 . Total RNA was isolated from duplicate cultures of proliferating ES-EP and MEL cells or R1 cells sorted from two wild-type and URE−/− animals using the RNeasy Kit ( Qiagen ) following the manufacturer's instructions . Total RNA was further processed by the AECOM microarray facility using the standard Affymetrix pipeline and hybridized to GeneChip Mouse Gene 1 . 0 ST ( Affymetrix ) . This expression data can be accessed from the GEO database under the accession number GSE21953 . Subsequent statistical analysis is described in Text S1 . For each PU . 1 peak , we extracted a 500-bp sequence around the peak center ( i . e . , +/−250 bp ) and used it for searching de novo motifs with the MEME software [26] and also known motifs in the TRANSFAC software suit ( release 11 ) [75] . For motif analysis with TRANSFAC , we utilized the program MATCH in the TRANSFAC software package and applied the default parameters for minimizing the false positive rate ( i . e . , minFP option ) . As our background control , we ran MATCH against a randomly chosen set of 10 , 000 sequences ( 500-bp each ) . The enrichment of a known motif in PU . 1 peaks was then calculated as the ratio of the motif occurrence frequency in PU . 1 peaks and its corresponding frequency in background sequences . P-value was derived with a binomial test of the difference in motif frequency . | Cellular identities are established by master regulatory transcription factors that promote cell type–specific gene expression . In some instances such factors also inhibit differentiation of alternative , closely related lineages . PU . 1 is an Ets family transcription factor that is required for myeloid and B cell development . PU . 1 is also expressed in red blood cell progenitors where it blocks erythroid terminal differentiation . One mechanism used by PU . 1 to block red blood cell differentiation is by binding to and inhibiting the erythroid master regulator GATA-1 . Here , we describe another mechanism utilized by PU . 1 in erythroid progenitors . Using chromatin immunoprecipitation and high-throughput sequencing ( ChIP-Seq ) combined with gene expression profiling of erythroid progenitors , we show that PU . 1 controls a large gene network in immature erythroid cells , including genes in pathways involved in regulating growth , survival , and differentiation of these cells . We also find that PU . 1 controls many of the same genes and pathways in other blood cells . The results indicate that , in addition to activating lineage-specific genes , master regulatory transcription factors , like PU . 1 , also control numerous , widely expressed genes in multiple cell lineages . | [
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"expression"
] | 2011 | A Large Gene Network in Immature Erythroid Cells Is Controlled by the Myeloid and B Cell Transcriptional Regulator PU.1 |
The importance of Cryptosporidium as a pediatric enteropathogen in developing countries is recognized . Data from the Global Enteric Multicenter Study ( GEMS ) , a 3-year , 7-site , case-control study of moderate-to-severe diarrhea ( MSD ) and GEMS-1A ( 1-year study of MSD and less-severe diarrhea [LSD] ) were analyzed . Stools from 12 , 110 MSD and 3 , 174 LSD cases among children aged <60 months and from 21 , 527 randomly-selected controls matched by age , sex and community were immunoassay-tested for Cryptosporidium . Species of a subset of Cryptosporidium-positive specimens were identified by PCR; GP60 sequencing identified anthroponotic C . parvum . Combined annual Cryptosporidium-attributable diarrhea incidences among children aged <24 months for African and Asian GEMS sites were extrapolated to sub-Saharan Africa and South Asian regions to estimate region-wide MSD and LSD burdens . Attributable and excess mortality due to Cryptosporidium diarrhea were estimated . Cryptosporidium was significantly associated with MSD and LSD below age 24 months . Among Cryptosporidium-positive MSD cases , C . hominis was detected in 77 . 8% ( 95% CI , 73 . 0%-81 . 9% ) and C . parvum in 9 . 9% ( 95% CI , 7 . 1%-13 . 6% ) ; 92% of C . parvum tested were anthroponotic genotypes . Annual Cryptosporidium-attributable MSD incidence was 3 . 48 ( 95% CI , 2 . 27–4 . 67 ) and 3 . 18 ( 95% CI , 1 . 85–4 . 52 ) per 100 child-years in African and Asian infants , respectively , and 1 . 41 ( 95% CI , 0 . 73–2 . 08 ) and 1 . 36 ( 95% CI , 0 . 66–2 . 05 ) per 100 child-years in toddlers . Corresponding Cryptosporidium-attributable LSD incidences per 100 child-years were 2 . 52 ( 95% CI , 0 . 33–5 . 01 ) and 4 . 88 ( 95% CI , 0 . 82–8 . 92 ) in infants and 4 . 04 ( 95% CI , 0 . 56–7 . 51 ) and 4 . 71 ( 95% CI , 0 . 24–9 . 18 ) in toddlers . We estimate 2 . 9 and 4 . 7 million Cryptosporidium-attributable cases annually in children aged <24 months in the sub-Saharan Africa and India/Pakistan/Bangladesh/Nepal/Afghanistan regions , respectively , and ~202 , 000 Cryptosporidium-attributable deaths ( regions combined ) . ~59 , 000 excess deaths occurred among Cryptosporidium-attributable diarrhea cases over expected if cases had been Cryptosporidium-negative . The enormous African/Asian Cryptosporidium disease burden warrants investments to develop vaccines , diagnostics and therapies .
Cryptosporidium , the highly infectious protozoan that causes diarrhea in immunocompetent and immunocompromised subjects [1–4] , is transmitted via contaminated water or food [1 , 3 , 5] , swimming or bathing in surface waters [1 , 3] and by direct person-to-person contact [6] , particularly in developing country settings of suboptimal sanitation and limited access to safe drinking water [1 , 3 , 4] . Clinical cryptosporidiosis ranges from self-limited mild diarrhea ( most commonly ) to more severe forms such as persistent diarrhea ( lasting 14 days or more ) leading to malnutrition , hospitalizations and even death [1 , 2 , 5 , 7–13] . Immunocompromised hosts , e . g . , persons with HIV/AIDS and malnourished children in developing countries , are more prone to develop severe clinical illness [1 , 14] . Fecal shedding of Cryptosporidium oocysts can persist for weeks after clinical illness resolves [15 , 16] . Since Cryptosporidium oocysts tolerate chlorination , waterborne outbreaks also occur in industrialized countries [1 , 3 , 5] . Recently , the Global Enteric Multicenter Study ( GEMS ) elucidated the relative importance of Cryptosporidium versus many other enteropathogens as a cause of medically-attended diarrhea in young children in developing countries of sub-Saharan Africa ( SSA ) and South Asia [10] , where most young child diarrheal deaths occur . Cryptosporidium was the second leading cause ( 5–15% ) of moderate-to-severe diarrhea ( MSD ) in infants at all 7 GEMS study sites . Cryptosporidium remained a leading cause of MSD in toddlers age 12–23 months , ranking third after rotavirus and Shigella; 5–9% of all MSD cases in 5 of the 7 sites were attributable to Cryptosporidium [10] . Cryptosporidium-associated MSD negatively impacted linear growth and significantly increased the risk of death in toddlers [10] . A follow-on study , GEMS-1A , investigated Cryptosporidium in association with less-severe diarrhea ( LSD ) over a 1-year period in 6 of 7 GEMS sites; the LSD cases enrolled in GEMS-1A , like the MSD cases enrolled in GEMS , were pediatric patients who were brought to health care facilities . We extrapolated GEMS site-specific burdens of Cryptosporidium-associated MSD and LSD in children age <24 months to estimate Cryptosporidium-associated diarrhea burdens for the entire SSA region ( except the Republic of South Africa ) and the India/Pakistan/Bangladesh/Nepal/Afghanistan ( I/P/B/N/A ) region of South Asia , where ~80% of global young child deaths due to diarrheal disease occur [17 , 18] .
GEMS was a prospective matched case-control study conducted for 36 months at 7 sites where demographic surveillance systems ( DSS ) regularly updated censused populations . Sites included: Basse , The Gambia; Bamako , Mali; Manhiça , Mozambique; Siaya County , Kenya; Kolkata , India; Mirzapur , Bangladesh; and Bin Qasim Town , Pakistan . The published rationale [19] , working assumptions [20] , epidemiological [21] , laboratory [22] , and statistical methods [23] of GEMS are summarized below . The GEMS sampling frame comprised children age <60 months residing within each site’s DSS area . Children brought to sentinel health centers ( SHCs ) serving each DSS were assessed for criteria for MSD ( vide infra ) . Every fortnight , 8–9 cases were targeted for enrollment , per age stratum ( 0–11 , 12–23 and 24–59 months ) , per site . Within 14 days of each case enrolled , we undertook to enroll 1–3 randomly selected age- and sex-matched controls from the same or nearby communities . MSD was defined as a new acute diarrheal episode ( ≥3 loose stools in the previous 24 hours , occurring after ≥7 diarrhea-free days , and beginning within the previous 7 days ) , and having some or severe dehydration , initiation of intravenous rehydration based on a clinician’s judgment , visible blood in stools ( dysentery ) , or hospitalization for diarrhea or dysentery . At enrollment , a standardized evaluation , anthropometric measurements , and a stool sample were obtained from cases and controls . A single follow-up home visit was carried out ~60 ( range 49–91 ) days after enrollment , during which the vital status of cases and controls was recorded and anthropometric measurements were made . GEMS-1A was a 1-year extension in which children with MSD and LSD were enrolled at the SHCs in 6 of 7 GEMS sites , while in Kenya only MSD cases were enrolled . LSD was defined as a new acute diarrhea case seen at SHCs that did not meet the definition of MSD . Data collection methods , including the ~60-day follow-up household visit , were otherwise identical to GEMS . Case and control stool samples were tested for numerous enteropathogens [10 , 22] , including Cryptosporidium , which was detected using an enzyme immunoassay ( EIA ) ( TechLab , Inc . Blacksburg , VA ) . A random subset of stool specimens from 3 , 809 GEMS MSD case-control pairs from across all sites was also tested for various enteropathogens using TaqMan Array Card ( TAC ) -based real-time polymerase chain reaction ( PCR ) . Briefly , nucleic acid was extracted from stool specimens using the QIAamp Fast Stool DNA Mini kit ( Qiagen , Valencia , CA ) . The TaqMan Array Card methodology compartmentalizes PCR reactions for 48 targets per specimen as previously described [24] . For this project we included primers to amplify the 18S rRNA gene of Cryptosporidium species [24] and primers for alleles of the LIB13 locus that differentiates C . hominis from C . parvum [25] . The LIB13 locus is not known to be present in other Cryptosporidium species , other than a divergent sequence in C . cuniculus ( GU327781 ) . The assay did not amplify genomic DNA from C . meleagridis isolate TU1867 . Specimens with cycle threshold ( CT ) values ≤40 were considered positive with the species identification assay . For specimens that did not yield a LIB13 result , we performed nested amplification of a longer fragment of the 18S gene rRNA [26] , as well as GP60 to try to identify the species [27] . GP60 sequencing was also performed to subtype the available C . parvum and C . hominis specimens . Since Cryptosporidium was incriminated as a cause of MSD and LSD mainly among children aged <24 months [10] , disease burden extrapolations focused on infants 0–11 and toddlers 12–23 months of age . Details of the analysis are presented in Fig 1 . For each site and age group , pathogen-specific attributable fractions ( AFs ) , weighted according to calendar time and presence or absence of dysentery and adjusted for the presence of other pathogens , and annual attributable incidence ( AI ) rates for MSD during the 3 years of GEMS have been reported [10] . Employing similar methodology [10 , 23 , 28] , GEMS-1A data were used to estimate Cryptosporidium-attributable incidence of LSD , for site/age groups in which Cryptosporidium was associated with LSD with P<0 . 1 after adjustment for other pathogens . Data from GEMS and GEMS-1A were used to estimate odds ratios ( ORs ) for Cryptosporidium and MSD by 6-month age interval for children aged 0–23 months; weighting by time or presence of dysentery should have little effect on associations with Cryptosporidium , and it was not employed in this analysis . Cryptosporidium-specific AFs and AI rates , healthcare utilization rates for MSD and LSD , along with population estimates for the sites , were used to calculate overall Cryptosporidium-specific MSD and LSD AI rates separately for the 4 African sites ( 3 sites for LSD ) and 3 Asian sites . These AI rates were extrapolated to the countries where GEMS sites were located , the 51 countries of the SSA region ( excluding Republic of South Africa ) , and the India/Pakistan/Bangladesh/Nepal/Afghanistan ( I/P/B/N/A ) region of South Asia . For each country or region , the Cryptosporidium AI rate was multiplied by the total population ( per United Nations estimates ) [29] to generate national and region-wide estimates of annual Cryptosporidium-attributable MSD and LSD cases . Since GEMS and GEMS-1A were conducted over 5 calendar years , we used the average UN estimated population size ( for each GEMS country and region ) of children 0–4 years of age during 2005–2010; we divided by 5 to estimate the number of children aged 0–11 and aged 12–23 months . To estimate 95% confidence intervals ( CIs ) , we took the 2 . 5th and 97 . 5th percentiles of the number of Cryptosporidium-attributable diarrhea cases from 100 , 000 Monte Carlo simulations , assuming normal distributions for relevant parameters , with standard deviations estimated from Taylor series approximations . The proportions of C . hominis and C . parvum among the subset of cases ( by PCR ) were multiplied by the total number of Cryptosporidium-attributable diarrhea cases to estimate the species-specific attributable burdens . The number of deaths among children aged <24 months with Cryptosporidium-associated diarrhea over the ~60-day follow-up period following enrollment provided an extended case fatality risk ( ECFR ) [10 , 21] . However , because GEMS and GEMS-1A were conducted in populations with high or moderate <5 years mortality , and given numerous risk factors for death among children with MSD or LSD , some proportion of deaths among Cryptosporidium-associated diarrhea cases would have occurred unrelated to Cryptosporidium infection . Accordingly , we utilized two different analytical strategies to estimate more specifically the role of Cryptosporidium in deaths of children with Cryptosporidium-associated diarrheal illness . First , we estimated the Cryptosporidium-attributable ECFR by subtracting the extended fatality risk ( EFR ) over the ~60-day observation period in GEMS/GEMS-1A controls from the ECFR in Cryptosporidium-positive cases . We used deaths in all controls , because the numbers of deaths among matched controls of Cryptosporidium-positive cases were very small . Multiplying the Cryptosporidium-attributable ECFR by the estimated number of Cryptosporidium-positive MSD and LSD cases in the region ( SSA or I/P/B/N/A ) provides a regional estimate of Cryptosporidium-attributable deaths . We estimated the excess ECFR in Cryptosporidium-positive MSD and LSD cases relative to the ECFR in Cryptosporidium-negative cases by subtracting the ECFR in Cryptosporidium-negative cases from the ECFR in Cryptosporidium-positive cases . This excess risk represents the contribution of Cryptosporidium to death risk beyond both the background risk of death in the general pediatric population and that of diarrhea patients . The excess ECFR was then multiplied by the number of Cryptosporidium-attributable cases in each region , to estimate excess deaths among Cryptosporidium-attributable cases compared to the expected number of deaths if these cases had been Cryptosporidium-negative . Estimates of Cryptosporidium-related deaths for the combined age group 0–23 months were calculated separately for MSD and LSD for the SSA region , given the much higher ECFR in children with MSD; deaths were estimated for MSD and LSD combined for the I/P/B/N/A region . Two-sided 95% CIs for differences in death risks were estimated by the Miettinen and Nurminen likelihood score method [30] . CIs for numbers of deaths were estimated assuming normal distributions for numbers of deaths , with variances estimated from Taylor series approximations . Statistical significance was defined as a two-sided P-value <0 . 05 . Analyses were performed using SAS version 9 , IBM SPSS version 22 , and NCSS 8 . The study protocol was approved by ethics committees at the University of Maryland , Baltimore and at each field site [21] . Parents/caregivers of participants provided written informed consent , and a witnessed consent was obtained for illiterate parents/caretakers .
Among 15 , 284 cases ( 12 , 110 MSD and 3 , 174 LSD ) and 21 , 527 matched controls from GEMS-1 and GEMS-1A , Cryptosporidium data were missing for 11 cases ( 0 . 07% ) and 10 controls ( 0 . 0046% ) and these participants were excluded from analyses . Among the total 15 , 284 MSD and LSD cases , six ( 0 . 039% ) had four matched controls rather than a maximum of three; these six deviations occurred in one African site during GEMS-1 . The six extra matched controls were not censured from the dataset . Overall , Cryptosporidium was detected in stools from 1632 cases ( 10 . 7% ) and 1184 controls ( 5 . 5% ) ( P<0 . 001 ) ; positivity was significantly higher in MSD cases than matched controls in the age groups 0–11 and 12–23 months at all sites , and among the 24–59 month age group in Kenya . Cryptosporidium was also significantly more common in LSD cases than controls aged 0–11 and 12–23 months in Gambia and India , while in Mali and Mozambique this was found only in the toddler age group and in Pakistan only in infants and in children age 24–59 months ( Table 1 ) . Adjusted attributable incidence rates of Cryptosporidium LSD by site and age group are shown in Table 2 . When MSD data were examined in narrower age groups , we found significant positive associations between Cryptosporidium and MSD at 4 sites ( Mali , Mozambique , Kenya , India ) in the first 5 months of life . ORs were higher at age 6–11 months , except in Mali . A significant OR between Cryptosporidium and MSD was observed in Mali , Mozambique , Kenya and India at ages 0–17 months . In Gambia and Pakistan , this association was significant from 6–23 months of age and in Bangladesh only at age 6–11 months . Adjusted AFs increased from age 0–5 months to age 6–11 months and were highest at age 6–11 months , except in Pakistan ( Table 3 ) . We identified Cryptosporidium species by PCR testing for 18S and Lib13 targets in a random subset of 3 , 809 case/control pairs . Samples with unresolved species by Lib13 ( which only differentiates C . hominis from C . parvum ) had species investigated by 18S and GP60 assays , as described in the Methods . This revealed 338 EIA+/PCR+ cases and 157 EIA+/PCR+ controls . Among the 338 samples from Cryptosporidium-positive MSD cases , 333 were suitable for further testing , of which 259 ( 77 . 8% ) , 33 ( 9 . 9% ) , 4 ( 1 . 2% ) , and 2 ( 0 . 6% ) , respectively , were positive for C . hominis , C . parvum , both C . hominis and C . parvum and C . meleagridis; the species of 35 ( 10 . 5% ) specimens remained undetermined . Corresponding percentages in controls were 68 . 2% , 8 . 9% , 0 . 6% , 0 . 6% , and 21 . 0% . The species of one control sample ( 0 . 6% ) was identified as C . canis . GP60 subtypes were identified on 71 EIA+/PCR+ specimens including 32 C . hominis , 37 C . parvum , and 2 C . meleagridis . Of 37 C . parvum infections , 34 ( 91 . 9% ) were anthroponotic strains: 21 were IIc ( 19 A5G3 and 2 A4G3 ) ; 13 were IIe ( 1 IIeA6G1 , 2 IIeA7G1 , 7 IIeA10G1 , 2 IIeA11 , 1 IIeA15 ) ; all three non-anthroponotic strains were IIdA15G1 . These derived from Mali ( n = 13 ) , Kenya ( n = 9 ) , Mozambique ( n = 5 ) , Pakistan ( n = 7 including the 3 non-anthroponotic types ) , Bangladesh ( n = 2 ) , and Gambia ( n = 1 ) . Samples from Kenya and Mozambique were mostly IIcA5G3 ( 12/14 ) . Mali’s strains were diverse; containing 10/11 IIe strains as well as 3 IIcA5G3 . The C . hominis subtypes included Ia ( 1 A18R2 , 1 A19R2 , 1 A23R2 , 1 A24G1R2 , 2 A25R2 , 1 A26R2 ) , Ib ( 3 A9G3 , 8 A13G3 ) , Id ( 1A14 ) , Ie ( exclusively 10 A11G3T3 ) , and If ( 2 A14G1 ) . Both C . meleagridis were subtyped as IIIdA6R1 . The Cryptosporidium-attributable MSD incidence was estimated to be 3 . 48 ( 95% CI , 2 . 27–4 . 67 ) and 3 . 18 ( 95% CI , 1 . 85–4 . 52 ) per 100 child-years in the African and Asian sites , respectively , in the 0–11 months age group . The respective incidences for toddlers aged 12–23 months were 1 . 41 ( 95% CI , 0 . 73–2 . 08 ) and 1 . 36 ( 95% CI , 0 . 66–2 . 05 ) per 100 child-years . Corresponding Cryptosporidium-attributable LSD incidence rates were 2 . 52 ( 95% CI , 0 . 33–5 . 01 ) and 4 . 88 ( 95% CI , 0 . 82–8 . 92 ) in infants and 4 . 04 ( 95% CI , 0 . 56–7 . 51 ) and 4 . 71 ( 95% CI , 0 . 24–9 . 18 ) in toddlers , per 100 child-years . Applying these incidence rates to the pediatric population age <2 years in the SSA and I/P/B/N/A regions , respectively , yielded annual estimated Cryptosporidium MSD burdens of ~1 . 2 million and ~1 . 5 million cases . The total number of LSD and MSD cases was estimated to be 2 . 9 million in this age group in SSA and 4 . 7 million in the populous I/P/B/N/A region ( Table 4 ) . The proportions of cases due to C . hominis and C . parvum in the subset tested for species were multiplied by the total estimated number of Cryptosporidium-attributable diarrhea cases in both regions ( ~7 . 6 million ) , yielding estimates of 5 . 9 million C . hominis , 0 . 76 million C . parvum and 90 , 000 thousand co-infected ( C . hominis plus C . parvum ) cases in children aged <2 years . Subtracting the EFR among all controls of MSD cases aged <24 months at African sites ( 37/6258 , 0 . 6% ) from the ECFR among Cryptosporidium-positive MSD cases ( 41/643 , 6 . 4% ) ( Table 5 ) yielded a Cryptosporidium-attributable ECFR of 5 . 8% ( 95% CI , 4 . 4%–7 . 6% ) . Similarly , subtracting the EFR in controls of LSD cases aged <24 months at African sites ( 4/1261 , 0 . 3% ) from the ECFR among Cryptosporidium-positive LSD cases ( 1/139 , 0 . 7% ) produced an estimated Cryptosporidium-attributable ECFR of 0 . 4% ( 95% CI , -0 . 4%–3 . 7% ) . Multiplying these Cryptosporidium-attributable ECFRs by the numbers of Cryptosporidium-positive cases generated estimates of 107 , 000 Cryptosporidium-attributable MSD deaths ( 95% CI , 68 , 200–151 , 000 ) and 16 , 300 Cryptosporidium-attributable LSD deaths ( 95% CI , 0–75 , 200 ) in the SSA region . After subtracting the EFR in controls of MSD and LSD cases at Asian sites ( 6/6758 , 0 . 09% ) from the ECFR among Cryptosporidium-positive MSD and LSD cases combined ( 5/523 , 1 . 0% ) in the Asian sites and multiplying the resultant Cryptosporidium-attributable ECFR of 0 . 9% ( 95% CI , 0 . 4%–1 . 9% ) by all Cryptosporidium-positive diarrhea cases , we estimate 78 , 900 ( 95% CI , 0–159 , 000 ) deaths attributable to Cryptosporidium-positive diarrhea in the ( I/P/B/N/A ) region . Thus , we estimate a total of ~202 , 000 Cryptosporidium-attributable diarrhea deaths in the two regions combined . Using the same methodology , we estimate 455 , 000 ( 95% CI , 280 , 000–630 , 000 ) annual diarrhea-attributable deaths in the SSA region and 254 , 000 ( 95% CI , 13 , 900–494 , 000 ) in the ( I/P/B/N/A ) region . Subtracting the ECFR of Cryptosporidium-negative MSD cases ( 3 . 3% ) from the ECFR of Cryptosporidium-positive MSD cases ( 6 . 4% ) ( Table 5 ) and multiplying the result by the number of Cryptosporidium-attributable MSD cases in the SSA region indicated that Cryptosporidium was responsible for an excess ~38 , 400 ( 95% CI , 13 , 400–68 , 100 ) MSD deaths among Cryptosporidium-attributable MSD cases . The analogous calculations based on LSD data ( Table 5 ) suggested that Cryptosporidium led to ~3 , 600 ( 95% CI , -31 , 600–46 , 000 ) excess LSD deaths per year . In Asian sites the estimate of annual excess deaths in children <24 months of age with Cryptosporidium-attributable diarrhea was ~16 , 900 ( 95% CI , -20 , 900–61 , 100 ) . Thus , between the two regions we estimate that Cryptosporidium was responsible for ~59 , 000excess deaths in cases of Cryptosporidium-attributable diarrhea in children aged <24 months , compared to the expected deaths in the same number of Cryptosporidium-negative cases .
There have been few attempts to estimate the burden of Cryptosporidium diarrheal disease in large populations . One exception is the Cryptosporidium burden estimate published for India [31] . Others are the global Cryptosporidium-associated mortality estimates contained within Global Burden of Disease ( GBD ) and Child Epidemiology Estimation Group ( CHERG ) reports [32–34] . Three obstacles have heretofore impeded attempts to estimate region-wide Cryptosporidium disease burdens , including: 1 ) marked heterogeneity of clinical and laboratory methods used in studies of the etiology of pediatric diarrhea; 2 ) failure to take into account that many children without diarrhea also excrete Cryptosporidium; 3 ) a lack of species-specific data from clinical studies which might guide vaccine development efforts . In this paper , we utilized datasets and laboratory tests that allowed these obstacles to be overcome . GEMS-1 , pursued in representative sites in 7 developing countries , documented Cryptosporidium as a leading cause of endemic childhood diarrhea of a severity that brings children to healthcare facilities , particularly during the first 24 months of life [10] . Importantly , GEMS-1 utilized rigorous standardized clinical , epidemiologic and laboratory methods to collect extensive data over several consecutive years in 7 sites . By including matched control children without diarrhea and adjusting for mixed infections with other enteropathogens , GEMS-1 quantified the specific role of Cryptosporidium in childhood diarrheal disease beyond the background carriage of Cryptosporidium [10 , 23] . Finally , our results represent the first systematic , multisite , geographically-diverse assessment of the species-specific burden of Cryptosporidium-associated pediatric diarrhea , unequivocally corroborating the dominance of C . hominis [7 , 35–38] in infants and toddlers at all sites and revealing that 92% of C . parvum infections were due to recognized anthroponotic subtypes [39–41] . Despite our cautious extrapolation strategy , we found a substantial disease burden of ~7 . 6 million diarrhea cases annually attributable to Cryptosporidium , including ~2 . 9 million in SSA and ~4 . 7 million in the I/P/B/N/A region . Our estimated annual number of Cryptosporidium-attributable diarrhea cases ( 3 . 5 million ) among Indian children aged <24 months falls within the lower limit of the burden estimated by Sarkar et al . [31] . GEMS-1 [10] and other studies [8 , 13 , 42] have demonstrated a negative impact of Cryptosporidium-associated diarrhea on linear growth ( stunting ) , a nutritional insult that increases the risk for severe or fatal outcomes [43 , 44] . The Malnutrition and Enteric Infections ( MAL-ED ) study prospectively followed birth cohorts in Peru , Brazil , Tanzania , South Africa , Pakistan , Bangladesh , Nepal and India with twice-weekly household visits through age 24 months [45] , thereby detecting mostly mild diarrheal episodes typically not observed in healthcare facility-based passive surveillance . Thus , MAL-ED provides data on the etiology of milder diarrheal illness and revealed Cryptosporidium to be the fifth most important diarrhea-associated pathogen in the first year of life and seventh most important in the second year of life [45] . Regional burden estimates that we calculated did not incorporate the burden of milder clinical forms of Cryptosporidium-associated illness detected by active-surveillance household visits , as in MAL-ED , and thus probably under-estimates total burden . The two models of the death burden in children with Cryptosporidium described in this manuscript demonstrated that among the 4 African sites MSD cases infected with Cryptosporidium at enrollment had a significantly increased ECFR during the subsequent ~60-days [10] compared to the risk of death in controls and to the risk of death in Cryptosporidium-negative diarrhea cases . ECFR in children with Cryptosporidium in the African sites was particularly driven by Mozambique ( high HIV prevalence ) and rural Gambia ( low HIV ) , suggesting that factors other than HIV infection , such as malnutrition , play a role in Cryptosporidium-related deaths in SSA . While overall mortality during the 60-day follow-up was much lower in the Asian sites [10] , nevertheless our estimates indicate a substantial Cryptosporidium-related death burden because of the enormity of the <2 years population . Our GEMS-based estimates of deaths under 24 months attributable to Cryptosporidium diarrhea are greater than the estimates reported by GBD ( 35 , 200 deaths ) [33] and CHERG ( 12 , 000 ) [34] among children age <5 years . Discrepancies between GBD and CHERG estimates of <5 years diarrheal disease mortality are recognized [18 , 46 , 47] . Our estimates of total diarrhea-attributable deaths ( 455 , 000 and 254 , 000 in the SSA and I/P/B/N/A regions , respectively ) are somewhat larger than estimates for children <5 years of age for 2011 in a recent review [48] . In large part these differences are likely because the GEMS estimates are uniquely based on follow-up information on deaths among laboratory-diagnosed Cryptosporidium-associated diarrhea cases , whereas CHERG and GBD estimates are based on deaths that occur acutely . Our observational study design does not permit definitive determination of the direct causation between Cryptosporidium-positivity and deaths in young children . Nonetheless , the GEMS-based findings corroborate other results from West Africa [9] highlighting Cryptosporidium as a very clear strong signal for children at increased risk for death . From a public health perspective , this is sufficient to plan interventions aimed at reducing the risk of death in such high-risk groups . We observed a general age-specific pattern of Cryptosporidium infection and a strong association with MSD , with documentation of exposure in the first few months of life ( in both cases and controls ) , a peak adjusted Attributable Fraction at age 6–11 months and a decrease thereafter ( Table 3 ) . We interpret this as reflecting a time-limited ( first 5 months of life ) , passive protection mediated by maternally-transferred serum IgG as well as secretory IgA antibodies and other protective components of breast milk , despite exposure to the pathogen . Over the first two years of life , the prevalence of Cryptosporidium positivity in the controls remains impressively static documenting continuing exposure . However , beginning at ~6 months of age , clinical episodes of Cryptosporidium-associated diarrheal illness become more common and continue through age 23 months . By ~24 months of age these clinical and subclinical infections appear to induce in most toddlers acquired active immunity against further Cryptosporidium clinical illness . Support for this interpretation comes from a cohort study of Bedouin children in Southern Israel , a population under transition [49] . Serum IgG and IgM antibodies to a Cryptosporidium oocyst lysate were measured in children ranging from neonates to toddlers age 23 months [49] . High geometric mean titers ( GMT ) of serum IgG antibodies ( of presumed maternal origin ) were recorded at birth . GMT then decreased gradually until age 6 months , after which it increased progressively , as did the incidence of diarrheal illness and detection of Cryptosporidium in stools [49] . Collectively , these observations can be interpreted as indicating that immunity against Cryptosporidium develops following natural exposure to the pathogen . Measurements of serum anti-gp15 antibody and clinical and sub-clinical infections were also monitored in a cohort of infants and toddlers in Vellore , India . Children who lacked anti-gp15 antibodies just before weaning had higher rates of Cryptosporidium infections ( 77% ) than seropositive children ( 59% ) , although with the relatively small numbers in the cohort the difference did not reach statistical significance ( p = 0 . 076 ) [50] . Studies of infection-derived immunity in gnotobiotic piglets further support the notion of acquired immunity to C . hominis , as an initial induced C . hominis gastroenteritis in piglets significantly protected them against subsequent re-challenge with C . hominis [51] . Our documentation of the predominance of C . hominis over C . parvum as a pathogen for human infants implies that vaccine development research should prioritize protection against this species and against anthroponotic ( human host-restricted ) subtypes of C . parvum . The lower incidence of Cryptosporidium disease in infants <6 months old provides a window wherein multiple spaced doses of a future vaccine administered to infants may elicit protection for the subsequent increased risk of clinical Cryptosporidium disease encountered from age 6 to 23 months . Our study has three obvious limitations . While we have extensive data on MSD cases from 7 sites over 4 years , we have only 1-year enrollment of LSD cases from 6 sites . Thus , estimates on children with LSD are less robust than for MSD . Second , we generated pooled estimates of disease incidence and mortality for SSA and I/P/B/N/A and assumed each to be representative of those regions . However , until locally-representative , standardized data become available , our approach is warranted . That Cryptosporidium appeared important as a pathogen in both urban and rural , high and low HIV settings , provides evidence that broad extrapolation is justified . Third , the species of a small fraction of Cryptosporidium-positive specimens remained unresolved because , although they were positive by EIA and PCR , we could not amplify the long fragments of DNA necessary for species-specific sequence determination of samples mostly with lower parasite load ( average 18S PCR Ct 30±4 versus 21±5 for speciated samples , Mann-Whitney U test P<0 . 001 ) . That said , non-hominis/non-parvum species appeared to be quite rare . As for the species , the anthroponotic IIc and IIe were predominant C . parvum subtype families in this study with a larger portion of IIe than often appreciated [39] . Similar findings on C . parvum subtypes have been described in India [36] . Of 32 C . hominis infections with GP60 typing data , the predominant subtype families were Ia , Ib , and Ie . Ia had more diverse subtypes [40 , 52] , while Ie subtype was exclusively A11G3T3 , consistent with previous reports [36 , 39] . IbA13G3 infections appear to be common in West Africa . We found these in Mali ( n = 4 , 2 in cases ) and Gambia ( n = 4 , all in cases ) , consistent with the high proportions previously seen in Ghana [40] . The observation that the C . parvum parasites associated with MSD of young children in developing countries represent a restricted anthroponotic subset of all C . parvum is important as it enhances our ability to better understand the epidemiology of cryptosporidiosis and helps direct our vaccine development efforts . Currently , there is little research to develop Cryptosporidium vaccines for humans and only one licensed drug , nitazoxanide , to ameliorate Cryptosporidium diarrhea in children [53] . However , nitazoxanide is currently not recommended for use in infants <12 months of age , exhibits little efficacy in HIV-infected hosts and evidence of efficacy from controlled pediatric trials is limited [53] . The sizable case and death burden of Cryptosporidium in the SSA and I/P/B/N/A regions where ~80% of global deaths among young children occur calls for governments , global policymakers , and funding agencies to invest in developing new tools ( e . g . , vaccines ) to prevent Cryptosporidium diarrheal illness and improved methods to diagnose and treat it , while also advocating increased access to improved sanitation and safe water . | Cryptosporidium is a protozoan that causes diarrhea and malnutrition in young children in developing countries , and is associated with diarrhea cases and outbreaks in developed countries . To date , limited information exists on the burden of Cryptosporidium diarrheal disease in sub-Saharan Africa and South Asia , where most diarrheal disease deaths occur . We estimated the burden of Cryptosporidium-diarrhea and associated deaths in these regions using data from the Global Enteric Multicenter Study ( GEMS ) . Cryptosporidium was associated with diarrhea mainly in children aged <24 months . Infections began in the first few months of life but clinical episodes of Cryptosporidium-associated diarrhea illness peaked at age 6–11 months . The annual number of Cryptosporidium-attributable diarrhea episodes was estimated at 2 . 9 and 4 . 7 million in children aged <24 months in sub-Saharan Africa and in the India/Pakistan/Bangladesh/Afghanistan/Nepal region of South Asia , respectively . In both regions combined , Cryptosporidium is estimated to contribute to approximately 202 , 000 deaths per year , and to ~59 , 000 more deaths in Cryptosporidium-attributable cases than if those cases had been negative for Cryptosporidium . Our study highlights the enormous burden attributable to Cryptosporidium in Africa and Asia , which underscores the need for developing vaccines and treatments to reduce this burden . | [
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] | 2016 | The Burden of Cryptosporidium Diarrheal Disease among Children < 24 Months of Age in Moderate/High Mortality Regions of Sub-Saharan Africa and South Asia, Utilizing Data from the Global Enteric Multicenter Study (GEMS) |
Identification of the potential donor ( s ) of human germline-derived cells is an issue in many criminal investigations and in paternity testing . The experimental and statistical methodology necessary to work up such cases is well established but may be more challenging if monozygotic ( MZ ) twins are involved . Then , elaborate genome-wide searches are required for the detection of early somatic mutations that distinguish the cell sample and its donor from the other twin , usually relying upon reference material other than semen ( e . g . saliva ) . The first such cases , involving either criminal sexual offenses or paternity disputes , have been processed successfully by Eurofins Genomics and Forensics Campus . However , when presenting the experimental results in court , common forensic genetic practice requires that the residual uncertainty about donorship is quantified in the form of a likelihood ratio ( LR ) . Hence , we developed a general mathematical framework for LR calculation , presented herein , which allows quantification of the evidence in favour of the true donor in the respective cases , based upon observed DNA sequencing read counts .
Estimates of the incidence of human twinning range from <8 per 1000 live births in Asia to >18 per 1000 live births in Central Africa [1] . This considerable geographic variation is mainly attributable to dizygotic ( DZ ) twinning and likely reflects the influence of social , environmental and genetic factors . The incidence of monozygotic ( MZ ) twins , by contrast , is rather constant at approximately 4 per 1000 live births world-wide [2] . MZ twins arise from a single zygote and therefore initially have the same genome , hence the layman’s term ‘identical’ twins . With every 1 in 250 males being a MZ twin , instances in which the presence of a genetic ‘clone’ can hamper forensic case work are more than a theoretical possibility . In fact , real life examples [3] include the 1999 case of a female student who was raped in Grand Rapids , MI , US . Five years later , DNA analysis led to the identification of a potential perpetrator , who happened to have a MZ twin brother , and both the likely candidate and his brother denied their involvement . In 2009 , Malaysia police in Kuala Lumpur arrested MZ twin brothers , one of whom was a drug driver caught in the act . When the case came to court , however , there was reasonable doubt as to which twin was involved , and both men walked free . The ostensible indistinguishability of MZ twins has also challenged the probative value of genetic testing in the context of paternity disputes . For example , in 2007 , a woman in the US gave birth to a child after she had had sex with MZ twin brothers . A DNA test identified both likely fathers with 99 . 9% probability but , owing to the nature of the genetic markers included , could not discriminate between the two men . In the end , one brother was ruled the biological father on the grounds of other circumstantial evidence . The coalescence of all cellular lineages in a single fertilization event is the basis of the generally held view that MZ twins are indistinguishable genetically . However , after the twinning event ( i . e . , after the splitting of the original embryo ) , cell divisions along the lineages of one twin can be assumed to occur independently of the cell divisions in the other twin , at least regarding the acquisition of de novo mutations . Therefore , given the number of cell divisions during embryonic development and the size of the human genome , there is a reasonable chance that any two tissue samples taken from MZ twins after birth may differ regarding the presence or absence of one or more post-twinning genetic alterations . The potential utility of this phenomenon for discriminating between the germlines of male MZ twins was highlighted in a previous thought experiment [4] , suggesting an 83% probability that an offspring of a MZ twin carries at least one germline mutation ( henceforth termed ‘variant’ ) that can be detected in his sperm sample , but not in that of his twin brother . This theoretical conjecture was corroborated empirically by Weber-Lehmann et al . [5] who carried out ultra-deep next generation DNA sequencing ( NGS ) and confirmatory Sanger sequencing in sperm samples of a MZ twin pair and a blood sample of a child of one of the twins . Five de novo single nucleotide substitutions were found ( first by NGS and then confirmed by targeted Sanger sequencing ) in the father and child , but not the uncle . Given the technical feasibility afforded by NGS , it is anticipated that genetic MZ twin discrimination will become a common forensic practice . At the point when such testing is used in civil or criminal cases , however , the genetic expert will be required to quantify the evidential value of the laboratory results . The likelihood ratio ( LR ) , which weighs the probability of the data under two alternative ( mutually exclusive ) hypotheses , is generally regarded as the most reasonable way to fulfil this requirement [6] . In the aftermath of the proof-of-principle report [5] , Eurofins Genomics and Forensics Campus have been requested , by court order , to undertake similar analyses to distinguish between the germlines of MZ twin brothers . Such cases involve either the assignment of one twin to a sperm sample collected in connection with a criminal offense , or a paternity dispute . Under each scenario , DNA from saliva of the twins is used for genetic testing , i . e . the reference material is from a different tissue source than the forensic evidence ( sperm or peripheral blood , respectively ) . Although this indirect approach may be less certain than a same-tissue comparison , most cases can be solved eventually because a sufficient number of discriminatory mutations are detected . As was noted above , however , reporting the experimental evidence must also include quantification of its probative value by way of calculating LRs . In the following , we describe and exemplify a newly developed mathematical approach to meet this demand .
In our mathematical considerations , we presume that the intended germline discrimination is based upon NGS data from saliva or blood of the MZ twins , labelled A and B , that were generated as described in the Lehmann-Weber et al . report [5] . The same data reasonably will have served to identify potentially discriminating variants prior to the genetic analysis of the cells that derived from the germline of one of the twins . These latter cells will comprise either a sperm sample or the paternal genomic complement of an offspring of that twin , genotyped by targeted Sanger sequencing rather than NGS for reasons of the relatively large amount of input DNA required and current costs . Following common practice , the evidential value of the genetic data is quantified by means of the LR of the two mutually exclusive hypotheses “the cells came from the germline of twin A” ( hypothesis A ) and “the cells came from the germline of twin B” ( hypothesis B ) . Thus , the possibility that the cells derived from the germline of a third man essentially was ruled out beforehand on the basis of external evidence such as , for example , sufficiently discriminating short tandem repeat ( STR ) profiles . Typically , DNA sequence analysis will reveal a number , n , of de novo mutations ( most commonly single base pair substitutions ) that are prevalent in both the germline-derived cells and the somatic cells of one twin but not , or in only very small amounts , in the somatic cells of the other twin . Other variants , particularly those found in only one sample , are not informative for germline discrimination and are therefore not considered any further . Moreover , since the rate of recurrent somatic mutation is of the order of 2 . 7×10−9 per base pair per mitosis [7] , our mathematical considerations will rest on the presumption that every discriminating mutation traces back to one , and only one , molecular event during the development of the twins ( and their germlines ) . Finally , we will assume that all discriminating variants arose before the embryo split to give rise to the twins . Although most , if not all , discriminating variants will have arisen post-twinning in reality , this assumption is nevertheless reasonable because it is conservative in the sense that it systematically favours the alleged carrier of a discriminating variant , say twin A . Except for the remote possibility of a recurrent event in the germline of ( non-carrier ) twin B , postulating that the mutation occurred in twin A after twining would automatically rule out the possibility that the ( variant-carrying ) germline-derived cells came from twin B . For theoretical reasons , it appears reasonable to assume that the ( unknown ) frequency , pX , k , of the kth variant among the germ cells of twin X follows a beta distribution with parameters αX , k and βX , k . In fact , the course of pX , k during embryonic development can be viewed as a realization of Polya’s urn model , where single balls are repeatedly drawn from an urn containing black and white balls , each time followed by the return of the same ball and another ball of the same colour to the urn . The analogy works by equating the division of a variant-carrying cell with drawing a black ball . Probability theory then tells us that , with time , the distribution of the relative frequency of black balls in the urn , and hence pX , k , converges to a beta distribution [8] . The beta distributions employed here to characterise pX , k result from Bayesian updates of a single prior , with parameters α and β , of the variant frequency at the end of the pre-twinning period . Updating is based upon the NGS read counts , vX , k and wX , k , of the variant and wild-type alleles , respectively , in the body tissue sample of twin X , i . e . the germline frequency of the kth mutation follows a beta distribution with parameters αX , k = α+vX , k and βX , k = β+wX , k . Reasonable initial settings of parameters α and β can be derived from a consideration of the branching process underlying early embryonic development . If the pre-twinning embryo has undergone a small number , m , of cell divisions , the frequency , p , at that stage of any post-fertilization mutation has expectation E ( p ) =m2m+2−4 ( 1 ) and variance Var ( p ) = ( 2m−12m−1−m22m−1 ) / ( 2m+4−16 ) . ( 2 ) For a detailed derivation of formulas 1 and 2 , see Materials and Methods . In >98% of cases , MZ twinning occurs before the end of the first week of pregnancy , and 25% of twinning events pre-date blastocyst formation at day 5 [2] . The rate of cell division during early human embryonic development is fairly constant , amounting to approximately one cycle per day [9] . We may therefore reasonably assume that , on average , a pre-twinning embryo has undergone 0 . 25· ( 1+2+3+4+5 ) /5+0 . 75· ( 6+7 ) /2≈5 . 5 cell divisions . Setting m = 5 . 5 results in E ( p ) = 3 . 11×10−2 and Var ( p ) = 1 . 80×10−3 . When these two figures are equated to the expectation , α/ ( α+β ) , and variance , ( αβ ) /[ ( 1+α+β ) · ( α+β ) 2] , of a beta distribution , we obtain α = 0 . 4895≈0 . 5 and β = 15 . 2510≈15 . Below , two types of scenarios requiring discrimination between the germlines of MZ twins will be considered , namely sperm sample donor identification and paternity testing . In line with our proof-of-principle report [5] , the data underlying the likelihood calculations in both instances will be assumed to comprise ( i ) the NGS counts of the discriminating variants as obtained in the somatic tissue samples from the twins and ( ii ) the corresponding genotypes as determined in the germline-derived cells by targeted Sanger sequencing .
Identifying the male donor of a sample of germline-derived cells is a common issue in forensic casework , arising in paternity testing and in many criminal investigations , particularly in sexual offenses . Our own experience shows that both instances may also include an alleged donor who has a monozygotic ( MZ ) twin brother , so that unambiguous donor identification by genetic analysis alone appears challenging , if not impossible . With the advent of high throughput , time- and cost-efficient NGS technology , DNA sequencing of individual human genomes has become feasible and , in fact , relatively easy . Thus , distinguishing between the genomes of MZ twins on the basis of post-twinning somatic mutations is practical . This possibility has been demonstrated amply in studies targeting nuclear DNA [11 , 12] or mitochondrial DNA from peripheral blood [13] . To our knowledge , however , successful discrimination between the germlines of male MZ twins has only been reported once before , namely by our group [5] . Donor identification among MZ twin brothers is complicated by the fact that many legal systems do not provide for the enforcement of semen donation . This limitation implies that the reference tissue used for the identification of potentially discriminating genetic variants ( usually blood or saliva ) differs from the target tissue ( sperm ) . The opportunity for discriminating mutations to occur under this constraint is thus limited to the narrow developmental window separating the twinning event from the migration of the primordial germ cells into the yolk sac . This time period comprises ≤15 cell divisions and an intermediate population bottleneck , so that the number of discriminating mutations detectable in somatic tissue ( i . e . blood ) is likely to be very small . In fact , in the majority of cases that have been worked on so far , only two such mutations were observed , a number that is in very good agreement with its theoretical expectation of 1 . 78 derived in the thought experiment [4] that primed the proof-of-principle study [5] and the present work . At first glance , one might assert that two discriminating variants seem inadequate because , normally , genetic trace donor identification or paternity testing requires consistent matching of genotypes of , for example , 10 or more short tandem repeat ( STR ) markers . In the setting considered here , those same STRs are applied first to reduce the potential candidates for comparison to essentially the MZ twins and , thus , exclusion is required of only one alternative candidate donor , not many candidates . Putting the possibility of a sample switch aside , in principle , a single discriminating variant would suffice to identify the source of the germline-derived cells in question . Owing to issues of sample and processing quality , however , the evidential power of the approach undoubtedly would be bolstered by the presence of a least two discriminating variants . Moreover , if these variants are located on different chromosomes , they can be assumed to be stochastically independent in the sense that the presence of one of the two underlying mutations in a given cell did not affect the probability of the presence of the other mutation ( see below ) . Under this assumption , the joint likelihood of hypothesis X , given the genetic data , equals the product of the variant-specific likelihoods and the LR will reach a size sufficient for robust decision-making by the court or jury . When reporting inclusionary results ( i . e . , matches or similar terminology ) , the genetic expert is usually required to quantify the evidential value of the data , and the LR is a generally accepted manner to do so . The underlying mathematical theory is well established for classical forensic applications but has not been developed yet for cases involving MZ twins . Therefore , we devised a formal framework for LR calculations by relating the unknown germline concentration of a genetic variant to its NGS read counts in somatic tissue , using Bayesian updating . Our approach is based upon the assumption that all discriminating variants found in the alleged twin arose before twinning , which is highly conservative because it allows for the low-level presence of each discriminating variant in the other twin without invoking ( highly improbable ) recurrent mutations . Moreover , we employed one and the same prior for updating the frequency distributions in both twins . This strategy can easily be misunderstood as being anti-conservative because the high saliva frequency of a discriminating variant in the alleged twin may seem to require that the prior for the non-alleged twin is adjusted upwards . This argument is invalid because the cells constituting the two post-twinning embryos result from sampling without replacement , not from sampling with replacement . A high variant frequency in one twin therefore suggests a low variant frequency in the other . Moreover , the variant-bearing cells likely cluster spatially in the pre-twinning embryo because they emerge from the continued duplication of neighbouring cells . Therefore , the prior distribution rather should be adjusted downwards for the non-alleged twin , if anything , and adopting identical priors is conservative . It should be noted that calculating likelihoods from updated beta priors alone implies that the LR is bound to converge to infinity with increasing sequence coverage . Formally , this represents a logical inconsistency because recurrent mutation in the germline of the non-alleged twin remains a possible explanation of the sequence data if one or more discriminating variants are rare or even lacking from his somatic tissue . For the level of sequence coverage pertinent in current real-world cases , this is not an issue because the beta-derived likelihoods are orders of magnitudes larger than the human germline mutation rate of 1 . 2×10−8 per base pair per generation [7] . Therefore , the model-based numbers clearly would dominate any more complex likelihood definition accounting for the possibility of recurrent germline mutation as well . However , this issue may be worth revisiting if and when advances in DNA sequencing technology indeed allow substantial increases of the sequence coverage . As was noted above , discriminating variants on different chromosomes usually may be assumed to be stochastically independent . It must be emphasized in this context that the validity of this assumption is not a matter of high or low population frequency of the variants , or of high site-specific mutation rates ( i . e . location of the variants in mutational hotspots ) ; even highly probably events can be independent . Instead , stochastic independence between variants could be violated if the mutation rate during embryonic development varied between cell cycles in genome-wide fashion . One conceivable mechanism by which such temporal clustering of de novo mutations may arise is exposition to an exogenous mutagen . In this case , however , a higher overall prevalence of novel mutations would be expected to be detected in the twins . In the cases that we have worked on so far , however , the sequencing results did not show any indications of such an increase . In conclusion , NGS has rendered genetic discrimination between the germlines of MZ twins a realistic option , fit for practical forensic casework . The few but important somatic mutations that arise early on during the development of twin embryos can now be identified with justifiable effort . Although the experimental work required in connection with such cases may have been relatively expensive to date , the costs of NGS are likely to decrease in the future . More so , our novel read count-based method of LR calculation provides a simple means to quantify the residual uncertainty about donorship in a highly conservative and , therefore , mutually acceptable way . The current prevalence of MZ twin births [2] implies that , in ~1% of crime cases or paternity disputes , standard forensic DNA typing may turn out inadequate to resolve the potential donors . From now on , however , most cases implicating one or the other MZ twin can be successfully addressed genetically . Moreover , by highlighting the discriminatory power afforded by NGS in the special case of MZ twins , this and previous work [4 , 5] should also invigorate use of this technology in other forensic contexts such as , for example , the hitherto cumbersome kinship analysis of distant relatives . Whilst the validity of the statistical model underlying our work may occasionally require reconsideration , depending upon individual circumstances , it should represent a scientifically sound , simple and viable basis for the mathematical workup of practical cases . To put the approach in perspective , we refer to the famous quote from British statistician George E . P . Box: “Since all models are wrong , the scientist cannot obtain a ‘correct’ one by excessive elaboration . On the contrary , following William of Occam he should seek an economical description of natural phenomena . ” [14]
The present work was motivated by the requirement to analyse genetic data generated on official order by investigating prosecutions or family courts . All individuals affected in such cases are invariably informed about the reason and scope of the analyses . Neither the genetic data nor the analysis results are disclosed to third parties . In order to avoid ethical conflict , the authors and the editors of PLoS Genetics therefore agreed that no genetic data or other details from real forensic casework are publicized in connection with the present work . Our mathematical derivations start out from a zygote carrying two identical alleles at the ( autosomal ) genetic locus of interest . During each round of cell division , the number of alleles present in the developing embryo is doubled , so that a variant arising from a de novo mutation during the kth cell cycle has frequency 1/2k+1 among the 2k+1 homologous chromosomes present in the daughter cells . Moreover , the kth cycle comprises the synthesis of 2k+1 nascent chromosomes , each representing a target of possible mutation . Let p be the frequency of a variant that originated from one of the first m cell cycles . The above considerations imply that , after the mth cycle , P ( p=12k+1 ) =2k+1∑i=1m2i+1=2k∑i=1m2i=2k−12m−1 for 1≤k≤m . This leads to E ( p ) =∑k=1m12k+1⋅2k−12m−1=12m−1⋅∑i=1m122=m2m+2−4 for the expected value of p , and Var ( p ) =∑k=1m122k+2⋅2k−12m−1−m216 ( 2m−1 ) 2=∑k=0m−112k+4⋅12m−1−m216 ( 2m−1 ) 2 =116 ( 2m−1 ) ⋅[∑k=0m−1 ( 12 ) k−m22m−1]=116 ( 2m−1 ) ⋅[2m−12m−1−m22m−1] for the variance of p . | In many instances of practical forensic casework , particularly when connected to sexual assault , genetic analysis is carried out to identify the likely donor of a sperm sample left at the crime scene . The experimental and statistical methodology for such investigations is well established . In cases involving monozygotic ( MZ ) twin suspects , however , the procedure is hampered by the fact that the two individuals usually coincide for the genetic markers tested . One way to overcome this problem is to use the latest DNA sequencing technology to undertake a genome-wide search for those few mutations that occur during early embryonic development and hence allow distinguishing between MZ twins in later life . Following this approach , the first cases of criminal sexual offense have been worked on successfully by Eurofins Genomics and Forensics Campus , leading to the identification of sperm sample donors from saliva reference samples taken from MZ twin suspects . As a matter of principle , however , the residual uncertainty of the experimental results needs to be evaluated and reported as well . Therefore , we developed a novel mathematical framework to quantify the evidential power of the genetic data in cases attempting to identify MZ twin donors , based upon comprehensive DNA sequencing . Moreover , we demonstrate that the same mathematical method can be used to resolve paternity disputes involving alleged fathers who have MZ twin brothers . | [
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] | 2018 | Distinguishing genetically between the germlines of male monozygotic twins |
The mosquito-borne dengue virus ( DENV ) is a cause of significant global health burden , with an estimated 390 million infections occurring annually . However , no licensed vaccine or specific antiviral treatment for dengue is available . DENV interacts with host cell factors to complete its life cycle although this virus-host interplay remains to be fully elucidated . Many studies have identified the ubiquitin proteasome pathway ( UPP ) to be important for successful DENV production , but how the UPP contributes to DENV life cycle as host factors remains ill defined . We show here that proteasome inhibition decouples infectious virus production from viral RNA replication in antibody-dependent infection of THP-1 cells . Molecular and imaging analyses in β-lactone treated THP-1 cells suggest that proteasome function does not prevent virus assembly but rather DENV egress . Intriguingly , the licensed proteasome inhibitor , bortezomib , is able to inhibit DENV titers at low nanomolar drug concentrations for different strains of all four serotypes of DENV in primary monocytes . Furthermore , bortezomib treatment of DENV-infected mice inhibited the spread of DENV in the spleen as well as the overall pathological changes . Our findings suggest that preventing DENV egress through proteasome inhibition could be a suitable therapeutic strategy against dengue .
Dengue has emerged to be the most important mosquito-borne viral disease globally . An estimated 390 million infections occur annually while another 3 billion people that live in or travel to the tropics are at constant risk of infection with any of the four dengue virus ( DENV ) serotypes [1] . While the effort to develop a licensed vaccine appears to have taken significant strides recently [2 , 3] , whether vaccination can produce long-lasting protection against all virus serotypes remains to be determined . An important consideration is whether vaccination can avoid antibody-enhanced infection that is epidemiologically associated with increased risk of severe dengue [4 , 5] . Consequently , effective antiviral therapies against dengue would not only address disease burden imposed by dengue , it would also be useful in vaccinated populations should vaccine failure occur . Antiviral therapies must also be effective against both primary and secondary infections; the latter may be enhanced by the presence of heterologous antibodies and is associated with increased risk of severe disease . A rapid approach to therapeutic development is to repurpose existing licensed drug [6–8] . Indeed , DENV relies on host factors to supplement their relatively simple genome [9–12] . Hence , drugs that inhibit critical host factors could effectively stall the completion of the virus life cycle . Functional genomic screens as well as basic and clinical studies has identified several important host factors in the ubiquitin-proteasome pathway ( UPP ) [13–15] . This pathway is an attractive target for several reasons . Firstly , drugs that inhibit function of the proteasome , a major player of the UPP , have been licensed for therapeutic use . Secondly , genes in this pathway have been found to be differentially expressed during DENV infection [13 , 14 , 16] and serve as flaviviral replication promoting factors [10 , 11] . Thirdly , pharmacological inhibition of the UPP , such as proteasome inhibition [13] or interference with the ubiquitin E1 activity [14] has been shown to reduce DENV production significantly , in vitro . However , how the UPP serves to promote DENV replication remains ill defined . One possibility is that proteasome inhibition blocks DENV entry via endocytosis [11 , 17] , which is dependent on ubiquitylation [18] . However , this process may be cell-type dependent [19] . Furthermore , when opsonized with non- or sub-neutralizing levels of antibody , DENV is also able to bypass endocytosis and enter monocytes , macrophages , and dendritic cells , which are the primary targets of DENV via proteasome independent Fc receptor-mediated phagocytosis [20] . Thus , if proteasome inhibition only inhibits viral entry , it would not be a suitable therapeutic strategy for antibody-enhanced DENV infection . In this report , we investigated if proteasome inhibition can inhibit other parts of the viral life cycle besides viral entry . Using antibody-dependent infection of monocytic cells as a tool to bypass viral entry via endocytosis , our data suggests that a functional UPP is required for DENV egress . Finally , we demonstrate in vitro with primary monocytes and in vivo with a mouse infection model that virus replication is exquisitely sensitive to proteasome inhibition . Such a therapeutic approach may apply to other viruses that rely on a functional proteasome to complete their life cycle .
To elucidate the role of the proteasome on DENV2 replication , we took advantage of a subclone of THP-1 human monocytic cells for our investigations [21] . Importantly , inhibition of the proteasome could potentially inhibit virus entry via endocytosis [11] . This potential confounder can be bypassed by opsonizing DENV with enhancing levels of antibody where virus entry via proteasome independent Fc receptor-mediated phagocytosis can occur [20] . To demonstrate that proteasome inhibition with clasto lactacystin β-lactone ( β-lactone ) , a widely used proteasome inhibitor , indeed did not alter virus entry at non-toxic levels ( Fig 1A ) , we measured DiD ( 1 , 1'-dioctadecyl-3 , 3 , 3' , 3'-tetramethylindodicarbocyanine , 4-chlorobenzenesulfonate salt ) -labeled DENV in β-lactone treated cells . DENV2 opsonized with enhancing levels of humanized 3H5 monoclonal antibody ( h3H5 ) was added to a THP-1 subclone with increased susceptibility to antibody-dependent infection [21] . Cells were pretreated with β-lactone or DMSO as a vehicle control . Results showed that the proportion of DiD-positive cells were similar to that of DMSO control ( Fig 1B ) . As a positive control , we treated cells with genistein , a specific tyrosine kinase inhibitor that blocks Fc receptor-mediated phagocytosis [22 , 23] . Genistein treatment significantly reduced uptake of h3H5-opsonized DENV in a concentration-dependent manner ( Fig 1B ) [24] . Similar observations were made with confocal microscopy where Alexa Fluor 647 labeled DENV [25] opsonized with h3H5 were internalized in cells pre-treated with DMSO or β-lactone , while genistein prevented the uptake of virus at 2 hours post infection ( hpi ) ( Fig 1C ) . Altogether , these data indicate that any antiviral effect observed during β-lactone treatment is independent of DENV entry . While no difference was observed in the proportion of cells infected with fluorophore-labeled DENV2 , the outcome of infection was significantly different . Treatment with β-lactone resulted in a significant dose-dependent reduction in infectious DENV2 titers , measured by plaque forming units ( PFU ) ( Fig 1D ) despite no observable decrease in viral genomic RNA levels 48 hpi ( Fig 1E ) . Correspondingly , the ratio of PFU to intracellular DENV2 genome decreased after β-lactone treatment ( Fig 1F ) . These findings suggest that proteasome inhibition does not inhibit antibody-dependent infection but instead decouples infectious virus production from DENV2 genome replication . The observed decoupling of infectious DENV production from RNA replication in β-lactone treated cells has three possible explanations . Firstly , newly formed DENV were released from the cells , but as immature and non-infectious particles . Alternatively , normal proteasome function may be critical for either virus assembly or egress from infected cells . To test the first possibility , we measured viral RNA in the cell culture supernatant by qRT-PCR and compared it with the amount of infectious virions [26] . Ratio of viral genomic RNA to PFU showed no statistically significant difference regardless of the drug concentration used ( Fig 2A ) , indicating that treatment with β-lactone did not result in the release of proportionately more immature DENV compared to DMSO treated cells . To test whether UPP is essential for virion assembly or egress , we measured the amount of viral proteins or particles in infected cells . Results showed a dose-dependent increase in the DENV E protein with no change in NS3 protein on a western blot in β-lactone treated cells 24 hpi ( Fig 2B ) . Confocal microscopy also showed similar accumulation of structural proteins , E and prM in β-lactone compared to DMSO treated cells at 24 hpi with strong co-localization with Golgi markers ( Fig 2C ) . Electron microscopy revealed 50 nm viral particles in intra-cytoplasmic vacuoles in both β-lactone or DMSO treated cells , suggesting that DENV RNA could replicate , and be translated and packaged with structural proteins to form virions ( Fig 2D ) . To test quantitatively the possibility that packaged virions accumulate in the cell upon proteasome inhibition , we harvested β-lactone or DMSO treated cells at 2 , 4 , 6 , 18 and 24 hpi . The cells were lysed and treated with RNase to remove any RNA , except those packaged into virions ( S1A and S1B Fig ) which were then measured using qRT-PCR . The ratio of viral genome copy equivalents measured with or without RNase treatment provided an indication of the proportion of viral genome that was packaged into virions . In DMSO control , the proportion of packaged viral RNA peaked at 6 hpi and declined thereafter , consistent with the notion that DENV egresses from cells upon completion of replication ( Fig 2E ) . With β-lactone treatment , however , the proportion of packaged viral RNA remained elevated up till 24 hpi ( Fig 2E ) . Collectively , these results suggest that the UPP is critical for DENV egress from the host cell for subsequent rounds of infection . DENV completes its life cycle by egressing from host cells through exocytosis modulated by the exocyst complex . EXOC7 , a component of the exocyst complex is involved in the docking of exocytic vesicles with fusion sites on the plasma membrane via interaction with TC10 , a Rho GTPase [27] . More recently , EXOC7 has been shown in particular to be critical for DENV egress [28] . We thus investigated the effects of proteasome inhibition on the expression levels of EXOC7 , its effector , TC10 , and its interacting partner , EXOC1 . Our results indicate that transcript levels of EXOC7 , TC10 and EXOC1 remained constant after proteasome inhibition ( Fig 3A ) . Interestingly , the levels of the corresponding proteins decreased moderately in β-lactone treated cells , under both DENV2-infected and uninfected conditions ( Fig 3B ) . The differences in protein levels of EXOC7 and TC10 between DMSO and β-lactone treated DENV-infected cells were statistically significant when measured by flow cytometry ( Fig 3C ) . A mechanism that is known to repress translation is the ER stress-induced eIF2α-mediated translational repression of cellular mRNA . eIF2α is an effector of the PKR-like ER kinase ( PERK ) pathway in the UPR ( unfolded protein response ) and phosphorylation of this protein prevents GDP-GTP exchange on eIF2α by the guanine nucleotide exchange factor eIF2B , thereby inhibiting recycling of the ternary complex that contains the initiator methionine Met-tRNAi [29–31] . Consequently , global translation initiation , including that of EXOC7 and TC10 , is decreased . We thus tested if proteasome inhibition could activate the PERK pathway . eIF2α phosphorylation was assessed in β-lactone treated THP-1 cells with DENV2 infection . As expected , increased phosphorylation of eIF2α was observed in DENV2-infected β-lactone treated cells at 4 hpi ( Fig 3D ) . Up-regulation of the resident ER chaperone protein BiP , an ER stress protein , in DENV2-infected β-lactone treated cells was also observed at 4 hpi ( Fig 3D ) . These findings suggest the combination of DENV infection and proteasome inhibition may increase ER stress . If translational repression due to ER stress explains our observations , treatment of THP-1 cells with an ER stress agonist such as thapsigargin should lead to a similar reduction in EXOC7 and TC10 levels as well as DENV egress . Indeed , EXOC7 and TC10 levels in THP-1 cells showed a dose-dependent reduction with thapsigargin treatment whereas BiP level increased with increasing thapsigargin concentration , with no observable cytotoxicity to the cells ( Figs 3E and S2A ) . Likewise , although the virus genome was detected in thapsigargin treated DENV2-infected cells , no infectious DENV2 was detected using plaque assay ( Fig 3F ) . Taken collectively , our results suggest that UPP inhibition increases ER stress , which may trigger the UPR . Downstream activation of PERK can then attenuate the translation of exocyst complex components , which may be required for dengue egress via exocytosis . To ensure that our findings are not limited to THP-1 cell line or the use of β-lactone , we also explored if bortezomib , a FDA-approved reversible proteasome inhibitor used to treat multiple myeloma and mantle cell lymphoma , could inhibit DENV egress in primary monocytes at doses that cause minimal cytotoxicity . The 50% cytotoxic concentration ( CC50 ) of bortezomib in primary monocytes is above 1 μM ( S2B Fig ) . DENV1-4 opsonized with enhancing levels of humanized 4G2 ( h4G2 ) monoclonal antibody was used to infect primary monocytes [21] . Indeed , although the virus genome was detected in bortezomib treated DENV-infected cells for all four serotypes of DENV , no infectious DENV was detected using plaque assay at higher concentrations of bortezomib ( Fig 4A–4D ) . Furthermore , pre-treatment of primary monocytes with bortezomib also inhibited replication of different low-passage clinical isolates of all 4 serotypes of DENV in a dose-dependent manner ( Fig 5A–5D ) . The maximal effective concentration of bortezomib that inhibited 50% of virus replication ( EC50 ) is less than 20 nM for each of these isolates . Bortezomib was also able to inhibit 50% of virus production of the attenuated strain of yellow fever virus , YF17D , at a concentration of 0 . 5 nM , suggesting the dependence of functional UPP is not limited to DENV but may also apply to other flaviviruses ( Fig 5E ) . Similar observations were also made when another proteasome inhibitor , epoxomicin , from which the licensed carfilzomib was derived , was used at concentrations well tolerated by primary monocytes ( Figs 5F and S2C ) . The ability of proteasome inhibition to prevent completion of the DENV life cycle at low nanomolar concentrations suggests that this class of drug could have therapeutic potential for dengue patients . To test this possibility , we adopted a recently reported immunocompetent animal model [32] to test the in vivo efficacy of bortezomib as an anti-dengue drug . C57BL/6 mice were chosen in this study as they display several signs upon DENV infection , such as thrombocytopenia and elevated hematocrit levels , which are consistent with those observed in human dengue cases . Importantly , these animals are also immunocompetent , which could be necessary to deal with DENV entrapped in infected cells due to inhibition of egress . We treated C57BL/6 mice infected with DENV2 with a single dose of bortezomib at 6 hpi . The dose was based on that licensed for the treatment of multiple myeloma . The spleen was chosen for analysis since previous work has shown that this animal model produces no detectable viremia but the viral load in the spleen correlated with the degree of plasma leakage [32] . Using immunohistochemistry , we observed that mice treated with bortezomib showed significantly reduced number of DENV infected cells in the red pulp of the spleen at 24 and 48 hpi compared to vehicle control ( Fig 6A and 6B ) . No difference in NS3-positive cells was observed at 72 hpi , which is consistent with previously reported data that this strain of mice clears DENV infection rapidly without intervention [32] . Consistently , a significant difference in viral RNA genome copies was also observed at 48 hpi in bortezomib treated cells ( Fig 6C ) . Both observations suggest that proteasome inhibition could inhibit virus egress and hence spread in mammals . Along with reducing viral burden , bortezomib treatment also reduced the degree of thrombocytopenia ( Fig 6D ) and plasma leakage ( Fig 6E ) compared to control animals . Besides reducing the degree of change in platelet count and hematocrit , bortezomib treatment also reduced the previously observed inflammatory response in infected mice [32] . MCPT1 ( Mouse Mast Cell Protease-1 ) , an indicator of mast cell activation , was not detected in both serum and spleen in bortezomib treated mice compared to controls ( Fig 6F and 6G ) . Levels of TNF-α were also decreased in bortezomib treated mice compared to the vehicle control ( Fig 6H ) . These findings collectively indicate that bortezomib treatment is able to reduce DENV2 replication and the subsequent pro-inflammatory response , in vivo .
The UPP is a major extra-lysosomal pathway for regulated protein degradation , clearing misfolded or obsolete proteins and maintaining protein homeostasis . The proteasome is the main driver of the UPP as it recognizes and degrades polyubiquitylated proteins , modified via covalent attachment of ubiquitin through the sequential activities of E1-activating , E2-conjugating , and E3 ligase enzymes [33] . In this study , we show that proteasome inhibition does not prevent DENV replication but rather virus egress , in antibody-dependent infection of monocytic cells . Egress from the infected cell is perhaps one of the most ill-defined parts of the DENV life cycle . Previous work has demonstrated that this process occurs through exocytosis and is hence dependent on the expression levels of component of the exocyst complex [28] . The mechanism in which the UPP affects DENV egress remains to be determined . While there could be other mechanisms involved , our study raises the possibility that the expression of the exocyst component , EXOC7 , and its effector , TC10 , is regulated at the translational level , at least in part , by ER stress response . One explanation is that to ensure successful completion of its life cycle , DENV may rely on the proteasome to alleviate ER stress . Inhibition of the proteasome results in the accumulation of misfolded or obsolete proteins [34] , which induces the ER stress response [35 , 36] , triggering the PERK pathway in the UPR that represses translation of the exocyst components needed for exocytosis . In our study , although the levels of EXOC7 and TC10 decreased moderately in β-lactone treated cells , inducing ER stress with an agonist without inhibiting proteasome function recapitulated the observed down-regulation of EXOC7 and TC10 protein levels , along with the decoupling of infectious virus production from viral RNA replication . Supporting our data , salubrinal , a drug that inhibits eIF2α dephosphorylation , thereby increasing phosphorylated eIF2α levels was previously shown to reduce the production of infectious viruses [37] . While we have demonstrated the inhibition of virus egress as the antiviral mechanism effected by proteasome inhibition , it is interesting that this drug may have other modes of antiviral action . The UPP has also been shown to be critical for the life cycle of Nipah virus . Inhibition of the proteasome led to impaired nuclear export of the viral matrix protein to the cytoplasm [38] . The authors showed that a conserved lysine residue on this protein required mono-ubiquitylation for nuclear export . Concomitantly , depletion of free ubiquitin through proteasome inhibition inhibited the Nipah virus life cycle with exquisite sensitivity [38] . Likewise , studies on retroviruses have also demonstrated that disruption of the proteasome function depletes the free ubiquitin pool [39] , which is necessary for the ubiquitylation of late domain on Gag protein for proper viral budding [40 , 41] . While such mechanisms could also contribute to our observed inhibition of DENV replication , there is as yet no evidence that the function of any of the DENV proteins need to be activated by mono-ubiquitylation [14] . Although proteasome inhibition has been shown to exhibit anti-DENV activity in other cell types [13 , 14] , the effect of inhibiting endocytosis through proteasome inhibition appears to be cell strain specific , where HeLa cells from different laboratories respond to proteasome inhibitors differently with regard to viral endocytosis [11 , 19] . This may apply to the effect proteasome inhibition has on virus egress as well . However , we have focused our investigations on monocytic cells since this cell type has been shown to be important clinically in supporting DENV replication [42 , 43] . Likewise , post mortem histopathological analyses showed evidence of replicating DENV ( from expression of NS3 ) in hepatocytes and Kupffer cells in the liver and in macrophage-like cells in the spleen and lymph nodes . No virus was detected in endothelial cells in any organs examined [43 , 44] . We have focused our attention on the UPP as small molecule inhibitors of the proteasome have been licensed for use and could potentially be repurposed as a treatment for dengue . That proteasome inhibition could inhibit virus entry by both endocytosis , and egress by exocytosis would thus prevent both antibody-independent and antibody-enhanced infection . Indeed , the potency of proteasome inhibition as an anti-dengue strategy is suggested by the low nanomolar EC50 of bortezomib in DENV-infected primary monocytes . Similarly , bortezomib treatment in an immunocompetent mouse model was able to reduce both viral burden and pathological hallmarks of dengue . The potential of this drug to serve as an anti-dengue therapy needs to be explored in clinical safety and efficacy trials . Indeed , a known side effect of bortezomib is thrombocytopenia , although this is only observed in multiple myeloma patients after weeks of continuous treatment . As treatment for dengue would not exceed a week , the side effects observed only after prolonged therapy may not be relevant for dengue . One limitation , however , is that bortezomib is given subcutaneously or intravenously to patients . This is not ideal for any anti-dengue therapeutics , as injections are not recommended for dengue patients having the tendency to bleed . Opportunely , this problem can be circumvented by the recent introduction of ixazomib , the first oral proteasome inhibitor that is currently undergoing Phase 3 clinical studies [45] . Finally , another challenge that this and other antiviral therapies will face is how soon after the onset of illness must the drug be administered to produce the desired therapeutic effect . As the early features of dengue are mostly indistinguishable from other acute febrile illness , the remaining viremic period may be very short by the time a confirmatory dengue diagnosis is made . The effect of antiviral therapies may thus have minimal or even no efficacy at that point in the course of illness . Therefore , rapid point-of-care diagnostic tests that can reliably differentiate dengue from other causes of acute febrile illness should be prioritized for development to complement the advances in therapeutic development for dengue . In conclusion , our study provides new insights into the UPP plays in DENV infection , and suggests a potential therapeutic strategy against dengue by repurposing a licensed drug .
This study was carried out in strict accordance with the institutional ethical guidelines of the National University of Singapore ( NUS ) Government's ethical and animal experiments regulations . All experiments involving mice were performed in compliance with the guidelines of the institutional committee at NUS . The Institutional Animal Care and Use Committee ( IACUC ) approved the experimental protocol ( IACUC , Permit Protocol Number 2013–06157 ) . All efforts were made to minimize suffering . The guidelines followed by this Committee are based on the guidelines of Animal Welfare Act ( AWA ) and associated Animal Welfare Regulations ( AWRs ) and Public Health Service ( PHS ) Policy . THP-1 cells , BHK-21 , Vero and C6/36 cell lines were purchased from the American Type Culture Collection ( ATCC ) and cultured according to ATCC recommendation . Peripheral blood mononuclear cells were isolated from principal investigator’s blood using Ficoll-hypaque ( GE Healthcare ) and plastic adhered to obtain primary monocytes . The primary monocytes were then allowed to recover overnight before use in experiments . Different strains of DENV1 ( EDEN 872 , EDEN 2402 , EDEN 2928 , EDEN 3300 and PVP41 ) , DENV3 ( EDEN 803 , EDEN 863 and EDEN 2930 ) and DENV4 ( EDEN 2270 , EHI 69273 and EHI 16693 ) used in this study are clinical isolates from Singapore , while different strains of DENV2 ( ST , EDEN 3295 , PR2167 , PR8545 and PVP110 ) used are clinical isolates from Singapore and Puerto Rico . DENV was propagated as detailed in S1 File and infectious titer was determined by plaque assay as detailed in S1 File . THP-1 cells were pretreated for 1 h with DMSO or stated concentrations of β-lactone ( Sigma Aldrich ) or thapsigargin ( Sigma Aldrich ) before addition of DENV2 ( moi 10 ) opsonized with enhancing concentrations of humanized 3H5 antibodies ( 0 . 39 μg/mL ) . The mixture was then incubated for 20 min on ice to synchronize entry and infection was performed for 2 h at 37°C . The cells were then washed thrice in PBS to remove any inoculum that was not phagocytosed and cultured in maintenance media for another 46 h . Cells and supernatants were harvested for qRT-PCR using 3’UTR dengue primers and GAPDH as control , and plaque assay analyses . For bortezomib and epoxomicin experiment , primary monocytes were pretreated with stated concentrations of bortezomib diluted in PBS or epoxomicin diluted in DMSO and infected with DENV1-4 ( moi 10 ) opsonized with enhancing concentrations of humanized 4G2 antibodies ( 1 . 56 μg/mL ) . The supernatants were harvested at 48 h for plaque assay analyses . Western blot , confocal microscopy and electron transmission microscopy as detailed in S1 File was used to study DENV2 egress . DENV2-infected THP-1 cells were harvested at different time-points after infection . Two freeze-thaw cycles were performed to lyse the cells . The lysates were divided into two equal aliquots and to one set of the aliquots , 1 μ1 of RNase A/T1 Cocktail Enzyme Mix ( Ambion ) was added to degrade any host and unpackaged viral RNA . After incubation for 30 min at 37°C , viral RNA extraction ( QIAamp Viral RNA Mini Kit , Qiagen ) and qRT-PCR was performed to quantify the amount of packaged DENV2 in the cells . THP-1 cells were washed 3 times with PBS and fixed with 3% paraformaldehyde . The cells were then washed 3 times with FACS buffer consisting of PBS with 0 . 1% fetal bovine serum and permeabilized with 0 . 1% saponin in PBS . Cells were subsequently double-stained for the presence of DENV using anti-DENV complex monoclonal antibody , MAB8705 , and anti-EXOC7 antibody ( Abcam ) or anti-TC10 antibody ( Abcam ) before reading on BD LSRFortessa machine and analyzed with BD FACSDiva software . C57BL/6 mice were obtained from In Vivos company and maintained at MD2 facility of NUS . All experiments were performed in compliance with the guidelines of the institutional committees at NUS and Singapore-Massachusetts Alliance Institute of Technology ( SMART Centre ) . Briefly , eight to ten week-old male mice were inoculated with 1 x 106 PFU of DENV2 EDEN3295 intraperitonealy ( i . p ) as previously described in [32] . Six hours after DENV2 infection , mice were treated with a single dose of bortezomib ( 1 mg/kg ) via subcutaneous route ( s . c ) diluted in PBS solution . Vehicle mice received only PBS . Euthanasia was performed 24 , 48 or 72 h after DENV2 inoculation . During all time point , the hematocrit level and platelet count in whole blood were analyzed as detailed in S1 File . MCPT-1 quantification in serum and spleen , and TNF-α levels in spleen were performed using commercially available ELISA assays ( Ebioscience and R&D Systems , Minneapolis , MN , respectively ) in accordance with the manufacturer’s instructions . For viral load quantification , 30 mg of spleen was collected and stored in RNAlater stabilization reagent ( Qiagen ) at -20°C . Spleens were homogenized using stainless steel beads ( 5mm ) in Qiagen TissueLyser LT . The homogenate was collected , and viral RNA extraction for qRT-PCR was performed as detailed in S1 File . Immunohistochemistry analyses for detection and quantification of DENV infected cells in the spleen from the mice were performed . After euthanasia , spleen tissues were immediately fixed in 10% buffered formalin for 24 h and embedded in paraffin . Tissue sections ( 4 μm thicknesses ) were treated with 3% H2O2 diluted in Tris-buffered saline ( TBS ) ( pH 7 . 4 ) for 30 min . For antigen retrieval , tissue sections were immersed in citrate buffer ( pH 6 . 0 ) for 20 min at 95°C . For detection and quantification of DENV-infected cells in spleen , an anti-DENV NS3 MAb ( Gene Tex ) or an isotype control was used with a dilution of 1:50 at 4°C overnight in a humidified chamber . After incubation , tissue sections were washed with TBS and treated with a labeled streptavidin-biotin kit EnVision + Dual Link System-HRP ( Dako ) . Sections were then rinsed in PBS with 3 , 3′-diaminobenzidine tetrahydrochloride ( K3468 , Dako ) for 5 min and stained with Mayer’s hematoxylin . For quantification of NS3+ cells , cells counts were performed in 20 alternate microscopic high-power fields ( x 400 ) for each sample ( 4–5 mice per group ) . The number of positive cells in each field in the red pulp of spleen was counted and the mean calculated . Areas from the white pulp were excluded from analysis . All calculations were done using GraphPad Prism v5 . 0 ( GraphPad Software Inc . ) . | The lack of either licensed vaccine or antiviral drug has resulted in approximately 400 million dengue infections annually . A possible rapid approach to a specific therapeutic for dengue is to use a licensed inhibitor of a host factor critically required by dengue virus ( DENV ) to complete its life cycle . One such set of factors is in the ubiquitin proteasome pathway ( UPP ) . Despite the availability of licensed proteasome inhibitors , these studies have not led to any clinical translation , because the mechanism of action of this pathway on the virus life cycle is uncertain . We demonstrate that the UPP is critical for DENV egress after replication in human target cells . Intriguingly , treatment with the licensed proteasome inhibitor , bortezomib , inhibited the overall pathological changes in wild-type mice . Altogether , our study provides new insights into the role a functional UPP plays in DENV infection and suggests a potential therapeutic strategy against dengue by repurposing a licensed drug . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Proteasome Inhibition Suppresses Dengue Virus Egress in Antibody Dependent Infection |
The macaque brain serves as a model for the human brain , but its suitability is challenged by unique human features , including connectivity reconfigurations , which emerged during primate evolution . We perform a quantitative comparative analysis of the whole brain macroscale structural connectivity of the two species . Our findings suggest that the human and macaque brain as a whole are similarly wired . A region-wise analysis reveals many interspecies similarities of connectivity patterns , but also lack thereof , primarily involving cingulate regions . We unravel a common structural backbone in both species involving a highly overlapping set of regions . This structural backbone , important for mediating information across the brain , seems to constitute a feature of the primate brain persevering evolution . Our findings illustrate novel evolutionary aspects at the macroscale connectivity level and offer a quantitative translational bridge between macaque and human research .
Over a century of research has revealed that the brain is inhomogeneous and can be divided based on functional , macro- and micro- structural criteria [1]–[4] . The regions resulting from such a division are linked through fibre bundles that constitute the neural substrate for the exchange of information between the regions [5] . Early investigators highlighted the importance of the structural connections of a region to its functions , thus establishing the ground of structure-function dependencies and pinpointing the importance of brain connectivity for fundamental and clinical research [1] , [6] . In recent years , studies offered evidence for the close relation of structural connectivity and function in the mammalian brain [7]–[9] . Hence , regions with similar connectivity might be involved in similar functions , and large scale connectivity constitutes a guide to cognition [10] . Due to ethical and methodological reasons our most detailed knowledge of the brain originates from animal research . Specifically , the macaque brain serves as a model for the human brain , but such extrapolations might be inaccurate due to rewiring and/or expansion during primate evolution [11]–[13] masking out unique features of the human brain [14] . This has important consequences for translating macaque research to humans , which is valuable for cognitive , systems and clinical neuroscience . Hence , there is the need for examining if classical homology criteria such as similarity of connectivity patterns [15] , [16] are satisfied . Diffusion weighted magnetic resonance imaging ( dwMRI ) is used for the examination of the structural connectivity of the brain in vivo and for comparing the structural connectivity of the human and macaque brain [17]–[19] . However , up to date studies focus on a small subset of brain regions , examining particular fasciculi or lack direct quantitative interspecies comparisons . Hence , interspecies similarities and discrepancies of connectivity patterns and topological features at the whole brain level remain largely concealed . In addition , dwMRI is used for constructing in vivo the whole brain “wiring diagram” of humans , i . e . the human connectome [20] . Connectome analysis treats the brain as a complex network and employs tools from network science for unravelling key properties that are pivotal for its proper function and uncovering topological alterations related to mental disorders [21]–[24] . Recent work highlights key properties of the macroscale connectivity of the macaque brain [25] hinting at potential differences and similarities between the “connectome properties” of the two species , but with no explicit quantitative comparisons taking place . To complement and surpass limitations of previous comparative studies , we perform a direct comparative quantitative analysis of the macroscale connectional architecture of the macaque and human brain . We adopt a macroscale parcellation scheme called the Regional Map ( RM ) [4] , [26] and we construct whole brain species-specific connectomes , with the aid of dwMRI for the human and a neuroinformatics database for the macaque brain . Subsequently , we quantify the similarity of connectivity patterns , global topological features , and topology of the brain regions of the two species . This approach succeeds in uncovering preserved and divergent features of the macroscale connectional architecture of the brain of these two primate species .
For the whole brain examination of both species we employed a map specifically designed for this purpose , the RM [4] , [26] ( Fig . 1 A ) . This map consists of putative homologues between the two species based on structural , macroscopic and functional criteria . Its level of coarseness is dictated from the size of regions that are discernible in both human and macaque brains [4] . No connectivity criteria were used for the delineation of the various regions constituting the RM . The RM was delineated on the F99 standard brain which is based on an MRI scan of one macaque brain . Subsequently , the RM was morphed to match the human brain by using macroscopic and functional landmarks [27] , [28] . In total 82 regions ( 41 for each hemisphere ) constituted the RM that we used ( Table S1 ) . We should note that the use of the RM is necessitated by the lack of an unequivocal “standard” microstructure based map for even the brain of one species , let alone a “standard” microstructure based comparative map [29] , . Moreover , the regions constituting the RM are larger than regions defined based on e . g . cytoarchitecture , the so-called cortical fields , and one RM region might include various such cortical fields . This level of granularity of the RM was deliberately chosen in order to circumvent controversial issues with respect to macaque-human cortical field homologies across the whole brain , like the presence of more cortical fields in the human brain and/or duplication of certain cortical fields [4] , [29] , [30] , [31] . We used the RM and the CoCoMac database ( http://cocomac . g-node . org ) to assemble the whole brain connectome of the macaque . The database was accessed on December 2010 . Briefly , the CoCoMac database consists of entries describing the presence of a structural connection between two regions , as revealed by tracing studies , and have the format: region A has an efferent connection with region B . The database contains over 400 studies spanning several decades and thus represents a current best estimate of the macroscale connectivity of the macaque brain . Different researchers use different maps with divergent nomenclature . In order to link the different maps the database contains relation codes with the format: Region A of map X is identical to region B of map Y . Dedicated algorithms and algebra is used to map regions of one map to regions of a “reference” map [32] , [33] . In the current study , the RM functioned as the “reference” map and thus available connectivity information contained in the database was represented as an NxN connectivity matrix , where N = 82 the regions constituting the RM . A non-zero matrix entry Aij denotes the presence of a connection from region i and j . In order to compare the connectivity of the macaque and human brain ( see below ) , and since directionality of structural connections cannot be inferred in vivo in the human brain , the directed connectivity matrix of the macaque was symmetrized and binarized by taking into account all connections irrespective of their strength . The resulting macaque connectome ( MC ) consisted of 1857 undirected connections/edges between 82 regions/nodes . The binarization step is necessary since the connectomes of the two species were assembled from different modalities . DwMRI and tractography is not adequate for inferring density of connections [34] contrary to invasive tracing techniques in monkey studies . This limitation and the fact that certain network metrics employed for cross-species comparisons involve cross-matrix operations ( see below ) , do not allow the use of a weighted approach , since the weights obtained from the different modalities are not comparable ( see also Discussion ) . We aimed at examining key topological properties of the MC and HC . Below we introduce the various network metrics , defined at a region-wise or whole brain level , with relevant references . Given two matrices A and B , representing the MC and HC respectively , we computed the intersection network X defined as: ( 1 ) The number of edges Lx of the intersection network denotes the common edges/connections of the MC and HC . Lx divided by the total number of edges L ( = 1857 ) in each network A and B offers a measure of similarity of the two networks . Hence , the ratio Lx/L ranges from 0 to 1 , indicating no overlap and complete overlap respectively of the edges of networks A and B . We next procedeed to a region-wise analysis and sought to quantify the overlap of the connections of the assumed homologues of the MC and HC . This was performed with the Homologue Connectivity Similarity ( HCS ) metric: ( 2 ) The interspecies overlap/intersection of connections of region i in the macaque and human brain , represented by A and B respectively , is denoted by and the union with with denoting the set of neighbours of node i and the size of the set ( see Fig . 1 B ) . Hence , for each region i = 1…82 of the RM the HCSi ranges from 0 to 1 and indicates respectively low and high interspecies connectivity similarity of the assumed homologue region i . Subsequently , we quantified a “second-order” similarity of homologous regions , i . e . their connectivity similarity profile with the rest of the brain . To this end , we used the matching index [40] for A , denoting MC ( the same for B denoting the HC ) : ( 3 ) This resulted in one MI matrix for each species , with the row i capturing the connectivity similarity of region i with all the other brain regions of the same species . Hence , each row of each MI matrix can be conceived as a “connectivity similarity profile” of a region constructed for each species separately . In order to quantify if the connectivity similarity profile of putative homologous regions was preserved , we calculated the correlation between row i = 1…82 of the MI matrices ( without including the diagonal entries of MIA , MIB , , i . e no self-similarity values ) . This resulted in the Homologue Matching Index Similarity ( HMIS ) : ( 4 ) with r denoting Pearson's correlation coefficient of row i of the two MI matrices ( Fig . 1 C ) . Main aspects of the topology of the RM regions in the whole brain network , i . e . centrality and clustering , quantifying “importance” and “segregation” of regions [41] , were examined by calculating the betweenness centrality ( BC ) [42] , eigenvector centrality ( EC ) [43] and clustering coefficient ( C ) [44] . Segregation in this context implies a tightly interconnected neighborhood of a brain region , allowing “cross-talk” and exchange of information , while “importance” refers to highly central regions that are topologically ideal for information integration [45] . All these metrics were calculated for each species separately by using the formulas for binary undirected networks described in [45] , [46] . In order to assess perseverance of the centrality and clustering of the brain of the two species , Spearman's rank correlation between the same network metrics from the two species was computed . A statistically significant ( positive ) correlation would indicate the evolutionary perseverance of each network metric . Lastly we examined the presence of a rich club organization , indicating the presence of a “structural backbone” , quantified with the rich club coefficient ( RCC ) [47]: ( 5 ) where E>k denotes the number of connections/edges that exist among nodes/regions that exhibit more connections than a given number k and N>k denotes the number of nodes/regions that have degree higher than k , i . e . exhibit more connections than a given number k . The RCC is calculated for a range of k for a given network and for a number of randomized matched networks in order to estimate the RCC values expected by chance . This results in a normalized RCC: ( 6 ) Values higher than 1 for a range of values k indicate that the network is characterized by a rich club structure , with nodes with degree higher than k linked with more connections than expected by chance [47] . For each analysis , 10000 random networks , unless otherwise stated , matching each MC and HC in number of nodes , edges and degree distribution were created , with the use of a degree preserving algorithm [48] . The random networks were used for calculating p-values and z-scores of the network metrics . Thus , the random networks are used for obtaining “null” values for the metrics used . For the region-wise analysis , introducing multiple tests , we used a conservative Bonferroni correction . For verifying the robustness of the findings of the topology of the original MC and HC , we performed the following control analyses . First , we perturbed the MC and HC by rewiring the network with a low probability , i . e . 0 . 1 , while keeping the number of edges , nodes and degree distribution intact . That is , a pair of edges was swapped with 0 . 1 probability , thus introducing modest alterations to the network . Secondly , connections in empty positions were randomly and uniformly inserted . We inserted 10% of the initial number of connections , i . e . 1857*10% = 186 connections . Random networks for addressing the significance of the results in these “randomly enriched” MC and HC matched the new higher density . This type of analysis simulates in a simple way a scenario were previously absent connections appear to be present [49] . Techniques like the aforementioned ones were used for the examination of the robustness of features of the HC [21] . Additionally , we examined the effect of the choice of a particular parcellation by performing all the analyses on the connectomes in the exact same way , but assembled based on a different parcellation scheme [3] . Lastly , due to limitations of dwMRI-based local probabilistic tractography techniques in revealing long controlateral connections [24] , [38] and the lack of complete information on such connections in the CoCoMac database , we performed a within hemisphere analysis for the left and right hemisphere seperately . To this end , we constructed the MC and HC as previously described but for each hemisphere seperately . The hemisphere-wise MC appeared very dense ( left hemisphere:0 . 791 right hemisphere:0 . 792 density , compared to 0 . 559 for the whole brain connectome ) and the HC was thresholded accordingly . The very high density of the hemisphere-wise connectomes poses a problem for a binary analysis . This is due to the fact that many topological properties of the original MC and HC will not differ from their rewired counterparts because of inefficient “space” for rewiring . Taking an illustrative case for example , the rich club analysis will reveal dense connections between regions at increased levels of k but such strong interconnectivity can be merely explained by the very high density of the network and is not therefore not “surprising”/statistically significant ( for a similar discussion but a different direction see [50] ) . Therefore , we decided to adopt weighted networks and suitable weighted versions of the aforementioned metrics . As previously explained , the weights from the MC and HC are not comparable . Therefore , we restricted the weighted hemisphere-wise analysis to metrics that do not involve cross-matrix operations ( thus the intersection and HCS were not computed ) . The weighted version of the HMIS involved equation ( 4 ) but operating on the rows of a “weighted matching index” matrix obtained as the cosine similarity between rows i and j of matrix A , denoting MC ( the same for B denoting the HC ) : ( 7 ) The weighted version of EC , BC and C was computed as described in [45] . The weighted rich club can be formulated as follows [51]: ( 8 ) with W>k denoting the sum of the weights of the edges connecting nodes with degree higher than k and denoting the sum of the E>k first ranked ( in decreasing order ) edge weights in the whole network . For each analysis , 10000 random networks ( corresponding to the “link and weight reshuffle” model in [51] ) matching each hemisphere-wise MC and HC in number of nodes , edges and degree distribution were created , with the use of a degree preserving algorithm [48] . All network analysis was performed with the use of functions from the Brain Connectivity Toolbox ( https://sites . google . com/site/bctnet/ ) [45] and custom scripts written in Matlab ( Mathworks ) . The MATLAB code for the computation of the HCS is provided ( Software S1 ) . Brain renderings were performed with the following freely available software: Caret ( http://brainvis . wustl . edu/wiki/index . php/Caret:About ) and BrainGL ( http://code . google . com/p/braingl/ ) . For certain visualizations of the connections of the MC and HC a mean-shift edge bundling algorithm was used [52] .
Fig . 2 depicts the MC and HC and their intersection . Their intersection , i . e . the Lx/L ratio of the MC and HC was significant ( Lx/Loriginal = 0 . 754 p<0 . 001 , Lx/Lnull mean = 0 . 599 std = 0 . 006 , null values from 1000 random networks ) . Thus , the wiring of the macaque and human brain as a whole is more similar than expected by chance . The region-wise analysis of the HCS revealed significant connectivity preservation for many RM regions . Specifically , a set of frontal , occipital and temporal regions exhibited significant preservation of their whole brain connectivity across the species . Mainly parietal and cingulate regions appeared to lack such preservation ( Fig . 3 A , Table 1 ) . In sum , 51 out of 82 RM regions exhibited significant HCS and therefore communicate with a significantly overlapping set of brain regions in both species . The region-wise analysis of the HMIS revealed that 45 out of 82 RM regions , mainly involving frontal , temporal , occipital regions reached significance . Cingulate and parietal regions failed to reach significance ( Fig . 3 B , Table 1 ) . Hence , regions reaching significance seem to form the same “connectivity coalitions” , i . e . exhibit a statistically significant connectivity similarity profile with the rest of the brain regions in macaques and humans . This in turn can entail that their “functional coalitions” might also be preserved . Conversely , certain regions fail to reach significance and might suggest that “evolutionary rewiring” occurred in such a way that they formed distinct “connectivity coalitions” with the rest of the brain regions in the two species . The HCS and HMIS results involve distinct but also overlapping sets of regions ( Fig . 3 C , Table 1 ) . Thus , they illustrate converging but also diverging aspects of distinct connectional characteristics of the brain regions of the two species . The hemisphere-wise weighted HMIS analysis revealed broadly the same pattern with a notable difference: a subset of prefrontal and temporal regions did not reach significance ( Table S2 ) . Notably , even in this type of analysis cingulate regions failed to reach significance . Fig . 4 depicts the results of the centrality and clustering analysis of the MC and HC . In both species , a general pattern is discernible with regions in “association” cortex exhibiting the highest centrality and regions in “primary” cortex exhibiting the lowest ( Fig . 4 A B ) . Moreover , in both species the cingulate cortex appears as highly central ( Fig . 4 A B ) . Additionally , little overlap was observed between the macaque and the human brain when taking into account the regions that are highly central ( centrality>mean+1 std of the centrality of the RM regions ) ( Table S3 ) . It should be noted however , that bilateral posterior cingulate cortex ( CCp ) and left inferior parietal cortex ( PCi ) exhibited high ( >the mean+1 std threshold ) BC and EC in both species indicating the perseverance of the prominent central role of these regions . However , a region wise correlation of the BC and EC values across the species revealed a relative high but not significant correlation ( rho = 0 . 51 , 0 . 52 respectively p>0 . 1 ) . This might suggest that , at a whole brain level , there is a lack of perseverance of the topological importance of the assumed homologues of the macaque and human brain ( see also Fig . S1 A B for scatterplots of the BC and EC values from the two species ) . The C values for both species exhibited a “reversed” pattern with the centrality values: regions in “association” cortex exhibiting the lowest values and regions in “primary” cortex exhibiting the highest ( Fig . 3 C ) . The C values across the species did not reach significance either ( rho = 0 . 46 p>0 . 1 ) . Therefore , the regions of the brains of the two species seem to exhibit different levels of segregation ( see also Fig . S1 C for scatterplots of the C values from the two species ) . The hemisphere-wise weighted anlysis of EC , BC and C led to a comparable picture ( Table S4 ) with no significant correlation between these metrics in the two species . A significant RCC highlights the presence of a rich club organization in both the MC and HC ( Fig . 5 A , see also Fig . S3 ) in line with previous studies [22] , [53] . Importantly , our direct comparative analysis that employed a parcellation scheme applicable to both species demonstrates that the regions forming a rich club exhibit a high and significant overlap ( 14/20 ) , involving regions located at the frontal , parietal , cingulate and insular cortex ( Fig . 5 B C , Table S5 ) . This overlap is observed for a wide range within the rich club regime ( Fig . 5 C ) . This indicates that not only the macaque and human brain exhibits a rich club organization , but that this structure constitutes an evolutionarily preserved structural backbone involving a highly overlapping set of regions in both species . Since the regions constituting a rich club have a high degree , i . e . number of connections , and the degree is positively related to BC and EC [46] , it is expected that the rich club regions will have higher BC and EC values when compared to non-rich club regions . We directly tested this prediction and found that rich club regions in both the MC ( defining rich club and non-rich club regions by talking into account level k = 56 as a cutoff ) and HC ( defining rich club and non-rich club regions by talking into account level k = 55 as a cutoff ) exhibit significantly higher BC and EC values when compared to non-rich club regions ( p<0 . 001 , permutation tests ) . Moreover , comparing the C values of rich club and non-rich club regions revealed the reversed relation: the rich club regions exhibited significantly lower C values when compared to non-rich club regions ( p<0 . 001 , permutation tests ) . Hence , the regions of the “structural backbone” in both the MC and HC when compared with the rest of brain regions , appears highly central , further underlying their topological importance in mediating information across the brain . Moreover , they appear less segregated , indicating limited connections , and hence possible anatomical paths for “cross-talk” , between the regions that they connect to . Application of the weighted RCC to the left and right MC and HC seperately , led to similar results ( Table S6 S7 ) . One notable exception was the failure of the weighted rich club analysis to reveal a statistically significant rich club in the left HC . Hemispheric differences in network metrics have been reported [39] and this finding could signify a less prominent rich club structure in the left HC . However , given the high density of the network , and consequently a rather low cutoff used for considering connections in the HC to be taken into account , we suggest that this finding is the consequence of an inflated false positive rate obscuring the topology of the left HC . For the whole brain analysis involving binary networks , control analysis gave rise to the following picture: The Lx/L ratio on perturbed networks revealed that despite a slight decrease , as expected since scrambling of the networks was introduced , from the Lx/L ratio calculated between the original MC and HC , the Lx/L ratio remained significantly higher when compared to values obtained from random networks ( Lx/Lperturbed mean = 0 . 658 std = 0 . 003 , Lx/Lnull mean = 0 . 599 std = 0 . 006 , p<0 . 001 null values from 1000 random networks ) . The HCS , HMIS , and RCC analysis from networks derived from the perturbation analysis revealed that the results are robust ( Fig . S2 , S3 ) . This also held true for the control analysis of random insertion of connections . The usage of a different parcellation scheme [3] led to significant and converging results as the ones obtained for the RM ( Lx/Loriginal = 0 . 712 p<0 . 001 , Lx/Lnull mean = 0 . 558 std = 0 . 011 null values from 1000 random networks , see also Table S8 , S9 ) . The choice of a different parcellation scheme gave rise to significant and comparable results , albeit with less regions reaching significance ( Table S8 ) , something that might be attributable to the fact that this map was not “designed” to be applicable in both species . Hence , the above results conjointly underscore the robustness and relative independence of the results from parcellation scheme choices .
Cortical expansion of the human cortex in relation to the macaque is more prominent in prefrontal , parietal and cingulate regions [54] . Our results suggest different degrees of perseverance of the macroscale connectivity of these regions during primate evolution . An early view on the prefrontal cortex ( PFC ) suggests that it has been expanded in the lineage leading to humans [1] , [55] . Expansion of the human PFC relative to the macaque PFC is supported by contemporary investigations [13] and is linked to unique human cognitive processes [56] . Moreover , PFC connectivity changes have also been proposed to underlie unique cognitive processes in humans [11] . A recent review [57] as well as functional [58] and structural [19] connectivity studies suggest comparable connections of the PFC of the two species . Additionally , quantitative analysis has revealed similar connectivity of macaque and human PFC regions with a small set of cortical regions ( 17 ) . However , pronounced changes are reported for the arcuate and inferior fronto-occipital fasciculi of humans and macaques [19] , [59] . Our study suggests a statistically significant preservation of distinct aspects of the wiring of several PFC regions across the species ( Fig . 3 , Table S2 ) . Hence , unique features of the humans , i . e . “higher order cognitive processes/intelligence” attributed to the PFC , might not entail extensive reconfigurations of PFC connectivity in humans when compared to macaques . The parietal cortex in macaques and humans consists of distinct subregions that are discernible on functional , connectional , macro- and microstructural criteria [3] , [4] , [60] . Comparative studies reveal similarities but also some differences of the functional and connectional architecture of the parietal cortex subregions [60]–[62] . Our whole brain quantitative analyses offer complementary evidence by revealing that certain lateral parietal regions reach a statistically significant connectivity pattern similarity , while the medial parietal ones do not ( Fig . 3 , Table 1 S2 ) . This could entail a functional similarity of lateral parietal regions between the two species and a divergence with respect to the medial ones . The anterior cingulate cortex ( CCa ) exhibits extensive connections with parietal , motor , frontal , insular and limbic regions . Such connectivity renders it suitable for bridging the motivational , cognitive and motor domains [63] . Functional evidence in humans and macaques pinpoint such an integrative role and involvement in decision making [64] , [65] . CCa is highly central and part of the rich club ( Fig . 4 5 , Table S3 S4 S5 S7 ) a topological structural feature that might allow the involvement of this region in the aforementioned functional roles . Despite that CCa is part of the rich club in both species our results suggest a lack of preservation of its connectivity patterns ( Fig . 3 , Table 1 , Table S2 ) . This in turn might entail , alongside with potential preservance of certain functional properties , divergent functional roles of this region in the two species . The posterior cingulate cortex ( CCp ) is a major node of the default mode network in both species , also involved in processes such as social cognition [66]–[68] . In addition , recent evidence from a functional study in humans suggests that this region exhibits dynamic properties subserving the integration of information from regions of distinct large scale networks [69] . The fact that CCp is central and part of the rich club in both species ( Fig . 4 5 , Table S3 S4 S5 S7 ) might constitute the structural basis for such integrative property reflected in functional measurements . Consequently , we hypothesize that such property will also hold for the macaque . However , the CCp appears to have not retained its connectivity with the rest of the brain ( Fig . 3 , Table 1 S2 ) . Multimodal imaging of the macaque and human brain might be used to directly address if the aforementioned integrative functional property involving the CCp are common in the two species or a unique property of the human brain . Moreover , a possible rewiring of the CCp might have resulted in the reconfiguration of the neural circuitry , which seems also present in the macaque brain [68] , underlying aspects of social cognition in humans . A general pattern was discernable in both species: high BC and EC values were observed in “association” cortices and low ones in “primary” cortices . The C values exhibited the reverse pattern ( Fig . 4 , Table S4 ) . However , none of these network metrics appeared to persevere primate evolution , suggesting different levels of centrality and clustering at the whole brain level ( see also Fig . S1 ) . Neverthless , regions in the cingulate cortices appeared highly central in both species ( Fig . 4 A B , Table S4 ) in line with previous findings [24] , [53] . Hence , cingulate cortex regions , despite the evidence for a different “wiring” in the two species ( Fig . 3 , Table S2 ) , seem to have mantained their topological centrality , relevant for information integration . Our analysis demonstrates the presence of a rich club organization in both the MC and HC ( Fig . 5 , Table S5 , S6 , S7 , S9 ) confirming and extending previous findings [22] , [53] . Importantly , our comparative approach allowed us to demonstrate that the regions forming a rich club are highly converging with a significant overlap within the rich club regime ( Fig . 5 B C , Table S5 S7 S9 ) . Thus , this structural backbone is not only present in both macaques and humans , but also persevered through primate evolution , involving a highly overlapping set of regions in the two species . It is noteworthy , that the hemisphere-wise analysis failed to unveil a statistically robust rich club strucure for the left HC . We believe that this is due to an increased false positive rate . However , a “laterality” might be present in the HC with respect to rich club organization , a potentiality demanding further future elaboration . Network analysis in the macaque [53] and the human brain [70] revealed that the rich club connections are the most “costly” , i . e . span long distances , and mediate traffic between distant regions through a sequence of short-long-short range structural pathways . Studies in the human brain indicate that inter-regional functional interactions are modulated by connection distance and take place within specific frequency bandwidths [71] , [72] . Additionally , macaque studies suggest a frequency-specific dialogue between two cortical regions that depends on the laminar origin and termination of the inter-regional connections [73] . Our comparative analysis can guide invasive techniques for the functional examination of the rich club regions of the MC . Such investigation is crucial for assessing if and how the aforementioned factors co-shape the functional dialogue within rich-club and between rich club and non-rich club regions and thus highlight the principles that shape the flow of information through this structural backbone . Additionally , such functional investigation might unlock the mechanisms underlying the proposed role of rich-club regions in multisensory integration [74] . Our comparative approach helps translating such functional findings to the human brain and develop hypothesis that could be tested with e . g . electrocorticography . In that way , future studies could assess if “homologous rich club” regions exhibit comparable and/or unique functional properties in the two species . Lesions involving rich-club regions deteriorate the efficiency of the whole brain network and consequently can affect multiple cognitive domains as well as functional aspects like synchronization of functional networks [22] . The presence of a rich club structure involving highly overlapping regions in both MC and HC suggest that the macaque brain might be used as a model for e . g . studying the effects of lesions involving “homologous rich club” regions . However , certain common rich club regions , for instance CCp , lack significant interspecies connectivity similarity . Lesions in a brain region , apart from leading to the expected effects in regions directly connected to it , also lead to global effects through indirect connections [75] . Thus , if the wiring of the same lesioned regions differs , the lesion can lead to different global effects and consequently possibly different behavioural effects . The above conjointly , suggest that while lesioning common rich club regions will have detrimental global effects in both species , the nature and severity of such effects might depend on the degree of preservation of the connectivity of the involved regions . Both genetic and environmental factors underlie system-level changes , including connectivity , of the cortex of mammals [12] . For instance , functional connectivity differences observed between the inferior parietal lobule and anterior prefrontal cortex of macaques and humans can be the result of different foraging styles of the two species , dictated by different ecological factors which entail different challenges in decision making [65] . Our results revealed statistically significant connectivity similarities between humans and macaques while absence thereof might suggest a rewiring also caused by the aforementioned factors . In addition , inaccuracies of the methods used and data incompleteness might also give rise to connectivity discrepancies ( see Limitations and future directions ) . Thus , discrepancies might be attributed to “true” differences , methodological limitations and a mixture thereof . Both empirical and computational studies suggest that the connectivity of a region largely constrains its function [8] , [9] . We have demonstrated the perseverance of the connectional patterns of certain assumed homologues in the two species in a quantitative way . Obviously humans and monkeys differ in certain cognitive functions e . g . language production . Hence , one intriguing question is the extent to which such a statistically significant perseverance of connectivity similarity is translated to similarity of function persevering evolution . Other factors apart from macroscale connectivity can shape the functional role of a region , e . g . laminar patterns of connections [76] . Consequently , it could be the case that a statistically significant macroscale connectivity similarity of a region is not sufficient to guarantee evolutionary preserved functional similarity . In an analogous way , it has been demonstrated that the presence of an evolutionary conserved network can be accompanied by functional divergence [77] . Hence , while a statistical perseverance of macroscale connectivity suggests functional similarity , such a prediction demands explicit quantification in future studies . The network based methods employed in the current study in conjunction with data-driven methods for detecting cross-species functional homologies [78] could be adopted in future studies for addressing the degree of convergence and divergence of connectional and functional similarity across the brain regions of the two species . Certain limitations should be taken into account when interpreting the findings of our study . First , while the expansion model is used extensively for interspecies comparisons , evidence suggests the presence of interspecies functional correspondences not predicted by it [78] , [79] . To perform interspecies comparisons without using the expansion model , dwMRI and/or resting–state fMRI data collected in both species in conjunction with sophisticated techniques like network alignment [80] can be used for an interspecies connectivity based region-to-region match . Moreover , connectivity based parcellation strategies can be adopted for parcellating the cortical mantle in a data-driven fashion [58] , [61] without the need for an a priori defined parcellation scheme . This would allow addressing inter-species differences and similarities of connectional architecture at a more fine grained level going beyond the level of granularity currently adopted . However , performing connectivity based whole brain parcellation applicable to a comparative study remains challenging . Second , the MC was assembled through a meta-analysis of tracing studies , while the HC with the aid of dwMRI . Good correspondence exists between the structural connections as revealed by tracers and diffusion imaging [21] , [81] , [82] , but some inconsistencies are also discernible [38] . Hence , we predict that the usage of dwMRI for assembling the MC will lead to largely comparable results . Moreover , different weighting schemes for assembling the HC could also be adopted in the future [22] . Third , tractography methods have several limitations , like the limited detailed controlateral connectivity and the relation of false-positives and false negatives and connection distance , with longer connection distances appearing more prone to false negatives [38] . Hence , connections between distant regions might be underrepresented and might lead to lack of interspecies connectivity similarity ( for a discussion see [24] , [83] . Future studies employing the same modality for the estimation of connectivity in the two species , e . g . resting-state fMRI , will complement the current results . Forth , we currently used binary instead of weighted connectomes for the main analysis since certain network metrics currently employed ( e . g . HCS ) involve cross-matrix operations and the weights obtained from the different methods for assembling the MC and HC are not comparable . This restricted us from using all the metrics for the hemisphere-wise weighted analysis . Fifth , we compared the macroscale connectivity of the two species [20] . Apart from similarities and changes occurring at this level , connectivity changes between the species can occur at a mesoscale , i . e . connectivity at the laminar level [14] , [84] . Hence , a more complete understanding of interspecies differences requires quantitative comparative studies at multiple levels . Lastly , future incorporation of whole brain macroscale connectivity data from more primate species , e . g . apes , along with enrichment of existing connectivity databases and improvement of neuroinformatics tools [49] , [85] , will allow tracing the evolutionary trajectory of the primate brain in more detail . To this end , network metrics recently introduced for uncovering the structural backbone of the brain , such as core-periphery analysis [86] , could also be used as an alternative to the metric , i . e . rich-club , currently employed . We examined at the whole brain level the macroscale inter-regional structural connections of macaques and humans . While many similarities of the macroscale connectivity of the two species were observed , certain discrepancies were also present . This approach , which can be termed “comparative connectomics” , offers closer interspecies comparisons and brings forth novel insights into the evolution of the connectional architecture of the primate brain . Thus , it constitutes a translational bridge , valuable for clinical , cognitive and systems neuroscience , between macaque and human research . | What are the commonalities and differences of human brains when compared to the brains of other primates ? The brain can be conceived as a complex network . Its topological properties constrain its function . Ethical and technical reasons necessitate the use of animal brains , like the macaque monkey , as models for the human brain . However , evolutionary changes , including “brain rewiring” , might result in unique human features . Hence , a detailed and quantitative comparative analysis of the connectivity of the brains of the two species is needed . Here , we undertake this task by adopting techniques analogous to those used in comparative studies in other scientific fields . Our approach reveals converging but also diverging wiring patterns . The brain of the two species as a whole is similarly wired . The majority of the brain regions appear to have evolutionary conserved connectivity patterns while for certain regions this appears not to be the case . We also uncover an evolutionary conserved “structural backbone” in the brain of the two species . Our findings highlight common and unique “wiring properties” of the brains of these two primate species and offer a quantitative basis for translating findings from macaque research to human research . | [
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] | 2014 | Comparative Analysis of the Macroscale Structural Connectivity in the Macaque and Human Brain |
We investigated the mechanism of how the papillomavirus E2 transcription factor can activate promoters through activator protein ( AP ) 1 binding sites . Using an unbiased approach with an inducible cell line expressing the viral transcription factor E2 and transcriptome analysis , we found that E2 induces the expression of the two AP1 components c-Fos and FosB in a Brd4-dependent manner . In vitro RNA interference confirmed that c-Fos is one of the AP1 members driving the expression of viral oncogenes E6/E7 . Mutation analysis and in vivo RNA interference identified an essential role for c-Fos/AP1 and also for the bromodomain protein Brd4 for papillomavirus-induced tumorigenesis . Lastly , chromatin immunoprecipitation analysis demonstrated that E2 binds together with Brd4 to a canonical E2 binding site ( E2BS ) in the promoter of c-Fos , thus activating c-Fos expression . Thus , we identified a novel way how E2 activates the viral oncogene promoter and show that E2 may act as a viral oncogene by direct activation of c-Fos involved in skin tumorigenesis .
Papillomaviruses ( PV ) are small double-stranded ( ds ) DNA viruses that are able to cause epithelial tumors , including cancers of the cervix uteri and the oropharynx and are likely to be involved in the development of non-melanoma skin cancer [1] . Expression of viral oncogenes E6 and E7 that dysregulate the cell cycle via direct interaction with the tumor suppressor proteins p53 and pRb , respectively , is controlled by the viral protein E2 . Earlier results from us have shown that mutations in conserved amino acids of the trans-activation domain of E2 , which is a regulator of viral transcription and replication , dramatically reduced tumor induction in our rabbit animal model system [2] . Those amino acids were later proven to be important for the interaction with Brd4 , which belongs to the family of bromodomain- and extra-terminal ( BET ) proteins that are key regulators of transcription by controlling networks of genes , including P-TEFb and Mediator , involved in cellular proliferation and cell cycle regulation [3] . Dysregulation of BET protein activity has been linked to different cancers , notably NUT-midline carcinoma [4] . Papillomaviruses require Brd4 for efficient genome maintenance , partitioning and tethering viral genomes to the host chromosome in mitosis [5 , 6] and binding to Brd4 stabilizes the E2 protein [7–9] . Both the transcriptional activation and the repression function of E2 have been linked to an interaction of E2 with the far C-terminus of Brd4 [8] . Transcriptional repression of viral promoters controlling E6/E7 oncogene expression via E2 is partly due to the circumstance that P-TEFb and E2 compete for a binding site at the C-terminal domain ( CTD ) of Brd4 , while P-TEFb in complex with Brd4 is required for promoter activation [10] . In addition , sterical hindrance of basal transcription factors , like TBP , through the binding of E2 to two binding sites in close proximity to the transcription start site plays a role in E2-mediated repression [5] . The mechanism of transcriptional activation involving the E2-Brd4 complex is , however , less clear . Trans-activation of the natural enhancer/promoter has so far only been described for bovine papillomavirus , cottontail rabbit papillomavirus [11] and cancer-associated EV papillomaviruses [5 , 12] , while genital high-risk types involved in cervical cancer always have two E2 binding sites ( E2BS ) in close proximity to the transcription start site , which mediate repression of the promoter via E2 . We have recently shown that PV E2 protein induces the mRNA encoding matrix-metalloproteinase 9 ( MMP9 ) via the proximal AP1 site within the MMP9 promoter [13] . Interestingly , AP1 sites in the upstream regulatory region ( URR ) of PVs have been shown to be essential for the activity of the promoter driving expression of the viral oncogenes E6/E7 and to be responsive to stimulation by phorbol esters . Mutations of an AP1 site conserved in position in the enhancer of papillomaviruses almost completely abolished the activity despite the presence of intact E2 binding sites [14 , 15] . AP1 sites mediate transcriptional activation , when bound by dimeric complexes consisting of c-jun and c-Fos family members and the sequence composition of the AP1 sites determines the binding affinity of the 18 different dimeric AP1 complexes [16] . Interestingly , a shift in the AP1 complex composition from c-Jun/Fra-1 to c-Jun/c-Fos heterodimers has been observed during tumor progression [17 , 18] and c-Fos has been shown to be essential for malignant progression of skin tumors [19 , 20] . As the MMP9 promoter induction by E2 was independent of E2 binding sites , but required AP1 binding sites and interaction of E2 with Brd4 [13] we now investigated the mechanism how E2 can activate promoters via AP1 binding sites . In the present study , we identified a novel pathway how the E2/Brd4 complex activates the papillomavirus promoter via c-Fos and show that each of these two factors is essential for tumorigenesis .
To study the ability of CRPV E2 to activate AP1-dependent reporters , we performed luciferase experiments with artificial promoter constructs consisting of multimers of four different TPA- ( TREs ) and as control one cAMP-responsive element ( CRE ) . Seven copies of each sequence element originating from the human MMP9 promoter ( AP-MMP9 ) , from the URR of HPV18 ( AP18 ) , from the URR of CRPV ( dAP-CRPV , pAP-CRPV ) and from the CRE element were cloned upstream of the luciferase gene ( Fig 1A ) into the vector pLuc-MCS ( Stratagene Corp . La Jolla , CA ) and co-transfected with wild-type ( wt ) CRPV E2 in HPV-negative cervical carcinoma C33A cells ( ATCC ) . Wt CRPV E2 activated all TRE-dependent reporters ( 5- to 18-fold ) , but not the CRE reporter ( Fig 1A ) . As no E2BSs are present in the investigated promoter regions , the E2-mediated stimulation of the AP1 reporters seems to occur without specific binding of E2 to the reporter plasmids . Our previous studies demonstrated that the AP1-mediated activation of the MMP9 promoter by CRPV E2 does not require E2BS in the promoter region [13] . Therefore we tested if the CRPV E2 DNA-binding domain ( DBD ) is dispensable for activation of an AP1 reporter . Using a DNA-binding deficient E2 mutant with two amino acid substitutions in the highly conserved DNA binding domain ( DBD; CRPV E2 K320M/C321R ) [5 , 13] , we found a complete loss of the ability to activate an E2-dependent reporter construct ( pC18-Sp1-Luc ) and to cooperate with E1 in the replication of the CRPV-URR ( S1A Fig ) . Nevertheless , co-transfection of CRPV E2 K320M/C321R with AP18 into C33A cells resulted in activation of AP18 comparable to the wt CRPV E2 ( Fig 1B ) , suggesting that the DBD of E2 is not required for the activation of AP1 elements . Since Brd4 interacting with PV E2 proteins is required for stability of E2 and its transactivating capability [6 , 8] , we tested whether Brd4 is also necessary for AP1 activation . Specific knockdown of Brd4 was performed with an siRNA directed against Brd4 that was co-transfected with CRPVE2 and the AP18 reporter construct into C33A cells . Four independent experiments demonstrated a clear reduction of the E2-mediated induction of the AP18 reporter in the presence of the siRNA against Brd4 ( Fig 1C ) . In addition , co-transfection studies were performed with a dominant negative inhibitor form of Brd4 ( pcDNA4C-SV40NLS-hBrd4-CTD ) [21] that was previously reported to inhibit E2-mediated activation of E2BS-dependent promoters [21–23] . pcDNA4C-SV40NLS-hBrd4-CTD was transfected in increasing amounts together with a constant amount of CRPV E2 and the AP18 reporter construct into C33A cells . As a result , a dose-dependent decrease of the activation of the AP18 reporter devoid of E2BS by E2 was observed ( S1B Fig ) . To overcome limitations using artificial reporters with multimerized AP1BS , we next investigated the natural CRPV URR containing nine E2BS and two putative AP1BS . The entire URR encompassing the L1 stop codon and the E7 ATG driving the luciferase reporter was cloned ( CRPV-URR; Fig 1D ) and cotransfected with wt CRPV E2 or CRPV E2 proteins mutated in either the DBD ( K320M/C321R ) or the Brd4-binding domain ( I73A ) ( Fig 1E ) . The E2 DBD mutant still caused activation ( 4-fold ) compared to wt E2 ( 15-fold ) , whereas the Brd4-binding-deficient E2 mutant ( I73A ) completely lost the ability to transactivate the URR ( Fig 1E ) . The ability of the E2 DBD mutant to still activate might be explained by the presence of AP1BS in the natural CRPV URR . To investigate this in more detail , we mutated either one or both of the putative AP1 elements and co-transfected the resulting luciferase reporter constructs with CRPV E2 . As positive control we used an expression vector for the MAP3K1 protein ( pFC-MEKK , Stratagene , La Jolla , CA , ) [24] known to generally activate the MAPK signal transduction pathway . The induction of the CRPV-URR by MAP3K1 expression ( 7-fold ) indicates that the viral enhancer/promoter is responsible to stimuli acting via AP1 ( Fig 1F ) . Mutation of the single distal or both distal and proximal AP1BS affected the basal activity of the reporter construct and abolished the inducibility via MAP3K1 , whereas mutation of the proximal AP1BS ( mpAP ) had a less profound effect . Interestingly , activation of the CRPV-URR by E2 was strongly affected by the mutation of the single distal or both AP1BS despite the presence of nine intact E2BS , while mutation of the proximal mpAP did not change the extent of transactivation by E2 ( Fig 1F ) . These data suggest that AP1BS play a major role for the activity of the CRPV-URR both in the absence and the presence of E2 and that the distal AP1 site , which is a canonical AP1 site ( TGACACA ) is the dominant one . Gel mobility shift experiments using a DNA oligonucleotide carrying the sequence of the distal AP1 site and different recombinant AP1 dimers ( cJun/cJun; cJun/Fra1; cJun/Fra2; cJun/FosB; cJun/cFos ) co-expressed and purified from E . coli [16] demonstrated specific binding of those AP1 dimers to the distal AP1BS of CRPV , whereas binding to the mutated site ( mdAP ) was abolished ( Fig 1G ) . To study the role of the AP1BS for tumor induction in vivo , we introduced single mutations ( mdAP , mpAP ) and the double-mutation ( dpAP ) into the URR of the whole CRPV genome ( pLAII-CRPV-mdAP , pLAII-CRPV-mpAP , pLAII-CRPV-mdpAP ) . The skin of New Zealand White ( NZW ) rabbits was infected with these CRPV genomes and tumor growth was assessed 6 months post infection . We observed only 33 . 3% papilloma induction with pLAII-CRPV-mdAP as compared to pLAII-CRPV-mpAP and the wt CRPV positive control ( 70% and 100% , respectively; Fig 2; Table 1A ) . No tumors were observed in animals infected with a CRPV genome mutated in both AP1 sites despite the presence of intact E2BS ( pLAII-CRPV-mdpAP ) ( Fig 2 and Table 1A ) , demonstrating an essential role for AP1 in PV-induced tumorigenesis . We previously observed that mutations of conserved amino acids within the E2 transactivation domain ( TAD ) that mediate Brd4 binding severely impaired the ability of CRPV to induce tumors in rabbits [2] . In the meantime we developed a recombinant CRPV genome harboring an shRNA cassette instead of the late L2 gene ( pLAII-CRPVsh ) [25] , which allows the efficient knockdown of endogenous cellular proteins ( Fig 3A ) . By using this construct , we directly assessed the significance of Brd4 for CRPV-dependent tumorigenesis in vivo . First , two different siRNAs against Brd4 ( siBrd4-1 , siBrd4-2 ) were transfected in rabbit keratinocytes immortalized with the whole CRPV genome [26] . Both siRNAs efficiently reduced Brd4 mRNA levels to 44% or 58% as compared to the mock-transfected control ( Fig 3B ) . Using the identical sequences , shRNA expression vectors were constructed ( pLVTHMshBrd4-1 and pLVTHMshBrd4-2 ) and transduced into 293T cells ( ATCC ) where a reduced Brd4 protein level in comparison to the control shRNA was detected ( Fig 3C ) . The shBrd4-1/2 sequences were then cloned into the shRNA expression cassette of pLAII-CRPVsh ( Fig 3A ) and NZW rabbits were infected with the resulting constructs . As negative control , an shRNA sequence targeting the firefly luciferase gene was used ( pLAII-CRPVshLuc ) . Six months post infection no tumors were observed in pLAII-CRPVshBrd4-1 infected rabbits ( Fig 3D and Table 1B ) , while pLAII-CRPVshBrd4-2 diminished tumor induction to 67% as compared to the control ( pLAII-CRPVshLuc; Table 1B ) . These results strongly support an important role of Brd4 for CRPV-mediated tumor formation in vivo . To identify the mechanism of E2-mediated activation of AP1-responsive promoters , we used an unbiased approach to search for cellular genes with altered expression levels in the presence of CRPV E2 . For this we established an inducible E2-expressing cell line using the pRTS1-vector [28] . The system prevents the expression of the target gene through a tetR-KRAB fusion protein , which is released after the addition of doxycycline , whereas the co-expressed tetR-VP16 protein simultaneously mediates activation . C33A cells harboring the pRTS1-CRPV E2-HA plasmid were induced for 48h and induction of E2 was verified at protein levels ( Fig 4A ) . Total cellular RNA was extracted 48h after induction and analyzed by the Affymetrix GeneChip Human exon 1 . 0 ST Array . Gene expression analysis revealed 137 genes that were differentially expressed after induction of wt CRPV E2 ( ≥1 . 75-fold ) ( S1 Table ) . Surprisingly , c-Fos and FosB , two components of the AP1 family , were found to be transcriptionally upregulated . To validate this finding and extend this observation to other AP1 components , RNA was isolated from C33A cells transiently transfected with wt CRPV E2 , CRPV E2 K320M/C321R , and the empty vector as control . qPCR confirmed the induction of c-Fos ( 6-fold ) and FosB ( 8-fold ) by wt CRPV E2 and by the DNA-binding-deficient E2 mutant ( 2-fold ) ( Fig 4B ) . The upregulation of c-Fos occurred also at the protein level ( Fig 4C ) . To extend our findings to other papillomavirus types , we transiently transfected C33A cells with vectors expressing the E2 protein of different HPV types [29] . As a result we found that most E2 proteins were able to induce c-Fos as well as FosB in C33A cells ( Fig 4D and 4E ) and also in HPV18-positive HeLa cells ( ATCC ) ( S2 Fig ) . Based on the evidence of c-Fos involvement in skin tumorigenesis [19 , 30] , we decided to focus on c-Fos in the following experiments . Hence , we tested whether c-Fos–as one possible component of the AP1 complex–contributes to the in vitro activity of the CRPV URR either in the presence or absence of CRPV E2 . For this , c-Fos was silenced in C33A cells using a pool of three siRNAs ( Fig 4F ) before transfecting the reporter plasmid and then cells were serum starved prior to harvest . Silencing of c-Fos affected the basal activity of the CRPV-URR-Luc reporter , but not E2 expression levels ( S3 Fig ) and dramatically diminished E2-mediated activation as compared to the control siRNA ( Fig 4G ) . This indicated that c-Fos was not only upregulated by E2 , but is also part of the AP1-complex acting as a major stimulus on the promoter responsible for the expression of viral E6/E7 oncogenes . To study in vivo c-Fos regulation by wt CRPV genome , we characterized c-Fos expression by immunohistochemistry . We found up-regulation of c-Fos only in CRPV-induced papillomas in contrast to normal healthy rabbit skin ( S4 Fig ) . In addition , we performed immunohistochemistry on sections derived from papillomas that occurred after infection with a CRPV genome containing a mutation at amino acid 73 ( I73A ) of the E2 protein . This mutation was shown to cause a replication-competent , but transactivation-deficient phenotype with a strongly reduced ability to induce papillomas that appeared at a much later time-point in comparison to the wt CRPV control infection [2] . The I73A mutation has been shown to disable E2 from binding to Brd4 [5] . Our data show that this causes a loss of the ability to induce c-Fos in vivo in comparison to the wt control ( Fig 4H ) . As it has been shown that mild irritation of the skin before infection with CRPV DNA greatly increases tumor formation in rabbits [31] , we speculated that this might be due to the induction of the immediate early gene c-Fos . To test for this , punch biopsies were taken 10 , 20 and 30 minutes after manual irritation of the skin with sand paper and cellular RNA was analyzed by qPCR . In comparison to untreated skin , c-Fos mRNA was rapidly induced in irritated skin up to 3-fold 30 minutes after irritation ( S5 Fig ) , supporting an important role of c-Fos for the establishment of an infection with CRPV in vivo . To generate in vivo data supporting the role of c-Fos in tumorigenesis , we could not use our recombinant shRNA-CRPV genome to directly knock down c-Fos , as we already had to use a pool of three different siRNAs for in vitro knockdown . We therefore developed two shRNAs for MEK-1 as an important regulator of c-Fos expression , which demonstrated in tissue culture a remarkable knockdown of the MEK-1 protein ( Fig 3E ) . When those shRNAs were tested in vivo , they showed a dramatic effect on tumor induction ( 8% and 42%; Fig 3F and Table 1C ) , which supports a critical role of the MAPK pathway leading to the stimulation of c-Fos in tumorigenesis . First we performed luciferase reporter experiments with consecutively shortened c-Fos promoter fragments from -5238 till -362 bp in relation to the c-Fos ATG driving the luciferase gene ( Fig 5A ) . The -2795 fragment had the highest activity and truncation of the -530 fragment to -362 caused further loss of activity ( S6 Fig ) . In silico analysis of the nucleotide sequences revealed the presence of a canonical E2BS at position -2411 within the -2795 fragment and a c-Fos/AP1-site ( FAP1 [32] ) in the -530 fragment that is lost by truncation in the shorter -362 fragment [33] . Another potential AP1BS was detected at position -4961 . When we mutated the FAP1 site within the -530 fragment , we observed a loss in activity similar to the truncated -362 fragment , which no longer contains the FAP1 site ( S6 Fig ) . These results pointed towards an important role of the E2BS and FAP1 in the regulation of c-Fos activity . Therefore we first analyzed the binding of AP1 to FAP1 by gel-shift mobility experiments using a ds oligo matching either the wt or the mutated FAP1-sequence . Our results confirmed specific binding of different AP1 dimers ( cJun/cJun; cJun/Fra1; cJun/Fra2; cJun/FosB; cJun/cFos ) co-expressed and purified from E . coli [16] to FAP1 , which is completely abolished with the mutated mFAP1 ( Fig 5B ) . We next performed chromatin immunoprecipitation ( ChIP ) experiments and investigated the binding of different AP1 components to FAP1 and the potential AP1BS at -4961 in the endogenous c-Fos promoter . C33A cells were serum-starved for 18h and after addition of serum ChIP experiments were performed using antibodies targeting respective AP1components c-Fos , FosB , c-Jun , JunB and JunD and primers flanking the AP1BS at positions -454 and -4961 . All AP1 members , except JunB , were enriched at the FAP1 site and at the AP1 site at position -4961 ( Fig 5C ) . To investigate how E2 activates c-Fos expression , binding of E2 and Brd4 to the consensus E2BS in the c-Fos promoter at position -2411 as well as to both AP1 sites was investigated by ChIP . A distinct enrichment compared to the empty vector of both CRPV E2-HA ( 4 . 1 fold ) and Brd4 ( 2-fold ) at the canonical E2BS was observed , indicating that CRPV E2 indeed binds together with Brd4 to this sequence . In contrast , no enrichment of E2 or Brd4 was observed at the AP1BS at -4961 while Brd4 , but not E2 , was enriched approximately 6-fold at the FAP1 ( Fig 6A ) . This might be due to the close proximity of FAP1 to the transcription start site and the general transcriptional co-activator activity of Brd4 [3] . To address the binding requirements for E2 at the canonical E2BS in the c-Fos promoter , we first confirmed the E2-Brd4 interaction in our C33A cells stably expressing CRPV E2HA , CRPV E2HA I73A and CRPV E2HA K320M/C321R in co-immunoprecipitation experiments . Due to the extremely low expression levels , cells were pretreated with MG132 to detect E2 protein . As expected , wt CRPV E2HA and CRPV E2HA K320M/C321R interacted with endogeneous Brd4 , whereas CRPV E2HA I73A did not ( Fig 6B ) . In order to yield a higher protein amount of wt CRPV E2 , CRPV E2HA I73A and CRPV E2HA K320M/C321R for ChIP experiments , approximately 8x107 C33A cells were transiently transfected with the respective expression vectors for ChIP experiments . Again a specific enrichment of both CRPV E2-HA and Brd4 at the canonical E2BS was observed ( Fig 6C ) . In contrast , the I73A mutant showed no enrichment , possibly due to a lack in protein stability caused by the inability to bind to Brd4 [2 , 34] . The E2K320R/C321M mutant displayed a comparable enrichment as shown for wt E2 at the E2BS of the c-Fos promoter , which correlates with the binding of Brd4 . This is in line with our results showing a weak c-Fos induction at the mRNA level by the DNA-binding-deficient CRPV E2 mutant K320R/C321M ( Fig 4B ) . Our data strongly support that CRPV E2 together with Brd4 activates c-Fos transcription by binding in complex to the E2BS in the c-Fos promoter .
We here report a novel mechanism how the complex of E2/Brd4 is able to activate the papillomavirus early promoter responsible for expression of the viral oncogenes via AP1 sites that are bound by dimeric AP1 complexes which contain c-Fos . Further we show that the presence of intact AP1 sites in the CRPV genome as well as of the E2 binding partner Brd4 is essential for tumor formation in the rabbit . These observations explain earlier findings that AP1 is the major cellular transcription factor for the activity of the URR and that E2 requires additional cellular factors such as AP1 , Sp1 and Oct1 for the ability to trans-activate [35 , 36] . We here show that E2 itself causes upregulation of c-Fos as one component of AP1 , which is involved in the cell type specificity of papillomaviruses and the differentiation-dependent expression of viral genes in the differentiating epithelium [37 , 38] . Our data indicate that E2 transactivates the natural enhancer/promoter of PVs via stimulation of conserved AP1 binding sites in addition to binding to E2BS which are also required for the partitioning of the viral genome during mitosis of infected cells [5] . In addition , we demonstrate that any interruption of the E2-mediated transcriptional induction of the viral promoter through c-Fos either by mutating the AP1 binding sites in the genome or by knockdown of Brd4 affects the tumorigenic potential of papillomaviruses , although a possible side effect of both shRNAs used for knock down of Brd4 on other cellular oncogenes cannot be excluded . In the majority of cervical cancers caused by high-risk genital types , including HPV16 , the E2 protein is lost because of the integration of the viral genome into the host chromosome . However , in those cases the viral genome integrates preferably in transcriptionally active genomic regions of the host and therefore the viral promoter underlies other transcriptional regulation mechanisms [39] . In cases of skin cancer or cervical cancer with mixed viral genome status or episomal DNA [40 , 41] , E2 together with Brd4 might switch from a repressive effect on the viral promoter on integrated genomes to an activating effect on the episomal genome as has been proposed before [42 , 43] with AP1 playing a prominent role . In the case of high-risk genital type HPV31 , the URR with several AP1 sites was in fact previously found to be induced by E2 in the absence of functional E2 binding sites [37 , 44] which might be achieved by stimulation of AP1 activity and subsequent activation of the viral promoter through the E2/c-Fos/AP1 pathway . This might also explain some of the earlier reports showing that low levels of E2 stimulate the viral promoter of , e . g . , HPV18 [45] , while higher levels lead to repression through the promoter-proximal E2BS . In skin cancers caused by epidermodysplasia verruciformis PV types , which usually contain episomal viral genomes without repressive E2BS in the proximity of the early viral promoter , E2-mediated c-Fos induction could play a major role in tumorigenesis . This is supported by the finding that the E2 protein of the EV-papillomavirus HPV8 itself is able to induce skin tumors in transgenic mice [46] , which indicates that the tumorigenic potential of E2 could be related to its ability to induce c-Fos . Interestingly , HPV-positive cells undergoing tumorigenic transformation experience a shift in the composition of the AP1 heterodimers [17 , 18] . Protein levels of c-Fos increased along with increasing tumorigenicity and a shift in AP1 complex composition from c-Jun/Fra-1 to c-Jun/c-Fos heterodimers was only observed in tumorigenic cells [17] . Others showed high expression of JunD and c-Fos in HPV-positive tumors , with close to no Fra-1 expression [47 , 48] . Interestingly , in this study we observed induction of c-Fos and Fos-B , but not Fra-1 by E2 , which underlines a possible role of E2 in tumorigenesis . Furthermore it has been shown that c-Fos plays a major role in skin tumorigenesis . c-Fos-knockout mice that overexpressed a v-H-ras transgene developed papillomas that failed to undergo malignant conversion [19] . More recent data suggest that the suppression of squamous cell carcinoma is due to pharmacological inhibition of Fos/AP1 and p53/TACE reactivation [20] . While we have observed that E2 of CRPV and other HPV types induces c-Fos and Fos-B expression , we did not observe activation of Jun family members . However , when we quantified the expression levels of different Jun and Fos family members in C33A cells , we found rather high basal levels of c-Jun and JunD that would allow the formation of Jun/c-Fos dimers in C33A cells expressing E2 ( S7B Fig ) . Interestingly , the human Fos-B promoter that we found to be activated by E2 also contains a canonical E2BS approximately -3500 bp of the transcription start site ( TSS ) as well as two non-canonical E2BS about 1kb upstream of the TSS , and the rabbit c-Fos promoter contains four identical non-canonical E2BS within 3kb upstream of the TSS . We did observe Brd4-dependent binding of CRPV E2 to a canonical E2BS ( ACCCAGTCAGGT ) with a spacer region containing 50% A/T nucleotides located at -2411 upstream of the ATG of human c-Fos . E2 did not bind to the FAP1 site closest to the transcription start site ( -249 ) of c-Fos although we observed a clear enrichment of Brd4 at this site . Furthermore , the Brd4-binding-deficient E2 mutant I73A was not enriched at the canonical E2BS in the c-Fos promoter , which is probably due to a reduced stability of this mutant in the absence of Brd4 binding [5] . However , we observed an enrichment of the DNA–binding-deficient mutant E2 K320M/C321R together with Brd4 at the E2BS of the c-Fos promoter . This cannot be explained by binding of E2 in a sequence-specific manner as this mutant neither supports E2BS-dependent transcriptional activation nor replication in cooperation with E1 . One possible explanation might be the binding of DNA by the N-terminal domain of E2 as described for BPV1 E2 , which is supported by the presence of Brd4 at active cellular promoters [49 , 50] . Another possibility is that Brd4 , which we show to be bound to the canonical E2BS , brings along E2 via its CTD to chromatinized DNA as shown for the hematopoetic transcription factor GATA-1 [51] . Notably we found Brd4 enriched at the FAP1 site in close proximity to the transcription start site exclusively in cells expressing E2 suggesting that only active promoters require the Brd4/P-TEFb complex for phosphorylation of the CTD of RNA polymerase II as shown for c-Fos driven by the cooperative action of E2 and Brd4 by binding to the canonical E2BS . The use of AP1 as a major driver of viral gene expression appears to be a general phenomenon as the viral promoters of HTLV1 , KSHV and of JCV are also upregulated by AP1 [52–54] . Interestingly , other viruses also upregulate expression of c-Fos or Fos family members . Human T-cell leukemia virus type 1 ( HTLV-1 ) directly upregulates c-Fos [55–57] , while EBV [58] induces Fra-1/AP1 [59 , 60] . These observations support an important role of AP1 for the activity of tumor viruses . The ability of E2 to induce c-Fos was entirely dependent on Brd4 , which recruits transcriptional regulatory complexes to acetylated chromatin and is a major interactor of all papillomavirus E2 proteins [6] . In-frame fusions of Brd4 with the NUT gene as observed in the aggressive NUT midline carcinoma demonstrated its role as an aberrant transcription factor that requires the bromodomains for its tumorigenic activity [61] . More recently , a direct , specific and acetylation-independent interaction of Brd4 with distinct transcription factors , such as p53 , c-Jun and Myc/Max has been described [62] . Furthermore , the bromodomains of Brd4 also interact with specific acetylated regions of transcription factors , as has been shown for TWIST , a TF using Brd4 as co-activator controlling mesoderm formation during development [63] . The interaction of Brd4 with viral transcription or replication factors such as LANA from KSHV , Tax from HTLV1 , large T antigen from MCPyV , EBNA1 and EBNA2 from EBV and E2 from PVs seems to be another conserved feature among tumor viruses and was shown to be responsible for viral promoter regulation as well as viral replication [64–67] . Because of its fundamental role in transcriptional regulation , Brd4 has also been investigated as a therapeutic target to combat a number of cancers with deregulated Brd4 activity [61] . The recent identification of Brd4 inhibitors including JQ1 and I-BET provides great potential for treatment of HPV-induced cancers as both inhibitors target bromodomains [68 , 69] and prevent them from binding to acetylated histones and to act as transcriptional activators [70] . In summary , we present evidence of a novel pathway in which the E2/Brd4 complex activates the papillomavirus promoter via c-Fos and we show that each of the three components is essential for tumorigenesis . Furthermore we demonstrate that E2 contributes in two different ways to tumorigenesis . First by stimulating the viral promoter responsible for the expression of the viral oncogenes via E2- and AP1-binding sites and secondly by stimulating c-Fos , which is involved in skin tumorigenesis independently of PVs . Because both E2-mediated regulation of viral oncogene and c-Fos expression are completely dependent on Brd4 , our study supports the idea that bromodomain inhibitors as well as inhibitors of the MAPK pathway affecting protein levels of cellular AP1 may also be effective against PV-induced tumors , which requires further investigation . MAPK pathway inhibitors are supported by a recent study where BRAF inhibitors , such as vemurafenib , caused the appearance of ß-papillomavirus associated squamous cell carcinoma ( SCC ) in up to 26% of treated melanoma patients , while the combination of vemurafenib with the MEK-inhibitor cobimetinib reduced the appearance of SCC to 5% [71] . The identification of AP-1 , E2 and Brd4 as crucial regulators for CRPV and cellular c-Fos and MMP9 promoter activity further substantiates their implications in regulating HPV gene expression and the interplay between viral and cellular factors modulating eukaryotic transcription [72] .
Animal experiments were reviewed and approved ( Permit Number: H1/03 and H1/08 ) by the responsible authority ( Regierungspräsidium Tübingen , Baden-Württemberg , Germany ) according to the German Animal Welfare Act ( TierSchG §8 Abs . 1 ) and were performed according to national regulations ( TierSchVersV ) . New Zealand White rabbits were obtained from Charles River Laboratories . Rabbits were infected with different recombinant CRPV genomes using the “helios gene gun” ( Bio-Rad ) as described previously [2] . Tumor growth and papilloma size were regularly monitored and documented . Skin punches ( 6mm in diameter ) from normal rabbit skin were taken using sterile single use Biopsy punches ( pfmmedical ) and total RNA was extracted using Qiazol ( Qiagen ) followed by purification of the RNA with RNeasyMinElute columns ( Qiagen ) according to the manufacturer´s instructions . C33A cells were transfected with pRTS1-CRPVE2-HA or empty vector pRTS1 and selected with 250 μg/ml of hygromycin for 5 days . CRPV E2-HA expression was induced in stable pooled cell lines by the addition of 1 μg/ml of doxycycline for 48h . The cells were then harvested and total cellular RNA was isolated for microarray analysis . Double-stranded oligos ( sequences listed in S2 Table ) were labelled with 32P ( GE Healthcare ) using Polynucleotide Kinase ( Fermentas ) and purified with illustra ProbeQuant G-50 Micro columns ( GE Healthcare ) . 5–10 fmol/μl were used in binding reactions with 50 ng of recombinant protein in Bclw/BSA buffer ( 10% Glycerol , 10 mM Hepes pH 7 . 9 , 70 mM NaCl , 0 . 2 mM EDTA , 4 mM MgCl2 , 25 mM DTT , 50 μM Zn , 0 . 1 mg/ml BSA and 50 ng/μl polydI-dC ) . Binding reactions were performed for 30–40 min at 30°C and separated on a 4% polyacrylamide gel with 0 . 25x TBE ( 89 mM Tris , 89 mM boric acid , and 1 mM EDTA ) as running buffer for 1h . The gel was dried , exposed and then visualized by using Typhoon 9200 PhosphorImager ( GE Healthcare ) . For oligo competitions , a 100-fold molar excess of unlabeled DNA fragments containing either wild-type or mutated distal AP1 sequences was included at the beginning of the reaction . C33A stably expressing CRPV E2HA , CRPV E2HA I73A , CRPV E2HA K320M/C321R or the empty vector ( pIRESpuro3 , Clontech ) were seeded and treated with 1 μM MG132 16 h before harvest to prevent protein degradation . Cells were harvested in cold PBS , lysed in high salt NP-40 buffer ( 50 mM Tris-HCl , pH 8 . 0 , 400 mM NaCl , 0 . 1% NP-40 , and 0 . 25% sodium deoxycholate , 1 mM DTT and Protease-Inhibitors ( Roche ) and incubated for 30 min on ice . The lysate was centrifuged , and the supernatant was diluted to a final NaCl concentration of 150 mM . After that , the supernatant was incubated with 30 μl of anti HA-magnetic microbeads ( Miltenyi biotec ) for 1h . The supernatant was discarded and the beads were washed 2–3 times with NP-40 buffer containing 150 mM NaCl . Bound protein complexes were eluted using SDS sample buffer ( Carl Roth ) . For transfection for ChIP assays , one 100-mm plate per construct of C33A cells was transfected with 10 μg DNA using Fugene HD ( Promega ) according to the manufacturers’ instructions . ChIP was carried out as previously described [72] . See supplemental methods file for details . GraphPad ( version 5 ) was used to calculate unpaired and paired p-values . All transcriptional profiles have been submitted to the GEO database at NCBI ( Accession no . GSE67345 ) . | Human Papillomaviruses ( HPV ) are the etiological agents of cervical cancer and of skin cancer in individuals with the inherited disease epidermodysplasia verruciformis ( EV ) . While the role of the viral oncogenes E6/E7 as drivers of tumorigenesis in cervical cancer has been firmly established , the contribution of the early viral genes in skin cancer is less clear . For EV-associated HPV8 and for the skin cancer model system using cottontail rabbit PV , an important role of the viral E2 protein in tumorigenesis was suggested earlier and regulation of cellular genes by E2 through different mechanisms was demonstrated . We show now that the viral E2 and cellular Brd4 act together to induce the cellular gene c-Fos , which as a member of the AP-1 complex , is involved in the regulation of cellular genes and the viral promoter driving the expression of viral oncogenes . As c-Fos has also been shown to be essential for skin cancer , E2 contributes to tumorigenesis via expression of E6/E7 as well as by increasing c-Fos . | [
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] | [] | 2016 | Papillomavirus-Associated Tumor Formation Critically Depends on c-Fos Expression Induced by Viral Protein E2 and Bromodomain Protein Brd4 |
Cohesion between sister chromatids is mediated by cohesin and is essential for proper meiotic segregation of both sister chromatids and homologs . solo encodes a Drosophila meiosis-specific cohesion protein with no apparent sequence homology to cohesins that is required in male meiosis for centromere cohesion , proper orientation of sister centromeres and centromere enrichment of the cohesin subunit SMC1 . In this study , we show that solo is involved in multiple aspects of meiosis in female Drosophila . Null mutations in solo caused the following phenotypes: 1 ) high frequencies of homolog and sister chromatid nondisjunction ( NDJ ) and sharply reduced frequencies of homolog exchange; 2 ) reduced transmission of a ring-X chromosome , an indicator of elevated frequencies of sister chromatid exchange ( SCE ) ; 3 ) premature loss of centromere pairing and cohesion during prophase I , as indicated by elevated foci counts of the centromere protein CID; 4 ) instability of the lateral elements ( LE ) s and central regions of synaptonemal complexes ( SCs ) , as indicated by fragmented and spotty staining of the chromosome core/LE component SMC1 and the transverse filament protein C ( 3 ) G , respectively , at all stages of pachytene . SOLO and SMC1 are both enriched on centromeres throughout prophase I , co-align along the lateral elements of SCs and reciprocally co-immunoprecipitate from ovarian protein extracts . Our studies demonstrate that SOLO is closely associated with meiotic cohesin and required both for enrichment of cohesin on centromeres and stable assembly of cohesin into chromosome cores . These events underlie and are required for stable cohesion of centromeres , synapsis of homologous chromosomes , and a recombination mechanism that suppresses SCE to preferentially generate homolog crossovers ( homolog bias ) . We propose that SOLO is a subunit of a specialized meiotic cohesin complex that mediates both centromeric and axial arm cohesion and promotes homolog bias as a component of chromosome cores .
Meiosis is a specialized type of cell division that generates haploid gametes from diploid germ cells . It encompasses a single round of DNA replication followed by two rounds of chromosome division in which first homologous chromosomes then sister chromatids segregate . During prophase of the first division ( prophase I ) , homologous chromosomes pair , synapse and recombine with their partners . The resulting crossovers , stabilized by cohesion between sister chromatid arms , serve as chromatin linkers known as “chiasmata” that enable homolog pairs to bi-orient on the first division spindle . At anaphase I , resolution of sister chromatid arm cohesion leads to homolog segregation . Sister chromatids remain attached at their centromere regions until anaphase II , when resolution of centromere cohesion allows them to segregate [1]–[6] . Cohesion between sister chromatids is essential for several key steps in meiotic segregation and is mediated by ring-shaped cohesin complexes that embrace sister chromatid pairs [2] , [7] . The subunits of cohesin are two SMC ( structural maintenance of chromosomes ) proteins , SMC1 and SMC3 , and two non-SMC subunits , a “kleisin” subunit , which can be either the mitotic SCC1/RAD21 protein or its meiosis-specific paralog REC8 , and a SCC3/SA-family subunit . SMC1 and SMC3 are long intramolecular coiled-coil proteins that form extended hairpin structures with N- and C-terminal globular ATPase domains at one end and a globular hinge domain at the other . SMC1 and SMC3 bind to each other at their hinge domains and to opposite ends of the kleisin subunit at their ATPase domains , forming a tripartite ring that embraces pairs of sister chromatids . The SA subunit binds to the kleisin subunit and regulates cohesin chromosome binding . Cohesin is loaded on chromatin prior to or during S phase and establishes cohesion during DNA replication . Although cohesin can be removed by other means and at other times in the cell cycle , cleavage of RAD21 or REC8 by the protease Separase at anaphase leads to release of sister chromatids and triggers segregation [7]–[10] . In meiosis , cohesion has a dual role , to keep homologs connected by stabilizing chiasmata on chromosome arms until anaphase I , and to keep sister chromatids connected at their centromere regions until anaphase II . The same cohesin complex , REC8 cohesin , is responsible for both arm and centromere cohesion and the same protease , Separase , is responsible for cleaving both arm cohesin at anaphase I and centromere cohesin at anaphase II . Since cohesin must be loaded prior to the first division , the centromeric cohesin complexes require protection from cleavage during anaphase I . This function is carried out by the centromeric guardian protein Shugoshin and its effectors ( including the PP2A phosphatase ) [11] , [12] . REC8 and Shugoshin and the two-step cohesin release mechanism appear to be widely conserved [2] , [7] , [12] . REC8 and other cohesins are also required for several other essential steps during the first meiotic division , including homolog pairing , synapsis and recombination [2] , [8] , [9] . However , it is not clear to what degree these roles involve cohesion . In yeast and C . elegans , mutations in rec8 and smc3 can disrupt recombination , DSB formation and DSB repair without affecting cohesion [13] , [14] . Another crucial meiosis-specific centromere modification , mono-orientation , is needed at the first division to prevent sister centromeres from connecting to opposite poles ( bi-orienting ) as they do at all other divisions . Instead , sister centromeres must collaborate in forming a single microtubule-binding surface and orient toward the same pole ( mono-orient ) so that their counterparts on the opposite homolog can orient to the opposite pole . This coordinated orientation of centromeres is essential to ensure that they segregate reductionally , with both sisters co-segregating to the same pole during the first meiotic division , rather than equationally as in mitosis or the second meiotic division . The mono-orientation process is not well understood . In S . cerevisiae , mono-orientation is mediated by a specialized Monopolin complex that clamps sister centromeres together , and a different specialized monopolin protein Moa1 is required for mono-orientation in S . pombe . However , these yeast proteins are not conserved . In several higher eukaryotes including C . elegans and Arabidopsis , cohesin is required for mono-orientation but what role it plays is not known [15]–[19] . Proper homolog segregation requires recombination to generate the crossovers that serve as chiasmata . Meiotic recombination is initiated by programmed double-strand breaks ( DSBs ) induced by the conserved Spo11 endonuclease [20] . Breaks are then repaired by a meiosis-specific version of the ubiquitous homologous recombination pathway modified to ensure that the repair products include adequate numbers of homolog crossovers ( at least one per chromosome pair ) [21] , [22] . A crucial modification , known as “homolog bias” , involves preferential use of homologous over sister chromatids as repair templates , a reversal of the sister chromatid bias that prevails in somatic DSB repair [23] , [24] . Understanding of the mechanism of homolog bias is rudimentary but studies in yeast have identified two groups of proteins that play key roles: the meiosis-specific recombinase DMC1 , a paralog of RecA and RAD51 , which preferentially mediates invasion of homologous rather than sister strands [25] , [26]; and the SC proteins RED1 , MEK1 and HOP1 that seem to function mainly by inhibiting sister chromatid exchange ( SCE ) [23] , [24] , [27]–[29] . The few proteins outside of yeast that have been identified as being important for homolog bias , including ORD in Drosophila , HIM-3 in C . elegans , and SYCP-2 and SYCP-3 in mammals are also SC proteins , pointing to a possible conserved function of the SC in homolog bias [11] , [30]–[32] . Either before or coincident with the early stages of meiotic recombination ( depending on organism ) , homologs pair and “synapse” , a process that culminates in assembly of a tripartite structure called synaptonemal complex ( SC ) [5] . SC consists of two parallel lateral elements ( LEs ) that encompass the axes of the homologs , connected by densely packed transverse filaments that span a central region of about 100 nm , and a central element that lies parallel to and midway between the LEs . Transverse filaments are homo-dimeric coiled-coil proteins that bind to each other at their N-termini and to the LEs at their C-termini [33] , [34] . In many eukaryotes , the LEs are clearly visible prior to synapsis when they are called axial elements ( AEs ) , but in Drosophila no AEs have been observed . Instead , the LEs and central regions of the SCs assemble simultaneously during synapsis [5] , [6] . Synapsis initiates during zygotene as short stretches of SC assembled at axial association sites , accompanied or preceded ( depending on species ) by alignment of homologs [5] , [6] . In some eukaryotes , axial association sites correspond to DSB sites where the early stages of interhomolog recombination take place [6] . However , in Drosophila , DSBs are delayed until pachytene when homologs are fully synapsed , and synapsis is initiated and completed independent of the recombination apparatus [35] , [36] . The initial SC patches are extended by a poorly understood process that leads eventually , at pachytene , to fully aligned and synapsed homolog pairs . Recombination is thought to be completed during pachytene and after it is complete , the SCs are disassembled and homologs disassociate except at chiasmata , which keep them connected throughout the first division . [5] , [6] . AE/LEs are prominent , meiosis-specific versions of chromosome axes that develop in early prophase I [5] , [6] . They encompass the paired sister chromatid axes that anchor the chromatin loops and are built on a condensed “chromosome core” of densely packed cohesin complexes that serves as a scaffold for assembly of additional meiosis-specific AE/LE proteins that promote homolog interactions , mostly by mechanisms that remain to be defined [37] , [38] . The best understood AE/LE proteins are RED1 and HOP1 , mentioned above as yeast proteins involved in homolog bias . RED1 is also required for synapsis and SC formation but some other AE/LE proteins are dispensable for SC formation although they are often required to stabilize chromosome cores and SCs [27] , [30]–[32] , [39]–[43] . In mammals and Drosophila , homologous chromosome cores can synapse with each other in the absence of the non-cohesin AE/LE components although the resulting SCs tend to be unstable and to disassemble prematurely [37] , [38] , [43] . Many eukaryotes have additional meiosis-specific kleisin family members or other cohesin paralogs and many of these are found primarily or exclusively in cores [40] , [44] . One such paralog is C ( 2 ) M , a kleisin family member in Drosophila that is present only during prophase I in cores and is required for LE assembly , synapsis and normal levels of recombination but is dispensable for cohesion [45] , [46] . Thus current evidence points to a fundamental role of the cohesin-based chromosome cores in synapsis and SC structure . However , although cores are cohesin-based , the role of cohesion in chromosome core and SC assembly remains to be clarified . Cohesion is essential for chromosome segregation in Drosophila meiosis as well , but the way in which cohesion is mediated appears to differ from most other eukaryotes . No true REC8 homolog has been identified . The aforementioned C ( 2 ) M is the only known meiosis-specific kleisin , but its role is much more specialized than REC8 . It is an essential component of the chromosome cores and required for synapsis and recombination but it is not enriched at centromeres and has no apparent role in either arm or centromere cohesion [45] , [46] . Orientation Disruptor ( ORD ) is a cohesion protein that seems to carry out many of the functions of REC8 but it is not , on the basis of primary sequence homology , a cohesin . ORD localizes to centromeres and is required for centromere cohesion in both male and female meiosis . ORD also localizes to LEs and although not required for assembly of LEs or SCs , it is required to prevent their premature fragmentation and dissolution . Finally , ORD is required for normal levels of homolog recombination and is the only Drosophila protein known to suppress SCE . Although not a cohesin by sequence homology , ORD localizes along with the SMC cohesin subunits both at centromeres and on LEs and likely carries out some or most of its functions in collaboration with cohesin . The case is particularly clear for centromere cohesion where ord mutations lead to depletion of centromeric SMC cohesins in both male and female meiosis [30] , [43] , [47]–[52] . We have recently described a second meiosis-specific Drosophila cohesion protein , SOLO [53] . SOLO is required for centromere cohesion in Drosophila male meiosis and its loss leads to failure of mono-orientation and random chromatid assortment . SOLO and SMC1 are both enriched near centromeres throughout meiosis until both proteins disappear at anaphase II . In a mei-S332 ( Shugoshin ) mutant [11] , both SMC1 and SOLO dissociate from centromeres simultaneously at anaphase I . In solo mutants , like ord mutants , centromeric SMC1 foci are absent at all stages of meiosis . Together these data indicate that SOLO functions in very close collaboration with the SMC1 cohesin subunit . However , like ORD , SOLO shows no sequence homology with cohesins , or with any other proteins in the database [53] . The previous study was limited to male meiosis in which homologs segregate by a unique mechanism that does not involve SCs , recombination or chiasmata . Instead a specialized conjunction complex holds homologs together in place of chiasmata [54] . SOLO is not required for any step in homolog segregation in males except for centromere mono-orientation [53] . In this paper we describe the roles of SOLO in Drosophila female meiosis and show that SOLO , like ORD , carries out a broad spectrum of meiotic functions that include cohesion , pairing and clustering of centromeres , regulation of chromatid orientation and segregation at both meiotic divisions , stable assembly of LEs and SCs , achievement of normal levels of homolog exchange , and suppression of sister chromatid exchange . We also show that SOLO and SMC1 reciprocally co-immunoprecipitate from ovarian protein extracts , further underlining the close cooperation between SOLO and cohesin . The very similar mutant phenotypes and lack of synergism between solo and ord mutations suggest that SOLO and ORD function together with cohesin in the same molecular processes . Overall , our data indicate that SOLO has essential roles in centromere cohesion , AE/LE stability and recombination . SOLO joins ORD as the second such protein to be identified in Drosophila . Analysis of the multiple functions of SOLO in meiosis should further insight into the roles of cohesion in meiotic segregation .
Errors in meiotic chromosome segregation , referred to here as nondisjunction ( NDJ ) , generate aneuploid gametes that can be detected and quantified in genetic crosses . X chromosome NDJ generates diplo-X and nullo-X eggs that yield distinctive progeny classes ( matriclinous daughters and patriclinous sons ) ( Figure S1 ) in standard crosses . X NDJ frequencies were found to be highly elevated in females hemizygous for three different solo alleles , averaging 58 . 4% compared to 0% in the sibling wild-type ( WT ) control crosses ( Table 1 ) . Because the X chromosomes carried markers adjacent to and flanking the centromeres , the progeny that developed from diplo-X eggs could be analyzed for whether both X centromeres came from a pair of sister chromatids ( referred to as sister chromatid ( S ) NDJ ) or from homologous chromatids ( referred to as homolog ( H ) NDJ ) . The relative frequencies of S and H NDJ were similar for the three alleles , averaging approximately 21% S NDJ . This figure may underestimate %S because of reduced viability of the homozygous S NDJ classes relative to the heterozygous H NDJ class . NDJ of the autosomal 2nd chromosome pair was also assayed ( Table 2 ) . Because of the inviability of 2nd chromosome aneuploids , progeny derived from NDJ gametes are not recovered in crosses to chromosomally normal males . However , by crossing females to males carrying an attached-2 chromosome ( C ( 2 ) EN ) , which generate only diplo-2 and nullo-2 sperm , NDJ eggs can be recovered when fertilized by reciprocally aneuploid sperm . This assay allows detection of NDJ but does not permit calculation of a NDJ frequency as no regular gametes are recovered . Crosses of solo females to C ( 2 ) EN males yielded 2 . 5 and 3 . 0 progeny/female for two different alleles , indicating the occurrence of chromosome 2 NDJ . Heterozygous solo/+ controls yielded no progeny in similar crosses . Since two maternal 2nd chromosomes were recovered in half of the progeny , the relative frequencies of S and H NDJ could be measured . After correcting for viability differences , %S NDJ was estimated to be 32% , very near the expected frequency ( 33 . 3% ) if chromatids segregate randomly at both meiotic divisions . These results indicate that solo causes NDJ of both sex chromosomes and autosomes and suggest that the NDJ mechanism might involve random chromatid assortment . Crossover frequencies were measured in three euchromatic intervals , two ( pn-m and m-f ) that together encompass 80–85% of the recombinational length of the X chromosome and one ( cn-bw ) that encompasses about 90% of chromosome arm 2R , and in one mixed euchromatic/heterochromatic interval ( f-y+ ) on the X chromosome ( see Figures S1 and S2 ) . For the X chromosome , exchange was measured in females hemizygous for each of the three solo alleles , using heterozygous ( solo/+ ) siblings as controls to minimize background variation ( Table 3 ) . The chromosome 2 crosses were conducted similarly except that a null allele was used in place of the Df chromosome ( Table 4 ) . As the results for the three alleles did not differ significantly in either set of crosses for any of the intervals , combined results are also presented . Crossover frequencies decreased in all four intervals in the mutants , very substantially and uniformly ( 7 . 5- to 7 . 6-fold ) in the three euchromatic intervals , and more moderately ( 26% ) in the f-y+ interval that encompasses the X centromere . The 7 . 6-fold reduction in crossovers between the distal ( pn ) and proximal ( f ) euchromatic X markers in our experiments falls within the fairly wide range of reported results for strong alleles of ord ( 6 to 20-fold reductions ) and are in reasonable agreement with the reported 6 . 1-fold reduction for an ord-null genotype [47]–[49] . Based on very limited data , both solo and ord mutants cause similar reductions ( 6 to 10-fold ) in frequencies of crossovers in euchromatic autosomal intervals as well ( Table 4 ) [47] , but have much weaker effects on exchange in intervals near or encompassing centromeres [47]–[49] . However , existing data do not reveal whether ord and solo function independently of each other in controlling exchange . To determine whether a solo ord double mutant would reduce exchange any further , we generated females that were trans-heterozygous for null alleles of both genes . Crossover frequencies in the X euchromatin ( pn-f interval ) were reduced 6 . 5-fold in the double mutants relative to solo ord/+ sibling controls ( Table 3 ) , a fold-reduction value intermediate between those of ord or solo single mutants . This result suggests that solo and ord function in the same recombination pathway , one that controls about 85–90% of crossovers along the X euchromatin and probably in autosomal euchromatin as well . One way solo might function to promote homolog crossovers is by preventing recombination intermediates from being repaired by SCE . If so , solo mutations should increase SCE . Crossovers between sister chromatids cannot be detected in conventional recombination assays , but single ( or other odd number of ) crossovers between the chromatids of a circular ( or “ring” ) chromosome , generate double-ring dicentric chromosomes . In Drosophila females , the dicentrics generated by exchange between sister chromatids of a ring-X chromosome become trapped in unresolved bridges on the anaphase II spindle and are not transmitted . Since exchanges between sister chromatids of normal “rod” chromosomes have no consequence , the ratio of ring-X recovery to rod-X recovery among progeny of a ring-X/rod-X heterozygote is a rough measure of the SCE frequency . In previous studies , the ring-X/rod-X recovery ratio in WT control females ranged between 0 . 7 and 0 . 9 [30] , [35] , [45] , [55] , [56] . This likely reflects the normal background activity of the SCE pathway since in the absence of DSBs ( i . e . in a mei-P22 mutant ) , the ring-X chromosome is transmitted as efficiently as the rod-X [35] , [57] . These results also show that the meiotic apparatus in Drosophila can transmit ring chromosomes efficiently as long as they are not dicentric . Several meiotic mutants have been analyzed by this assay but to date , mutations in only one gene , ord , have significantly reduced ring-X recovery [30] , [35] , [45] , [55] , [56] . To estimate meiotic SCE frequencies in soloZ2-0198 and soloZ2-3534 females , we measured the ring/rod recovery ratio in progeny of solo/Df or +/+ females heterozygous for the ring-X chromosome Ring ( 1 ) 2 ( R ( 1 ) 2 ) . The ring/rod recovery ratios were 0 . 83 in the WT controls but only 0 . 35 and 0 . 36 in the solo crosses ( Table 5 ) . This result indicates that roughly 65 out of every 100 ring-X chromosomes were eliminated in solo meiosis . These results may actually underestimate the frequency of SCE because double ring-X crossovers , which might be quite frequent in solo mutants , yield normal mono-centric ring chromosomes which would not be detected in this assay . We conclude that solo mutations dramatically upregulate the SCE pathway , reversing the normal homolog bias to a sister bias . The recovery of both S and H NDJ progeny suggested that sister chromatid cohesion might be lost prior to the first meiotic division , as in solo males [53] . To test this idea , we used an antibody against Centromere IDentifier ( CID ) , a centromere-specific histone H3 variant [58] , [59] to examine centromere behavior during the first meiotic division in WT and solo ovaries ( Figures 1B and 1C ) . The maximum number of CID spots during the first meiotic division would be 16 if all centromeres were separate . However , sister chromatid cohesion and homolog alignment , which are essentially complete in all WT pachytene nuclei , reduce the expected number of CID spots to a maximum of four . Moreover since non-homologous centromeres tend to cluster in prophase I , observed numbers are usually even fewer [43] , [60] , [61] . As expected , in WT ovarioles , C ( 3 ) G-positive nuclei from both region 2a germaria ( early-mid-pachytene ) and stage 5–7 egg chambers ( late pachytene ) exhibited 1–4 CID foci , averaging 2 . 3 at both stages ( Figures 1B , 1D and 1E ) . In contrast , CID signals were much more numerous in solo pro-oocytes at all stages . In solo germaria only about 10% of pro-oocytes exhibited 4 or fewer spots , the remainder exhibiting 5–8 ( mean = 6 . 3 ( Figures 1C and 1D ) ) . This suggests that both homologous centromere pairing and centromere clustering were disrupted by early-mid pachytene in solo mutants but that sister chromatid cohesion remained intact at this stage . However , by late pachytene ( stage 5–7 egg chambers ) more than half of the oocyte nuclei from solo ovaries exhibited more than 8 CID spots ( Figures 1C and 1E ) , while the remainder exhibited 5–8 spots ( mean = 8 . 5 ) . Thus , in most oocyte nuclei , some sister centromere pairs had separated prematurely by the latter stages of pachytene . Very similar results were reported for an ord mutant [61] . Since prematurely separated sister centromeres are unlikely to establish mono-orientation on the spindle of the first meiotic division , these results may help explain the NDJ data . To explore the expression pattern of SOLO in the female germline , we made use of two different transgenes expressing full-length SOLO cDNAs tagged with the enhanced yellow-fluorescent protein Venus . UPS-SOLO::Venus ( UPS-SOLO ) is driven by native regulatory sequences carried in a 2 . 7 Kb fragment of upstream genomic DNA . UASp-Venus::SOLO ( UAS-SOLO ) is controlled by GAL4-responsive UAS sequences [53] . Both transgenes were able to complement the NDJ phenotype of a null solo allele but the UAS-SOLO construct did so more robustly ( Table S1 ) . A single copy of the UAS-SOLO transgene , when expressed under control of the germline-specific driver nos-GAL4::VP16 in a solo background , fully suppressed X chromosome NDJ . However , solo females carrying two to four copies of UPS-SOLO still underwent NDJ at modest but significant frequencies ( 7–11% ) . This difference cannot be explained by the location of the Venus tag because the C-terminally tagged SOLO protein completely rescued NDJ when expressed under control of nos-GAL4::VP16 ( Table S1 ) so may reflect a deficiency in expression level or pattern . In whole-mount ovarioles prepared from females lacking any functional copies of native solo , UPS-SOLO and UAS-SOLO exhibited overlapping but non-identical localization patterns ( Figures 2A and 2D ) . Both proteins were expressed only in germ cells and in all regions of the germarium except for the anteriormost segment of region 1 . The only really striking difference between the UPS-SOLO and UAS-SOLO expression patterns in whole-mount preparations was the considerably higher level of UAS-SOLO expression in a broad anterior domain that encompassed most of region 1 ( except for the anterior tip ) and anterior region 2a . As this domain coincides with the domain of highest expression of nos-GAL4 , this is probably an ectopic over-expression effect . In nearly all germ cells , both UAS-SOLO and UPS-SOLO exhibited small numbers of prominent bright nuclear foci and a broad diffuse pattern that appeared to encompass both cytoplasm and nucleus . In addition , some nuclei exhibited much fainter fibrillar or linear staining ( discussed below ) . A distinctive aspect of the bright focal and diffuse staining patterns was the uniformity of expression level within cysts , indicating strong expression in both nurse and meiotic cells . Similar expression patterns were previously reported for ORD and the SMC cohesins [30] , [43] . Bright foci of both UPS-SOLO and UAS-SOLO were observed in all germ cell nuclei in regions 2a , 2b and 3 of germaria and in egg chambers through at least stage 5 . ( UAS-SOLO signals have been detected as late as stage 8 ( data not shown ) ) . Fainter foci were also seen in some pre-meiotic nuclei in the posterior half of region 1 . Most nuclei exhibited one to four SOLO foci per nucleus , suggesting that the foci may correspond to centromeres . This idea was tested by staining UPS-SOLO-expressing ovarioles with an antibody against CID . As shown in Figures 2B and 2C for germarial region 2b and a stage 5 egg chamber , all of the bright UPS-SOLO foci aligned with anti-CID signals , confirming that SOLO is enriched in the vicinity of centromeres in female germ cells . However , at higher magnification , the overlap between UPS-SOLO and CID foci sometimes appeared only partial ( Figure 2C , inset ) suggesting that SOLO may be enriched at pericentromeric domains as well as centromeric domains . UAS-SOLO foci aligned with anti-CID foci as well ( data not shown ) . SMC1 and SMC3 have been shown to be highly enriched on centromeres of female germ cells at similar stages [43] . To confirm co-enrichment of SOLO and SMC1 in females , we stained germaria expressing UAS-SOLO with an antibody against SMC1 . As expected , the SMC1 signals formed bright nuclear foci throughout the germarium from posterior region 1 through region 3 in both meiotic cells and nurse cells ( Figure 2D ) . As reported previously [43] , and like SOLO , SMC1 signals were absent from the anterior tip of the germarium where germ line stem cells and cystoblasts reside . It is evident from Figure 2D that the bright SMC1 and UAS-SOLO foci overlap very extensively in germaria . They also overlap in later stages ( data not shown ) . Thus , SOLO and SMC1 are co-enriched on meiotic centromeres in females as well as in males . To test whether the centromeric SMC1 foci depend on solo , WT and solo germaria were stained with anti-SMC1 antibody . Whereas prominent SMC1 foci were present throughout the WT germarium , no SMC1 foci were detected in any nucleus in the solo germarium ( Figure 2E ) . SMC1 foci were also absent from solo oocyte nuclei in later stages ( data not shown ) . However , SMC1 staining did not disappear in solo germ cells . Diffuse staining was apparent in many germ cells in both WT and solo germaria , and appeared to be associated with chromosome arms ( Figure 2E , arrowheads , insets ) . This staining pattern is explored further below . Thus , in female meiosis as in male meiosis , enrichment of the SMC1 subunit of cohesin at centromere regions is dependent on solo . However , SMC1 can localize to chromosome arms in the absence of solo . The findings that SOLO and SMC1 are co-enriched on centromeres and that SOLO is required for SMC1 localization to centromeres suggest that they may interact physically . In order to address this issue , we generated transgenic flies that express a full-length SOLO cDNA with tandem 3XFLAG and 3XHA tags at its N-terminus regulated by UAS sequences . One copy of this transgene completely reverted the NDJ phenotypes of solo males and females ( Table S1 ) when induced by the germline-specific driver nos-GAL4::VP16 , indicating that the FH::SOLO fusion protein is fully functional . Western blots revealed high level expression of FH::SOLO in ovaries . The absence of signal in the lane derived from y w ( control stock lacking transgene ) ovary extracts confirms the specificity of the anti-FLAG antibody ( Figure 3A ) . In the co-immunoprecipitation experiment , FH::SOLO was pulled down from extracts of transgenic ovaries by the anti-SMC1 antibody ( Figure 3B ) used in immunofluorescence experiments in this and previous studies [53] , [54] but not by host control serum ( Figure 3C ) . To rule out the possibility that FH::SOLO could be precipitated by cross-reactivity from the anti-SMC1 antibody , the reciprocal immunoprecipitation , i . e . , using anti-FLAG antibody to immunoprecipitate SMC1 , was carried out and the result showed that SMC1 was co-immunoprecipitated by anti-FLAG antibody ( Figure 3D ) . Our results demonstrate that SOLO associates in vivo with SMC1 , one of the core components of the cohesin complex . The whole-mount preparations of germaria in Figures 2D , 2E and 4A show prominent linear signals of SMC1 and C ( 3 ) G in a subset of germ cell nuclei throughout regions 2–3 . Based on previous studies , these structures are presumed to correspond to the LEs and central regions , respectively , of SCs [30] , [33] , [43] , [45] , [60]–[63] . Although it was less obvious in whole mount preparations , SOLO also localized to linear structures in pro-oocytes and oocytes ( Figure 2D , arrowheads , and Figure 4A , arrows ) . To permit detailed comparisons of these patterns , chromosome spread preparations from UAS-SOLO germaria were stained with antibodies against C ( 3 ) G or SMC1 . Linear UAS-SOLO signals , presumed to represent staining of chromosome arms , could be clearly seen in meiotic cells ( Figures 4B and 4D , arrows ) , as identified by C ( 3 ) G or SMC1 linear structures , but were not confined to the meiotic cells . Thinner linear signals could be discerned in many pro-nurse cells in the same cysts ( arrowheads ) . The same was true for SMC1 ( Figure 4D , arrowhead ) , as previously reported [43] , but not for C ( 3 ) G ( Figure 4B , arrowheads ) , which is expressed in a meiosis-specific pattern . The thin linear UAS-SOLO and SMC1 signals in pro-nurse cells ( Figure 4D , arrowhead ) appeared to co-align extensively , similar to ORD and SMC1 [43] . Detailed comparisons of the ribbon-like localization patterns of UAS-SOLO with those of C ( 3 ) G and SMC1 in pro-oocytes were possible from magnified images such as those in Figures 4C and 4E . It is apparent from these images that the ribbon-like UAS-SOLO signals overlap quite extensively with the corresponding structures of SMC1 and C ( 3 ) G . The overlap is nearly complete for UAS-SOLO and SMC1 . Although there were a few prominent segments that exhibited stronger SMC1 signals than UAS-SOLO signals ( Figure 4E , arrowheads ) and other segments with the reverse pattern , there were no segments of significant length that stained with SMC1 but not UAS-SOLO or vice versa . The overlap between UAS-SOLO and C ( 3 ) G was also very substantial but with more segments in which staining was quite unequal ( Figure 4C ) . These results suggest that SOLO is widely distributed along SCs during pachytene and closely aligned with the cohesin SMC1 , a pattern consistent with a possible role of SOLO as a component of Drosophila LEs . To be sure that the results with the ectopically-driven UAS-SOLO were physiologically meaningful , we also carried out chromosome spread experiments using UPS-SOLO germ cells stained with anti-C ( 3 ) G ( Figure S3 ) . Like UAS-SOLO , UPS-SOLO localized to chromosome arms in pro-nurse cells ( lower panels ) and along C ( 3 ) G ribbon-like structures in pro-oocytes and oocytes ( upper panels ) . However , UPS-SOLO signals were weaker than UAS-SOLO signals , and staining of the LEs was patchy and discontinuous rather than continuous . It is unclear at this point which pattern is correct . The fact that UPS-SOLO failed to fully rescue the X NDJ phenotype may indicate that its expression level is lower than the native gene . However , we cannot rule out the possibility that the more continuous SC labeling pattern of UAS-SOLO is due to overexpression and is therefore misleading . A transgene that expresses SOLO at native levels and fully rescues solo mutants will be required to resolve this question . Overall , these data indicate that SOLO localizes along chromosome arms in a pattern largely parallel to that of SC proteins , suggesting it may have a role in SC formation . To assess the effects of solo mutations on SC formation , we stained dissected ovaries with antibodies against C ( 3 ) G and ORB . ORB is a cytoplasmic protein that is present in all cells in most pachytene cysts , but substantially enriched in pro-oocytes and oocytes [64] . Synapsis phenotypes were analyzed for two different solo alleles ( soloZ2-0198 and soloZ2-3534 ) , both of which are genetic null alleles for the NDJ phenotypes [53] ( Table 1 and unpublished data ) . In solo mutant germaria , both ORB-staining and C ( 3 ) G staining were significantly reduced relative to WT germaria . The reduction in staining resulted from two distinct phenotypes: first , a substantial reduction in the numbers of germ-cell cysts per germarium; and second , reduced and/or morphologically abnormal C ( 3 ) G staining in many pro-oocytes and oocytes ( Figures 5 and S4 ) . However , no defect in oocyte specification was observed . Cysts in region 3 and later stages nearly always had only one cell with enriched ORB staining and no more than one cell with C ( 3 ) G staining , ( e . g . , Figures 5B , 5C , S5B and S6B ) although C ( 3 ) G staining could be completely absent ( e . g . , Figure 5C ) , as described below . Further analysis revealed that the first phenotype is due not to loss of solo function but instead to an unexpected and , as yet , unexplained inhibitory effect of the solo alleles on expression of vasa , a gene with an overlapping transcription unit that is required for early germ-cell development [53] , [65] . Expression of a GFP-VAS transgene in solo/Df females substantially improved the germ-cell cyst number phenotype ( Figure S5 ) and nearly doubled fertility ( Table S2 ) but did not improve either the abnormal C ( 3 ) G staining patterns ( second phenotype ) ( Figure S6 ) or the fidelity of chromosome segregation ( Table S2 ) . This shows that the abnormal C ( 3 ) G staining patterns are due to loss of solo function , not to reduced vasa function , and will be our focus in the following sections . The C ( 3 ) G staining defects caused by the solo mutations were observed in cells with enriched ORB staining , marking them as pro-oocytes or oocytes , and fell into three main phenotypic categories: i ) cells with partial or fragmentary staining; ii ) cells with no linear segments at all but only C ( 3 ) G foci ( spotty staining ) ; and iii ) cells that should have exhibited C ( 3 ) G staining based on ORB-staining but did not ( no staining ) ( Figures 5B , 5C and S6B ) . A fourth category consisted of cells with nuclei that appeared to be fully stained and did not exhibit any obvious fragmentation; these were referred to as “normal-like” even though the staining patterns in these cells were often less clearly defined than in WT . Quantitative analysis showed that the three abnormal patterns , fragmentary , spotty , and no staining , were present at highly elevated frequencies , compared to WT , at all pachytene stages in solo germaria ( Figure 5D ) . The quantitative analysis also revealed a progressive deterioration in C ( 3 ) G staining with increasing age of cyst . 30–40% of ORB-enriched cells in regions 2a or 2b exhibited normal-like C ( 3 ) G staining but that frequency declined to less than 10% by region 3 . Some C ( 3 ) G staining persisted in some late pachytene oocytes ( e . g . , Figure S4 ) , but many lacked staining altogether . Staining defects were not limited to C ( 3 ) G . SMC1 staining patterns exhibited a similar spectrum of defects ( Figure 5E ) with very similar frequencies of staining categories ( data not shown ) . Moreover , when the C ( 3 ) G and SMC1 staining patterns were compared in the same cells by dual immunostaining , the patterns were very similar , as illustrated by the solo panel series in Figure 5E . Overall , these data indicate that solo mutants cause fragmentation and degeneration of LEs and SCs from the onset of pachytene and that these phenotypes worsen as cysts age . The phenotypes caused by mutations in solo and ord are very similar in most respects , including the progressive fragmentation and disintegration of both SCs and chromosome cores during pachytene [30] , [43] . However , there is a significant difference in the time of onset of abnormalities between solo and ord mutants . Whereas the phenotype is already present at high frequency in region 2a in solo mutants , it doesn't manifest to a significant degree until late stage 2a/stage 2b in ord mutants . To better understand the relationship between these phenotypes , we constructed solo ord double mutants and compared the C ( 3 ) G staining patterns to those in ord and solo single mutants . Whereas ord germaria exhibited normal C ( 3 ) G staining in region 2a , abnormal C ( 3 ) G staining patterns were seen in solo ord germaria at all stages ( Figure S7 ) and did not differ significantly from the pattern in solo mutants ( Figure 5D ) . Why solo mutants disrupt synapsis earlier in pachytene than ord mutants remains to be determined . The effect of solo on homolog exchange could reflect a defect either in formation or repair of meiotic DSBs . To address these possibilities , DSB frequencies were estimated in pro-oocyte and oocyte nuclei in solo and WT germaria using an antibody against γ-H2Av , a phosphorylated form of the histone variant H2Av protein that becomes enriched around DSBs shortly after their formation and that disappears when DSBs are repaired [66] , [67] . γ-H2Av foci and/or short stretches were absent in region 1 germ cells from both solo and WT germaria but were present in pro-oocyte nuclei in regions 2a and 2b in both genotypes , consistent with previous reports [36] , [68] . Although solo germaria exhibited fewer total foci than WT germaria , the two genotypes did not differ significantly in mean number of foci per pro-oocyte nucleus , indicating that the DSB formation is not impaired in solo mutants ( Figures 6A and 6B ) . By contrast , unlike in WT , γ-H2Av foci were not restricted to region 2 in solo germaria . All 24 ORB-stained region 3 oocytes that were scored in solo germaria exhibited foci . The mean focus numbers did not differ significantly between region 3 and region 2 ( Figures 6A , 6B and S8 ) , suggesting a delay in DNA repair . However , foci did not persist beyond region 3; nearly all stage 2 oocytes and all stage 3 oocytes in solo mutants lacked γ-H2Av signals ( Figure 6C ) . In this regard , solo mutants differ from DSB repair pathway mutants such as spnA , spnB and spnD , in which γ-H2Av foci persist until late pachytene [36] , [68]–[71] . In principle , the delayed disappearance of γ-H2Av foci in solo mutants could reflect delayed germ cell development due to the effect of solo mutations on vasa function . In other words , if most region 3 oocytes in solo germaria are really at a developmental age typical of region 2a or 2b pro-oocytes in WT , then the persistence of foci in region 3 would have a trivial explanation . If this were the case , one would expect to see other evidence of delayed development such as failure to restrict ORB staining to a single cell . However , as described above , this was not the case . Nevertheless , to be sure that reduced vasa expression was not somehow responsible for the delayed disappearance of γ-H2Av foci , we compared the γ-H2Av phenotypes of solo; GFP::VAS and solo females . Similar to solo mutants , γ-H2Av foci persisted in region 3 oocytes but were absent in stage 2 oocytes of solo/Df; GFP::VAS/+ ( Figure S9 ) and solo/Df; GFP::VAS/GFP::VAS ( data not shown ) . Thus the delayed disappearance of γ-H2Av foci exhibited by solo mutant females is not due to the effect of the solo mutation on vasa function . These results indicate that solo mutations have no effect on DSB formation but cause a transient delay in DSB repair . The cause of this delay and its significance with respect to the recombination phenotype of solo mutants are unknown .
Our previous analysis of solo in Drosophila male meiosis showed it to be essential for meiotic centromere cohesion and centromere orientation . However , the idiosyncratic homolog segregation mechanism in males precluded analysis of roles of solo in homolog interactions [53] , [54] . In this study we analyzed the role of solo in female meiosis and found that solo mutations disrupt a much broader range of meiotic processes in females , including centromere clustering , homologous centromere pairing , sister centromere cohesion , sister centromere mono-orientation , SC and lateral element stability , homolog exchange , and homolog bias . Moreover , SOLO protein localized to chromosome arms and along the LEs of the SCs as well as to centromeres in female meiosis . These results indicate that SOLO contributes to multiple sister chromatid and homolog interactions that underlie meiotic chromosome segregation . solo mutations severely disrupted chromosome segregation , causing X chromosome NDJ at frequencies in excess of 50% ( Table 1 ) . The NDJ pattern , a 1∶2 ratio of sister chromatid to homolog NDJ seen also in male solo mutants and ord mutants of both sexes , is consistent with random chromatid assortment caused by loss of centromere cohesion prior to prometaphase I [48] , [50] , [53] . Centromere cohesion was visibly impaired by late pachytene in solo females , based on CID spot numbers that consistently exceeded eight per cell ( Figure 1 ) . Similar observations were reported for ord mutants in female and male meiosis [52] , [61] and solo mutants in male meiosis [53] . Although cytological analysis of segregation in solo females has not been undertaken , FISH analysis in solo males revealed random co-segregation of chromatids at anaphase I , fully separated chromatids by mid-anaphase I and chaotic segregation at anaphase II [53] and several cytological studies of segregation in ord males and females have documented premature sister chromatid separation and disorderly segregation behavior [47]–[52] , [72] . The mechanism by which solo controls centromere cohesion seems likely to involve cohesin . In male meiosis , SOLO , ORD and SMC1 are enriched on centromeres until anaphase II and all three proteins depend on the Shugoshin ortholog MEI-S332 for maintenance on centromeres after metaphase I [43] , [52] , [53] . In female meiosis , SOLO , ORD , SMC1 and SMC3 are all enriched on centromeres in female meiosis throughout pachytene ( Figures 2 and 4 ) [30] , [43] . When either solo or ord is mutated , no centromeric SMC cohesin foci have been detected at any stage in either sex ( Figure 2 ) [43] , [53] with the consequences summarized above . These data are consistent with the hypothesis that centromere cohesion is mediated in male and female meiosis by centromere enrichment of a cohesin complex dependent on both SOLO and ORD . However , there has been no direct demonstration that the cohesive roles of SOLO and ORD are limited entirely to regulating cohesin . There also remains no direct evidence that any of these proteins – SMC1 , SMC3 , ORD or SOLO – persists on centromeres after pachytene . That may be a technical detection issue of some sort but until such evidence is obtained , the possibility that cohesion is maintained during the division stages in female meiosis by some other complex cannot be ruled out . It is worth noting that centromere cohesion persisted intact throughout early and mid-pachytene in solo mutants despite the absence of detectable SMC1 centromere foci at any stage ( Figure 1 ) . Similar observations have been reported for solo male meiosis and ord male and female meiosis [51]–[53] , [60] , [61] , [73] and indicate the existence of centromere cohesion that is independent of both SOLO and ORD and perhaps of cohesin ( although the possible presence of low levels of cohesin near centromeres in solo and ord mutants cannot be ruled out ) . Whether this early prophase cohesion is based on a protein complex or on chromatid entanglement remains to be determined . Homologous centromeres are paired in nearly all germ cells and they further coalesce into 1–3 clusters at the onset of meiosis in pro-oocytes and remain paired and clustered throughout prophase I [43] , [60] , [61] , [74] . There is considerable evidence that centromeric or heterochromatic associations between homologs underlie the robust achiasmate segregation system in Drosophila [74]–[77] . Moreover , centromere clusters serve as the first synapsis initiation sites during zygotene , accumulating the transverse filament protein C ( 3 ) G and the central element protein CONA [34] , [60] , [61] , [78] . Both pairing and clustering ( as well as synapsis initiation ) was shown to depend on ord [43] , [60] , [61] . Here we demonstrate that solo is also required for these events . In early-mid pachytene solo pro-oocytes exhibited 6 . 3 foci per nucleus compared to 2 . 3 in WT , indicating substantial loss of pairing and clustering ( Figure 1 ) . Since SOLO and ORD are required for centromere enrichment of SMC1 as well as for centromere pairing and clustering , a logical inference is that centromere pairing is also mediated by cohesin , as previously suggested [60] . This suggestion is supported by evidence that centromere pairing is weakened in certain chromosomal backgrounds by reducing SMC1 gene copy number [79] . The mechanism by which cohesin mediates pairing and clustering is not known . Clustering may involve recruitment of SC proteins since mutations in c ( 3 ) G and cona abolished clustering [61] . However , c ( 3 ) G and cona mutations had much weaker effects on centromere pairing suggesting other mechanisms are probably involved in this process . Interestingly , yeast REC8 is also required for centromere pairing ( called coupling ) in early prophase I and promotes pairing by recruiting the yeast version of C ( 3 ) G , ZIP1 [80] . However , the relevance is not clear since centromere coupling in yeast is entirely promiscuous whereas Drosophila pairing is homologous [51]–[53] , [73]–[76] . The mechanistic relationship between cohesion and centromere pairing remains to be elucidated . Given the association between centromere pairing and synapsis , it will also be of interest to investigate the role of solo in synapsis initiation . What are the roles of ORD and SOLO in cohesin function ? Neither protein exhibits significant homology to any of the four cohesin protein families [49] , [53] , yet they appear to co-localize with SMC cohesins and are required for enrichment of SMC cohesins at centromeres . We favor the idea that SOLO and ORD are subunits of a meiosis-specific cohesion complex that includes the SMC subunits . ORD and SOLO may function to replace the canonical non-SMC subunits which , with the exception of C ( 2 ) M , have yet to be identified in Drosophila meiosis . Our finding that SOLO and SMC1 reciprocally co-immunoprecipitate from ovarian protein extracts is consistent with this idea but also with alternatives such as that SOLO is a regulator rather than a subunit of cohesin . More detailed biochemical analyses will be required to resolve the composition of Drosophila meiotic cohesin and to clarify the roles of SOLO and ORD . Cohesion between sister chromatid axes is clearly essential for maintenance of chiasmata but its role in early prophase I events such as homolog pairing , synapsis and meiotic recombination is unclear . In WT Drosophila , FISH studies indicate that sister chromatid arm sequences are tightly cohesive throughout prophase I [30] , [74] , but the genetic basis for arm cohesion remains to be elucidated . In c ( 2 ) M mutants , recombinant chromatids were not recovered in NDJ gametes , suggesting that chiasmata are stable and can bi-orient bivalents [45] . In ord mutants , absence of metaphase I arrest indicated an absence of chiasmata [51] . Presumably this implies that ORD also provides arm cohesion during prophase I and C ( 2 ) M does not , but direct evidence is lacking . ORD and the SMC cohesins are abundant on chromosome arms in all cells in 16-cell germ-line cysts , but ord mutants have little if any effect on intensity of SMC1/3 arm staining even in pro-oocytes and oocytes with fragmented cores [43] . Moreover , the limited FISH analysis that has been carried out thus far has not detected any disruption of arm cohesion during prophase I in ord mutants [30] . Our data show that SOLO is also expressed in all cells in 16-cell germline cysts and localizes to chromosome arms in both pro-nurse cells and pro-oocytes and oocytes . UAS-SOLO and UPS-SOLO are fully consistent in this respect ( Figures 2 , 4 and S3 ) . In spread preparations co-stained with anti-SMC1 it is quite clear that the two proteins co-align very strongly even though the staining lines are thin ( Figure 4D ) . These data suggest that SOLO may also be involved in arm cohesion . This is an important question for future research because the roles of solo in synapsis , chromosome core stability and recombination could be related to its role in arm cohesion . Our data show that SOLO localizes to extended ribbon-like structures on the chromosome arms of pro-oocytes and oocytes , where it co-aligns with both SMC1 and C ( 3 ) G . This localization pattern is unlikely to be an artifact since it was seen with both UAS-SOLO and UPS-SOLO . However , it remains unclear whether the true pattern is the continuous staining pattern seen with UAS-SOLO or the discontinuous pattern seen with UPS-SOLO . Since UAS-SOLO appeared to be somewhat overexpressed in anterior 2a , the continuous localization could be an overexpression artifact . However , since UPS-SOLO did not fully rescue X chromosome NDJ in solo females , the discontinuous localization pattern could be an underexpression artifact . For now , we favor the continuous pattern in part because ORD localizes continuously [30] , [43] and the phenotypes of ord and solo are so similar that sharply different localization patterns seem unlikely . Ascertaining the true localization pattern is an important goal . Where exactly does SOLO localize ? Overall , SOLO appeared to align slightly better with SMC1 than with C ( 3 ) G . However , this difference is not large and would not in itself suffice to assign SOLO to the LEs rather than the central regions . There are two independent reasons to favor the LEs . First , the close alignment of SOLO and SMC1 signals along unsynapsed chromosome arms of germ cells would likely persist during core assembly ( Figure 4 ) . Second , the highly correlated SMC1 and C ( 3 ) G staining phenotypes in solo mutants suggest that solo controls chromosome core stability directly rather than indirectly through effects on the central region , as even null mutations in c ( 3 ) G do not perturb chromosome core integrity ( Figure 5 ) [45] . In other eukaryotes a distinction is often made between chromosome core proteins , which are cohesins , and non-core AE/LE proteins such as RED1 , HOP1 , SYCP-2 , SYCP-3 , etc . , and there has been a spirited debate about how the two groups of proteins are organized relative to each other [6] , [27]–[32] , [37]–[42] . For Drosophila , the distinction would seem artificial at this point . The only proteins identified thus far that localize to the LEs – SMC1 , SMC3 , C ( 2 ) M , ORD , Nipped-B and SOLO – are all either cohesins or cohesion proteins with very close links to cohesins , and therefore seem likely to be components of the cores [30] , [43] , [45] , [53] , [60]–[63] . Our working model is that SOLO and ORD function as subunits of a cohesin complex that is distributed along the chromosome arms of all germ cells and likely provides cohesion between the sister chromatid axes . We do not dismiss the possibility that SOLO/ORD cohesin maintains cohesion in the chromatin loops as well but evidence has been presented that SMC cohesins are mostly confined to the axes in Drosophila germ cells [43] . In meiotic cells these arm cohesins condense along with C ( 2 ) M-cohesin ( and perhaps other complexes ) and assemble into continuous cores that underpin synapsis and SC formation . SOLO and ORD are unlikely to be components of different cohesin complexes since core stability was no worse when both ORD and SOLO were absent than when just SOLO was absent ( Figures 5 and S7 ) . Thus cores may consist of two cohesin complexes , one anchored by C ( 2 ) M and one anchored by ORD and SOLO . Additional cohesin complexes involving mitotic cohesins such as RAD21 might be present as well . Our observation that pro-oocytes with fragmented , patchy or no SMC1 and C ( 3 ) G staining are abundant even at the earliest stages of pachytene in solo mutants could indicate a requirement for SOLO in assembly of cores . In addition , the progressive degeneration of cores throughout early and mid-pachytene in solo mutants might indicate a possible role in core maintenance as suggested for ord [30] , [43] . A role of SOLO in core assembly seems unlikely . Full-length cores can be assembled in the absence of SOLO or of both ORD and SOLO ( Figures 5 and S7 ) [30] , [43] . However , no cores are assembled in the absence of C ( 2 ) M , suggesting that C ( 2 ) M is the motor for assembly and that SOLO and ORD play passive roles [43] , [45] , [60] . A maintenance function is plausible but not especially compelling since it doesn't relate in any direct way to the primary function of SOLO . In our model , SOLO is a subunit of arm cohesin complexes that become assembled into cores in meiotic cells . This would make SOLO a structural component of WT cores and its absence would be expected to compromise core structure in one of two ways . First , cores assembled with abnormal ( i . e . , SOLO-deficient ) cohesins might be less stable than WT cores and prone to breakage or disassembly . Second , exclusion of deficient cohesins from core assembly would likely lead to monolithic cores which might lack important structural or functional properties such as flexibility or ability to complete exchanges with homologous cores . A major strength of this hypothesis is that it does not require a fundamentally different explanation for the solo and ord phenotypes , just a difference in degree of instability of the cores . If absence of SOLO is for some reason more destabilizing than absence of ORD , then it could trigger core degeneration at earlier stages of meiosis . One way this could work is based on our proposal that SOLO and ORD are subunits of the same cohesin complex . The effect of loss of a subunit on complex stability depends on the specific role of that subunit . For example , absence of the kleisin subunit is more destabilizing for conventional cohesin than absence of the SA subunit . In solo mutants , all of the assembled cores in early pachytene must be defective but actual fragmentation and dissolution does not begin until later in pachytene in some cells . In other cells , dissolution is already complete in region 2a . This suggests that the defect creates a fragile state and that onset of degeneration may require a stressful event of some kind to trigger it , as suggested for ord [43] . The cell-to-cell variability in phenotype could reflect stochastic variation in degree of fragility , or perhaps cell-to-cell variation in the numbers or intensity of stressors . SOLO is required for completion of DSB repair on the normal schedule although the repair delay is brief compared with the delays caused by mutations in components of the DSB repair pathway ( 36 , 68–71 ) . Mutations in other Drosophila chromosome core components such as c ( 2 ) M and ord have no effect at all on DSB repair ( 30 , 36 , 68 ) . This is somewhat surprising in light of the often severe DSB repair defects seen in cohesin mutants in other eukaryotes ( 2 , 7–9 , 13 , 14 ) . Additional studies will be required to determine if the transient repair delay in solo mutants contributes to its recombination phenotype . Our data indicate that SOLO promotes homolog exchange and suppresses SCE ( Tables 3–5 ) . As SCE and homolog exchange are alternative pathways for DSB repair , suppressing SCE is likely to promote homolog exchange; direct molecular analysis of recombination intermediates in yeast confirms this [23] , [24] . We conclude that a major role of SOLO in recombination is to regulate homolog bias , although this does not preclude SOLO acting in other ways to promote homolog exchange . How might SOLO regulate homolog bias ? There is a bit of a conundrum here: the primary function of SOLO is cohesion and although cohesion is very effective at promoting DSB repair , it does so by promoting SCE , presumably by reinforcing sister chromatid proximity [81] . REC8 becomes depleted around crossover sites presumably because it promotes SCE [82] . Moreover , in yeast , rec8 mutations promote homolog bias , not sister bias [24] . Therefore , simply providing extra cohesion at a recombination site is more likely to inhibit homolog exchange than to promote it . An alternative is that the chromosome cores per se are responsible for suppressing SCE . Several recent models have postulated that AE/LEs serve as “barriers to sister chromatid repair” ( BSCR ) [28] , [29] . This mechanism seems unlikely to apply to Drosophila because c ( 2 ) M mutations completely abrogate core assembly but do not de-repress SCE at all [45] . Our proposal is that SOLO/ORD-cohesin is an unconventional cohesin that is able to flexibly regulate cohesion in the context of meiotic recombination . It becomes enriched at future DSB sites , perhaps specifically at future crossover sites , during the synapsis initiation process , where it regulates the cohesive status of chromatids involved in the recombination reaction to promote inter-homolog exchanges . For example , relaxation of cohesion between the broken chromatid and its sister may be necessary to allow a homology search and inter-homolog strand invasion [24] . We speculate that ORD/SOLO-cohesin is able to rapidly switch to a “cohesion-off” mode in response to local signaling related to DSB or recombination intermediate status . In doing so , ORD/SOLO-cohesin might be able to promote homolog exchange locally while still maintaining cohesion globally . In conclusion , SOLO is a meiotic cohesion protein with major roles in centromere cohesion , chromosome core integrity and homolog bias . It is enriched at centromeres and chromosome cores and interacts with the SMC1 cohesin subunit . Further investigation of SOLO's meiotic functions is expected to provide insight into the roles of cohesion in inter-homolog interactions .
The solo mutants used in this paper were described previously [53] . soloZ2-0338 , soloZ2-0198 and soloZ2-3534 are single-base substitutions predicted to insert stop codons in the SOLO coding sequence and truncate the proteins at amino acid positions 173 , 387 and 1010 ( out of 1031 ) , respectively [53] . All three alleles are considered to be functionally null with respect to chromosome segregation . Although a closely-linked semi-lethal mutation has thus far prevented accurate measurement of NDJ in soloZ2-3534 homozygotes , both male and female sex chromosome NDJ frequencies in the other two homozygotes and in all three hemizygotes are statistically indistinguishable and consistent with random chromatid segregation [53] ( Table1 , unpublished data ) . The b vas7 stock was obtained from M . Ashburner ( Cambridge University , England ) . The X chromosome mapping stock y pn cv m f . y+/FM7c was provided by K . McKim ( The state University of New Jersey ) . ord5 and Df ( 2R ) WI370 were donated by S . E . Bickel ( Dartmouth College ) . The GFP::VAS transgenic line was provided by P . Lasko ( McGill University ) . Other flies were from the Bloomington Drosophila Stock Center at Indiana University . Unless otherwise specified , the females being tested were crossed singly to two males in shell vials . All flies were maintained at 23°C on standard cornmeal molasses medium . Parents were removed from the vial on day 10 and progeny were counted between day 13 and day 22 . The methods for analyzing NDJ and recombination on the X and second chromosomes are explained and illustrated in Figures S1 and S2 and in Tables 1–4 and S1 . To accurately estimate the relative frequencies of sister and homolog NDJ , it is necessary to correct for the reduced viability of the sister NDJ classes which are homozygous for most or all of chromosome 2 , relative to the homolog NDJ classes , which are heterozygous . The viability test was based on recoveries of the homozygote and heterozygote progeny classes from two crosses: soloZ2-0198 cn bw/b vas7 males crossed to soloZ2-0198 cn bw/Cy females and soloZ2-0198 cn bw/b vas7 males crossed to b vas7/Cy females . The viabilities of b vas7 and soloZ2-0198 cn bw homozygotes were found to be 51 . 76% and 63 . 49% , respectively , compared to their heterozygous siblings ( b/cn bw ) . Plugging the decimal versions of those correction factors into the formula for %S NDJ gives %S = 100× ( 144/0 . 5176+106/0 . 6349+37 ) / ( ( 144/0 . 5176+106/0 . 6349+37 ) + ( 1012+36 ) ) = 32% . R ( 1 ) 2 , y1 f1/BSYy+ males were crossed to Df ( 2L ) A267 , b cn bw/CyO , cn females . The R ( 1 ) 2 , y1 f1/+; Df ( 2L ) A267 , b cn bw/+ F1 female progeny were crossed to y w/Y; solo , cn bw/CyO , b cn males to generate F2 R ( 1 ) 2 , y1/y w; Df ( 2L ) A267 , b cn bw/solo , cn bw females and sibling control R ( 1 ) 2 , y1/y w; +/CyO , b cn females . These F2 females were crossed to w1118/Y males and their progeny scored for the ring-X ( w+ ) and rod-X ( w ) . The crosses were carried out without an X chromosome balancer to enable estimation of SCE frequencies under conditions in which both homolog and sister chromatid exchanges were free to occur . The ring-X chromosome was tracked using the y/y+ marker in the F1 cross and the w/w+ marker in the F2 ( test ) cross . The y1 allele on the ring-X chromosome is recombinationally inseparable from the centromere , and w , which is 1 . 5 cM from y , does not recombine with y at appreciable rates in ring/rod heterozygotes where only double exchanges can be recovered ( unpublished data ) . In the F1 cross , cn was used as a proxy for Df ( 2L ) A267 . solo/Df F2 females were sorted by Cy cn+ phenotype and verified ( or not ) on the basis of fertility and NDJ . Only regular ( disjunctional ) progeny were used to calculate the ring/rod recovery ratio . pENTR-Ntag-SOLO entry vector [53] was recombined into Gateway P-element vector pPFH ( Drosophila Genomics Resource Center ( BDGC ) ) , generating the germ line transformation vector P{w+mC UASp-FH::SOLO} , which contains tandem 3XFLAG and 3XHA tags at the N-terminus of SOLO fusion protein . The construct was transformed into w1118 flies ( BestGene Inc . ) . Transgenes were mapped by standard methods and tested for ability to suppress X chromosome NDJ in solo females when expressed with the nos-GAL4::VP16 driver [83] ( see Table S1 , lines 4 and 5 ) . FH::SOLO expression was induced by nos-GAL4::VP16 in Drosophila females and 100 pairs of ovaries were collected with 1X PBS ( pH 7 . 4 ) . Ovaries of y w and transgenic flies were lysed using 500 µL of NP40 Cell Lysis Buffer ( Invitrogen ) . The lysates were centrifuged at least 4 times each at 13 , 000 g for 10 minutes to remove tissue debris and the supernatants were used for Western blots and immunoprecipitations . Before immunoprecipitating , lysates were first pre-cleared with rabbit serum . 4 µL of rabbit serum ( 159 mg/ml , Sigma ) were added to the 500 µL lysates and rocked for 1 hr at 4°C , then the lysates with rabbit serum were added to 100 µl of protein A agarose beads ( Invitrogen ) which had been washed 5 times with wash buffer ( 1 mM PMSF , 1 mM DTT , 1X PI ( Protease Inhibitor ( Roche ) ) , 10% glycerol , 10 mM NaCl , 1X PBS , pH 7 . 4 ) rocking for 30 minutes at 4°C . To immunoprecipitate FH::SOLO with anti-SMC1 , 50 µl of pre-cleared lysates were incubated with 20 µl of anti-SMC1 rabbit antibody ( 1 . 03 mg/ml ) or rabbit serum ( 1 . 06 mg/ml , diluted from original serum ) and IP solution ( 1 mM PMSF , 1 mM DTT , 1X PI ( Protease Inhibitor ( Roche ) ) , 10% glycerol , 1X PBS , pH 7 . 4 ) rocking for 4 hrs . The lysates with anti-SMC1 antibody or serum were then added to 80 µL of washed protein A agarose beads and rocked overnight in a cold room at 4°C . To immunoprecipitate SMC1 by FH::SOLO , 50 µl of lysate pre-cleared with mouse serum and protein G agarose beads ( similar procedure to rabbit serum ) were incubated with 30 µL of anti-FLAG M2 ( 1 mg/ml , Sigma ) or mouse serum ( 1 . 10 mg/ml , diluted from original serum ) and the IP solution was rocked for 4 hrs . Lysates with anti-SMC1 antibody or serum were then added to 100 µl of washed protein G agarose beads and rocked overnight in a cold room ( 4°C ) . After IP , lysates/antibody or serum/IP solutions/beads were centrifuged and beads were washed 6× times with wash buffer . 30 µL of loading buffer were added to the beads and heated to release protein binding to the beads . The lysates from FH::SOLO and y w flies that were used in Western blot to test antibody specificity and the released solutions ( from IP experiment ) were run in 8% SDS-PAGE Acr/Bis electrophoresis . FH::SOLO was detected by using anti-FLAG M2 antibody ( 1∶1000 , Sigma ) and goat anti-mouse HRP-conjugated ( 1∶1000 , Chemicon ) with Supersignal West Pico ( Pierce ) . SMC1 was detected by using anti-SMC1 ( 1∶200 , rabbit ) and goat anti-rabbit HRP-conjugated ( 1∶2000 , JacksonImmuno ) with Supersignal West Pico ( Pierce ) . Newly eclosed females were fattened 1–3 days in vials with yeast paste and males and then ovaries were dissected in 1X PBS ( pH 7 . 4 ) . Immunostaining of whole-mount ovarioles was performed according to Page and Hawley [34] . After immunostaining , ovaries were separated into individual ovarioles and transferred to slides and mounted with Prolong Antifade reagent ( Invitrogen ) . UASp-Venus::SOLO expression was induced by nos-GAL4::VP16 and fluorescent signals were detected in the FITC channel or detected by anti-GFP antibody . Egg chambers were staged according to Matthies et al . [84] . Chromosome spreads were performed according to Webber et al . [30] . For WT germaria , pro-oocytes and oocytes in pachytene were identified by full-length C ( 3 ) G nuclear staining and enriched cytoplasmic ORB staining . For solo germaria , pro-oocytes and oocytes were identified by enriched cytoplasmic ORB staining , except in Figure S6 where C ( 3 ) G staining was used . In that figure , the “no-staining” category was not scored . For solo pro-oocytes in region 2a with abnormal C ( 3 ) G staining , the ORB-enrichment criterion ensured that zygotene cysts were not inadvertently included in the scoring . Even without ORB staining , however , zygotene nuclei could usually be distinguished from the defective pachytene nuclei by C ( 3 ) G staining . The C ( 3 ) G foci are usually smaller and more uniform in size in zygotene than the “spotty” staining in pachytene , and lengthy linear fragments are never seen in zygotene . Staging ( regions 2a , 2b , and 3 ) was based on position of cysts in the germarium ( see Figure 1 ) and/or shape of cysts ( rounded in region 2a , flattened in region 2b ) . Oocytes in egg chambers were identified by ORB enrichment , C ( 3 ) G staining , nuclear size ( smaller than polyploid nurse cell nuclei ) and/or position in cyst ( posterior ) . For scoring of γ-H2Av foci , pro-oocytes and oocytes were identified by enriched ORB staining . Pro-nurse cell nuclei were not scored . Linear C ( 3 ) G staining was used to identify pachytene pro-oocytes and oocytes from the 2a region of WT and solo germaria . Nuclear boundaries were established based on margins of DAPI and C ( 3 ) G staining . Nuclei with overlapping DAPI or C ( 3 ) G staining were not used for scoring . Only non-overlapping CID spots were scored as separate spots . Size and brightness of CID spots was not considered . Primary antibodies used : 1∶500 anti-C ( 3 ) G ( mouse monoclonal and guinea pig polyclonal antibody ( provided by R . S . Hawley ) , 1∶500 rabbit anti-GFP polyclonal antibody ( Invitrogen ) , 1∶800 rabbit anti-CID polyclonal antibody ( Active Motif ) , 1∶200 anti-SMC1 rabbit polyclonal antibody [53] , [54] , 1∶5000 rabbit anti-γ-H2Av antibody ( Rockland ) , 1∶3000 anti-VASA antibody ( P . Lasko ) , 1∶150 anti-ORB ( 6H4 and 4H8 , monoclonal , Developmental Studies Hybridoma Bank ( DSHB ) ) . Secondary antibodies ( IgGs ) used: Alexa Fluor 488 donkey anti-rabbit , Alexa Fluor 488 goat anti-guinea pig , Alexa Fluor 555 donkey anti-mouse , Alexa Fluor 555 donkey anti-rabbit , Alexa Fluor 647 donkey anti-mouse ( Invitrogen ) . All images were collected using an Axioplan ( ZEISS ) microscope equipped with an HBO 100-W mercury lamp and high-resolution CCD camera ( Roper ) . Image data were collected and merged using MetaMorph Software ( Universal Imaging Corporation ) . Adobe Photoshop CS2 and Illustrator CS2 were used to process images . Each image in the immunofluorescence figures came from a sum projection of 3D deconvolved z-series stacks . All images from WT and mutants were exposed for equal periods and deconvolved and processed identically . | Sexual reproduction entails an intricate 2-step division called meiosis in which homologous chromosomes and sister chromatids are sequentially segregated to yield gametes ( eggs and sperm ) with exactly one copy of each chromosome . The Drosophila meiosis protein SOLO is essential for cohesion between sister chromatids . SOLO localizes to centromeres throughout meiosis where it collaborates with the conserved cohesin complex to enable sister centromeres to orient properly – to the same pole during the first division and to opposite poles during the second division . In solo mutants , sister chromatids become disconnected early in meiosis and segregate randomly through both meiotic divisions generating gametes with random ( and mostly wrong ) numbers of chromosomes . In this study we show that SOLO also localizes to chromosome arms where it is required to construct stable synaptonemal complexes that connect homologs while they recombine . In addition , SOLO is required to prevent crossovers between sister chromatids , as only homolog crossovers are useful for forming the interhomolog connections ( chiasmata ) needed for homolog segregation . SOLO collaborates with cohesin for these tasks as well . We propose that SOLO is a subunit of a specialized meiotic cohesin complex and a multi-purpose cohesion protein that regulates several meiotic processes needed for proper chromosome segregation . | [
"Abstract",
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] | [
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] | 2013 | The Cohesion Protein SOLO Associates with SMC1 and Is Required for Synapsis, Recombination, Homolog Bias and Cohesion and Pairing of Centromeres in Drosophila Meiosis |
Recurrent urinary tract infections ( UTIs ) caused by uropathogenic E . coli ( UPEC ) are common and morbid infections with limited therapeutic options . Previous studies have demonstrated that persistent intracellular infection of bladder epithelial cells ( BEC ) by UPEC contributes to recurrent UTI in mouse models of infection . However , the mechanisms employed by UPEC to survive within BEC are incompletely understood . In this study we aimed to understand the role of host vesicular trafficking proteins in the intracellular survival of UPEC . Using a cell culture model of intracellular UPEC infection , we found that the small GTPase Rab35 facilitates UPEC survival in UPEC-containing vacuoles ( UCV ) within BEC . Rab35 plays a role in endosomal recycling of transferrin receptor ( TfR ) , the key protein responsible for transferrin–mediated cellular iron uptake . UPEC enhance the expression of both Rab35 and TfR and recruit these proteins to the UCV , thereby supplying UPEC with the essential nutrient iron . Accordingly , Rab35 or TfR depleted cells showed significantly lower intracellular iron levels and reduced ability to support UPEC survival . In the absence of Rab35 , UPEC are preferentially trafficked to degradative lysosomes and killed . Furthermore , in an in vivo murine model of persistent intracellular infection , Rab35 also colocalizes with intracellular UPEC . We propose a model in which UPEC subverts two different vesicular trafficking pathways ( endosomal recycling and degradative lysosomal fusion ) by modulating Rab35 , thereby simultaneously enhancing iron acquisition and avoiding lysosomal degradation of the UCV within bladder epithelial cells . Our findings reveal a novel survival mechanism of intracellular UPEC and suggest a potential avenue for therapeutic intervention against recurrent UTI .
Urinary tract infections ( UTIs ) are one of the most common bacterial infections in humans , affecting at least 50% of women at some point in their lifetime . UTIs constitute significant morbidity and economic burden , accounting for more than 1 million hospitalizations and $2 . 4 billion in medical expenses in the USA alone annually [1 , 2] . Most ( >80% ) UTIs are caused by Escherichia coli , thus the term uropathogenic E . coli ( UPEC ) [3] . After an initial infection , 25% of patients suffer a recurrence within 6 months , with 68% of these UTIs apparently caused by the original strain , despite appropriate antibiotic therapy [4 , 5] . Mouse models of UTI have been used by many groups to elucidate mechanisms underlying UPEC pathogenesis [6–8] . Experimentally infected mice also suffer episodes of recurrent UTI subsequent to clearance of bacteriuria following antibiotic therapy [9] . These recurrent infections are due to UPEC that persist within urinary bladder epithelial cells . UPEC have been described to form several types of intracellular populations in vivo , such as intracellular bacterial communities ( IBCs ) and quiescent intracellular reservoirs ( QIRs ) , that contribute to phenotypic antibiotic resistance and evasion of the host immune response [10] . QIRs , in particular , cause persistent infections lasting for months in mice [11] , while IBCs and other intracellular bacteria have been detected in the urine of both adult and pediatric human UTI patients [12–15] . Therefore , understanding the molecular mechanisms underlying the establishment of the intracellular bacterial reservoirs is critical for the development of efficient therapeutic strategies to control recurrent UTIs . UPEC utilize type 1 pili , which carry the FimH adhesin at their distal tips , to specifically bind to mannosylated surface proteins ( including uroplakins [16] and α3 and β1 integrins [17] on the surface of bladder epithelial cells [18] ) . Upon binding , UPEC are internalized into cyclic AMP- modulated Rab27b/CD63+ vesicles [19] . Internalization requires components of the host cell cytoskeleton and activation of Rho GTPases , various kinases , and other signaling intermediates [17 , 20–24] . Most of the internalized bacteria are expelled out of urothelial cells by Rab27b-mediated exocytosis [25] . In vivo , however , some bacteria escape their enclosing vesicle , enter the cytosol , and subsequently multiply to form biofilm-like IBCs that contain 104−105 bacteria each [26 , 27] . As a host countermeasure , infected epithelial cells undergo an apoptotic-like cell death , detaching from the urothelium to be expelled in the urine with the internalized bacteria [28] . However , this exposes deeper layers of the urothelium to infection , which is thought to lead to QIR formation . In contrast to IBCs , QIRs are smaller structures ( 6–8 bacteria ) , enclosed in host membranes ( LAMP1 , ATG16L1 , and LC3- positive and CathepsinD negative ) , and found in immature epithelial cells [11 , 29] . However , the molecular profiles of these bacterial reservoirs and the interplay between UPEC and the host resulting in their formation are still in need of further characterization . For successful intracellular survival , UPEC must obtain sufficient nutrients from the host cell . In mammals , iron is tightly bound to plasma and cellular proteins such as hemoglobin , transferrin , lactoferrin , or ferritin , which limit its availability to microbial pathogens , a phenomenon termed nutritional immunity [30] . In order to acquire iron , UPEC , like other bacteria , synthesize several siderophores ( catecholates enterobactin and salmochelin , the hydroxamate aerobactin , and yersiniabactin , a mixed-type siderophore ) and their cognate receptors [31] . These iron acquisition systems are important for virulence in mouse models of UTI and are among the most highly upregulated genes in both mouse [32 , 33] and human infections [34 , 35] . They are therefore being actively pursued as vaccine candidates [36–39] . With only one exception , all of these studies have focused on the expression of iron acquisition systems by extracellular bacteria . Notably , in the sole intracellular study [32] , host cell transferrin receptor ( TfR ) levels increased within IBC-containing epithelial cells and epithelial cells near IBC-containing cells . Transferrin is the major iron carrier protein in the blood . Transferrin bound iron ( Fe3+ ) is internalized from the circulation by the ubiquitously expressed TfR in almost all cells [40] . TfR trafficking is regulated in host cells by Rab GTPases [41] . Rab proteins are a subfamily of the Ras superfamily of GTPases that regulate various aspects of intracellular membrane trafficking [42] . Many intracellular pathogens that reside within host cell-derived vacuoles selectively recruit Rab proteins to facilitate trafficking of nutrient-rich vesicles to their vacuolar niches . Several pathogens also use Rab recruitment to prevent fusion of their vacuoles with degradative lysosomal compartments [43] . The connection between access to iron , transferrin , and Rab proteins was first noted in Mycobacterium tuberculosis , which associates with Rab11 , a key regulator of the transferrin recycling pathway , to acquire iron [44] . Recent studies have identified another Rab protein , Rab35 , a component of the rapid endocytic recycling pathway , that plays an important role in the recycling of endocytosed transferrin receptors ( TfR ) to the cell surface [45 , 46] . Upon internalization , the iron loaded transferrin-TfR complex is delivered to the early endosomes , where the Fe3+ dissociates from the transferrin . The receptor and transferrin are then recycled to the cell surface by Rab35 positive recycling endosomes to initiate a new round of iron uptake [47 , 48] . Inside the endosome , Fe3+ is reduced to Fe2+ , which is subsequently released into the labile iron pool ( LIP ) in the cytosol , where it may be utilized or stored in complex with ferritin [49] . Similar to Rab11 recruitment by Mycobacteria , Rab35 has been shown to be recruited to the vacuoles of Anaplasma phagocytophilum [50] , although its functional relevance in the intracellular persistence of pathogens has not yet been investigated . We hypothesized that Rab35 might play a role in iron acquisition during intracellular infection by UPEC . We found that UPEC infecting cultured bladder epithelial cells do indeed recruit Rab35 to their enclosing vesicles , structures we term the UPEC containing vacuoles ( UCV ) . In a mouse model of persistent UPEC infection , UPEC within the uroepithelium also associates with Rab35 . We found that Rab35 recruitment leads to increased TfR association with the UCV , which supports UPEC survival through the provision of iron . Finally , Rab35 recruitment serves a second function for UPEC survival by avoidance of UCV fusion with degradative lysosomes . Therefore , Rab35 recruitment is a key feature of the UPEC strategy for exploiting host vesicular trafficking during intracellular infection .
To identify host cell molecules and pathways utilized by UPEC for intracellular survival within BEC , we first focused on membrane trafficking pathway proteins . Rab GTPases are critical regulators of mammalian membrane trafficking pathways and many pathogenic bacteria are known to exploit these proteins for their intracellular survival within the host [43] by recruiting ( or excluding ) specific Rab proteins to bacteria containing compartments . We hypothesized that UPEC might subvert Rab GTPases for intracellular survival , possibly by recruiting specific Rab proteins to intracellular UPEC-containing compartments . To examine this , we initially focused on a subset of 15 human Rab GTPase proteins [50] and assessed their localization patterns during the UPEC intracellular infection , using the well-established bladder epithelial cell line 5637 ( BEC5637 ) based infection model system [19] . BEC cells over-expressing GFP/EGFP-tagged Rab GTPases were infected with UPEC ( CI5 strain ) for various time intervals ( 4 , 24 and 48 h ) . Based on previously reported data [11] , we reasoned that intracellular bacterial levels at 4 h post-infection would represent the number of bacteria that had invaded the BEC or intracellular bacteria levels during the early stages of infection , while the number of intracellular bacteria at 24 and 48 h ( or ≥ 24 h post-infection ) post-infection would represent the surviving bacterial population ( or bacterial levels during late stages of infection ) . In comparison to other tested Rab proteins ( S1A Fig ) , Rab35 showed a rather striking degree of localization to UPEC-enriched vesicular structures , which we termed UPEC-containing vacuoles ( UCV ) . BEC5637 cells over expressing GFP-tagged Rab35 protein were infected with UPEC for 4 , 24 and 48 h . As shown in the graph ( Fig 1A ) , a major fraction of Rab35 was found to associate with UCV at all the tested time points , as observed by confocal microscopy . There was a notable increase in the percentage of Rab35 localizing to the UCV membrane from 4 h to 24 h; but thereafter this value remained fairly constant . Representative confocal images of infected ( 24 h post-infection ) and uninfected samples are also shown . Depending on the stage of UCV development , we observed three categories of associations of Rab35 with UCV/UPEC at the 24 h time point . ( 1 ) UCV with ~2–10 bacteria , showing a distinct rim-like demarcation of Rab35 enriched at the periphery of UCV membrane ( Fig 1A , marked as 1 in the merged image ) ; ( 2 ) UCV with ~1–2 bacteria , where Rab35 may not always outline a complete rim-like structure , but is nonetheless enriched at the periphery of UCV membrane ( Fig 1A , marked as 2 in the merged image ) ; ( 3 ) Single UPEC where an UCV is not very well developed , but the bacteria appear to be associated with cytoplasmic patches of Rab35 ( S1B Fig , % seen = 30 ± 8 ) . The first two categories were counted as the positive population ( % seen = 64 ± 8 ) for Rab35 association with the UCV . On the other hand , UPEC-infected BEC5637 cells transfected with GFP showed diffuse green fluorescence throughout the cell with no specific GFP signal association with UCV ( S1C Fig ) . Interestingly , comparison of infected and uninfected GFP-Rab35 expressing cells revealed that UPEC infection resulted in a distinct pattern of Rab35’s localization and membrane association within the infected cells ( Fig 1A ) . While the uninfected cells showed a rather uniform distribution of Rab35 in the cytoplasm , UPEC infection induced a notable recruitment leading to increased presence of Rab35 around the UCVs . Rab35 recruitment was specific for UPEC as a commensal K12 E . coli strain that expresses type 1 pili ( MG1655 ) was unable to recruit Rab35 ( S1D Fig ) . In addition , we also found that heat-killed UPEC was unable to recruit Rab35 ( S1E Fig ) . Rab35 is involved in the rapid endocytic recycling pathway and regulates membrane traffic via cyclical conversion between its active GTP-bound state and its inactive GDP-bound state [51] . We reasoned that if UPEC co-opts Rab35 to modulate a cellular process , it might preferentially associate with the GTP-bound , functionally active form of Rab35 . Indeed , we observed that UPEC preferentially associated with the constitutively active Rab35 Q67L mutant ( GTP locked ) as compared to dominant negative Rab35 S22N ( GDP bound ) ( 2 fold difference , p = 0 . 03 ) ( Fig 1B ) . This suggests that not only do UPEC recruit Rab35 to its vacuole , but it also possibly utilizes the transport pathways regulated by Rab35 for its survival . We also found an increase in the expression of Rab35 mRNA ( 2 . 2 fold at 24h , p = 0 . 008; Fig 1C ) as well as protein ( 3 fold at 24h , p = 0 . 005; Fig 1D ) with the progression of infection . We also examined whether Rab35 is associated with intracellular UPEC during in vivo infection using a well-established murine UTI infection model [52] . Mice were infected with a cystitis isolate of UPEC , and bladders were removed at 24 h and 2 week time points , fixed , sectioned , and stained for UPEC and Rab35 . We found that approximately 25% of the UPEC in bladder sections , found in small intracellular collections resembling QIRs , were associated with Rab35 ( n = 4 sections/mouse bladder , n = 3 mice per experiment ) . An example of this association is shown in Fig 1E ( top panels shows results from 24 h and bottom panel shows results from 2 weeks post-infection ) . As not all UPEC were colocalized with Rab35 , we could conclude that the Rab35 staining was not due to nonspecific binding of the Rab35 antibody to UPEC ( S1F Fig ) . Previous in vivo studies have shown that UPEC resides in a Lysosomal-associated membrane protein 1 ( LAMP1 , a late endosomal/early lysosomal marker ) positive and Cathepsin D ( late lysosomal marker ) negative compartment in bladder cells during persistent infection ( QIRs ) [11] . We also observed that the Rab35-positive QIRs were also positive for LAMP1 ( S2A Fig ) . Interestingly , similarly to ATG16L1 and LC3 , which associate with both QIRs at 2 weeks post-infection and with IBCs at 6 h post-infection [29] , we found that Rab35 also colocalized with immature IBCs at 6 h post-infection ( S2B Fig ) . Since we observed localization of Rab35 to UCV in vitro and to IBCs and QIRs in vivo , we next asked whether Rab35 played a functional role during UPEC infection of bladder epithelial cells . Using a short interfering RNA ( siRNA ) mediated gene-silencing approach , we knocked down the expression of Rab35 in BEC5637 cells and then infected them with UPEC . The ability of these cells to support UPEC infection was assessed at various time points by cfu enumeration . Gene knock down was confirmed by Western blot analysis ( Fig 2A , inset ) . Rab35 silencing did not significantly affect the invasion/entry ( 4 h ) of UPEC into bladder cells ( Fig 2A ) . Remarkably , we found that silencing of Rab35 led to a significant reduction in the intracellular bacterial load of BEC5637 cells at 24 and 48 h post infection ( 3 . 3 fold at 24 h , p = 0 . 01 and 2 . 5 fold at 48 h , p = 0 . 04 ) ( Fig 2A ) . Infected BECs have the capacity to expel UPEC [19] . We also measured the levels of UPEC that are expelled from Rab35 silenced cells at 4 h and at 24 h post-infection . There was no change in the % of UPEC expelled from Rab35 silenced cells compared to control at 4 h post-infection ( S2C Fig ) . At 24 h post-infection , there were reduced levels of UPEC expulsion from Rab35 silenced cells ( Fig 2B ) . This experiment ruled out the possibility that the observed reduction in the intracellular UPEC load in Rab35 silenced cells is due to enhanced expulsion of bacteria; instead , the reduction appears to be due to reduced survival of intracellular UPEC . Consistent with the knockdown data , ectopic overexpression of Rab35 protein ( GFP-Rab35 ) led to enhanced levels of UPEC ( 2 . 3-fold , p = 0 . 03 ) in bladder epithelial cells , as assessed at 48 h , although invasion of UPEC into BEC cells was not affected ( Fig 2C ) . Expression levels of Rab35 were assessed by Western blot analysis ( Fig 2C inset ) . These results demonstrate that Rab35 plays a role in the intracellular survival of UPEC in bladder epithelial cells . Because UPEC intracellular survival was compromised within Rab35 knocked down cells , we examined the fate of internalized bacteria in Rab35-deficient cells . Previous reports [11] and our current data show that UPEC in QIRs reside in a LAMP1 positive compartment in bladder cells in vivo . We also observed that UCVs in vitro were positive for both LAMP1 and Rab35 . The percentage of Rab35 and LAMP1 positive UCV increased from 4–24 h , but thereafter this value remained fairly constant ( Fig 3A ) . The Mander’s overlap coefficient of Rab35 and LAMP1 on UCV was 0 . 65 ± 0 . 09 ( n ≥ 50 ) at 24 h . Since UPEC was apparently unable to sustain its intracellular growth in the absence of Rab35 , we hypothesized that the bacteria might be directed to the lysosomal compartment for degradation . To test this , we first studied the colocalization of UPEC with lysosomes in normal and Rab35-depleted cells , using lyso-Tracker Red , which stains late endosomes and lysosomes [53] . Very few UPEC colocalized with lyso-Tracker Red stained compartments in Rab35 sufficient cells . However , we observed that in Rab35-deficient cells , a significantly higher number of intracellular UPEC colocalized with lyso-Tracker Red at 24 h post infection ( Fig 3B ) . Since LysoTracker Red cannot reliably distinguish endosomes and lysosomes , we performed further experiments to probe the exact nature of the compartment occupied by UPEC in the absence of Rab35 . The lysosomal protease cathepsin D is first synthesized as an inactive precursor ( procathepsin D ) that is then cleaved to form active cathepsin D in lysosomes [54 , 55] . Hence , colocalization of UPEC with cathepsin D , and the processing status of cathepsin D , could be used as further indicators of UCV’s fusion with lysosomes . Subsequent colocalization experiments revealed a significant increase in the colocalization of UPEC with cathepsin D in Rab35-silenced cells , compared to controls ( 2 . 6 fold , p = 0 . 001; Fig 3C ) . In addition , we also found that the levels of mature/activated form of cathepsin D increased in Rab35 knockdown cells with the progression of infection ( Fig 3D ) . These results indicate that UPEC are transported to the degradative lysosomal compartment in the absence of Rab35 . We next sought to determine the mechanism by which Rab35 is utilized by UPEC to avoid fusion of UCV with lysosomes , thus promoting its intracellular survival within BEC . Rab35 is involved in the fast recycling of TfR , the protein critical for the cellular uptake of iron , and UPEC is known to up-regulate TfR1 levels during intracellular infection of BEC [32] . Because of its involvement in TfR recycling , we hypothesized that Rab35 might be recruited by UPEC in order to acquire iron ( a highly limiting nutrient ) in addition to its role in avoidance of fusion with lysosomes . We performed several experiments to address this possibility . First , we re-visited the requirement of iron for the multiplication of UPEC in cell-free cultures [56] . As seen in S3A Fig , addition of iron significantly increased UPEC levels in cell-free cultures , while the addition of the iron chelating compound deferoxamine reversed this effect . Although previous studies have reported the induction of iron acquisition-associated genes in UPEC during IBC formation [32 , 57] , the role of iron in the intracellular survival of UPEC has not yet been studied in detail . We therefore determined whether iron is critical for the intracellular survival of UPEC . We found that supplementation of iron in the form of holotransferrin indeed significantly increased the intracellular levels of UPEC in BEC cells at 24 h ( 2 . 4 fold , p = 0 . 006 , Fig 4A ) . Conversely , treatment with deferoxamine reduced the intracellular survival of UPEC ( 2 . 4 fold , p = 0 . 004 , Fig 4A ) . Accordingly , we also observed increased fusion of UCV with lysosomes in deferoxamine treated cells ( S3B Fig ) . To determine whether a Rab35-mediated pathway is exploited by UPEC to meet their iron requirements through TfR , we first analyzed the role of Rab35 and TfR1 in modulating the levels of metabolically available iron or the labile iron pool ( LIP ) in BEC , within the context of UPEC infection . Knock down of either Rab35 or TfR1 did not significantly alter LIP in uninfected cells at both 4 h ( S4A Fig ) and 24 h ( Fig 4B ) . Notably , UPEC infection led to a severe reduction of the LIP in negative control siRNA treated BEC cells ( 3 . 26 fold , p = 0 . 001 ) at 24 h ( Fig 4B ) , but not 4 h ( S4A Fig ) , post infection . This data argues that UPEC utilize the bulk of the pre-existing LIP of BEC during infection . Remarkably , this reduction of LIP was further aggravated in both Rab35 ( 2 . 8 fold reduction in comparison to infected negative control siRNA treated samples , p = 0 . 0001 ) and TfR1 knocked down cells ( 11 . 4 fold reduction in comparison to infected negative control siRNA treated samples , p = 0 . 0001 ) at 24 h post UPEC infection ( Fig 4B ) . Interestingly , silencing of TfR1 , but not Rab35 , also reduced the LIP at 4 h post-infection ( S4A Fig ) , although UPEC load was unaffected at this time point ( S4B Fig ) . Since the reduction of LIP upon UPEC infection was much greater in Rab35 and TfR1 silenced cells at 24 h than in infected control cells , we reasoned that UPEC is dependent on Rab35 and TfR1 to actively acquire iron from the extracellular milieu to replenish intracellular iron levels for its survival at later stages ( 24 h ) of infection . Further highlighting the association between Rab35 and TfR1 during UPEC infection , we found that similar to Rab35 knock down cells , the reduced iron pool in TfR1 knockdown cells ( Fig 4B ) was also associated with reduced survival of UPEC at 24 and 48 h post infection ( 3 . 4 fold at 24 h , p = 0 . 02 , Fig 4C ) . These experiments demonstrate that UPEC exploits Rab35 and TfR1 to acquire iron during later stages of intracellular infection ( ≥ 24 h ) in bladder cells . To ensure the specificity of the siRNAs used , we also performed siRNA phenotype rescue experiments . Rab35 or TfR was knocked down using siRNAs targeting their 3’UTR regions , followed by ectopic overexpression of Rab35/TfR respective coding regions , and followed by UPEC infection . It was found that ectopic overexpression of non-degradable Rab35 ( Fig 4D ) or TfR ( Fig 4E ) rescued the growth defect observed in Rab35/TfR-deficient cells . In order to determine the specificity of the requirement of Rab35 in meeting the iron requirement of persistent UPEC infection , we examined whether Rab14 , which is also known to be involved in TfR recycling [58] is utilized by UPEC for intracellular survival . Rab14 was knocked down in BEC cells and the intracellular UPEC levels were determined at various time points ( 4 , 24 and 48 h ) . As seen in S5 Fig , we found that Rab14 was not essential for the UPEC survival within BEC . Since both Rab35 and TfR1 are critical for UPEC intracellular survival , we hypothesized that UPEC recruits Rab35 to meet its iron nutrient requirements through the regulation of TfR transport towards bacteria containing vacuoles . To test this , we first examined whether TfR1 and Rab35 protein colocalized with UCV during infection . Confocal microscopy analysis showed that TfR1 colocalized with Rab35 at the cell surface during UPEC infection ( S4C Fig ) . More interestingly , TfR1 shows very obvious colocalization with UPEC-containing Rab35 positive UCVs ( Fig 4F ) with Mander’s overlap coefficient of 0 . 85 ± 0 . 12 ( n ≥ 50 ) . To further characterize the nature of UCV , we performed colocalization studies with another marker of early endosomes ( early endosomal antigen , EEA1 ) and found no obvious colocalization of UCV with EEA1 ( S4D Fig ) . A recent study has reported that UPEC in QIRs in vivo survive within an autophagosome-like compartment [29] . Consistent with this , we found that Rab35-positive UCV in vitro were also associated with the autophagy marker LC3 ( S6 Fig ) . The Mander’s overlap coefficient of Rab35 and LC3 in the infected cells was determined to be 0 . 60 ± 0 . 10 ( n ≥ 50 ) . We next examined whether UPEC infection affects TfR recycling in the presence and absence of Rab35 expression . It was found that UPEC infection per se did not affect TfR recycling ( as determined by measuring transferrin release ) in Rab35 sufficient ( siNT ) cells compared to uninfected cells ( Fig 4G ) . We then examined whether absence of Rab35 expression affects TfR recycling in the context of UPEC infection . Consistent with previous reports , we found that Rab35 silencing significantly reduced TfR recycling in uninfected BEC cells ( 2 . 5 fold at 30’ , p = 0 . 002 ) , compared to uninfected control siRNA treated cells ( Fig 4G ) . Similarly , the recycling of TfR to the cell surface was significantly reduced ( 2 . 53 fold at 30' , p = 0 . 002 ) in UPEC infected Rab35 knockdown BEC cells ( Fig 4G ) . We also quantified the surface expression of TfR in similar conditions , using immuno-staining based fluorescence microscopy . Rab35 knockdown resulted in a 1 . 6-fold ( p = 0 . 0001 ) and 1 . 9-fold ( p = 0 . 0001 ) reduction in the levels of cell surface TfR in uninfected and infected cells , respectively , compared to control siRNA treated cells ( Fig 4H ) . Although there was no significant difference in the surface expression of TfR during infection in the siNT group , there was a moderate but significant reduction ( 1 . 5-fold , p = 0 . 0007 , Fig 4H ) in the TfR surface expression in infected cells compared to uninfected cells in the Rab35 knockdown group , indicating a requirement for Rab35 in maintaining the TfR surface levels during UPEC infection . Additionally , consistent with the reduction of the surface TfR receptor levels and their reduced ability to accumulate a labile iron pool within persistently infected cells , Rab35 knock-down cells also showed a significantly reduced uptake of Alexa Fluor labeled Transferrin at 24 h post infection ( 2 . 2-fold , p = 0 . 0001 , Fig 4I and 4J ) . There was no reduction in the uptake of Alexa labeled Transferrin upon infection of wild type cells , in comparison with uninfected cells . Similarly , Rab35 knockdown alone did not reduce Transferrin uptake in uninfected cells . In order to confirm that the reduced uptake of transferrin was not due to a general defect in endocytosis due to infection , we analyzed the uptake of a lipid ( MFI Bodipy ) in Rab35 silenced cells . As shown in Fig 4K , UPEC infected Rab35 knockdown cells were not defective in bulk endocytosis . The above-described observations demonstrate that Rab35 is critical for the maintenance of the cellular surface levels of TfR and the subsequent iron-transferrin uptake by BEC cells during UPEC infection . The data provided earlier showed that iron supplementation by the addition holotransferrin enhanced the intracellular UPEC levels within normal cells . We next investigated whether iron supplementation can rescue the growth defect observed in Rab35/TfR-silenced cells . Holotransferrin was added to the culture media of control cells , TfR- or Rab35-knockdown cells and UPEC infected cells . We found that iron supplementation was unable to rescue the UPEC growth defect in both TfR and Rab35-knock down cells . As shown in Fig 5A , UPEC infected control ( si-NT ) cells showed significantly higher bacterial load in the presence of extracellular iron ( holotransferrin ) treatment ( 2 . 4 fold , p = 0 . 006 ) . However the TfR/Rab35-silenced cells did not show any increase in the bacterial load even with iron supplementation , most likely due to the failure of these cells to accumulate iron from the extracellular milieu . These results collectively suggest that iron is important for UPEC survival inside bladder cells , and the Rab35-TfR pathway plays a major role in meeting the iron requirements of intracellular UPEC . Earlier studies have shown that the expression levels of TfR are negatively regulated by intracellular iron content [59] . Since UPEC-infected cells had reduced intracellular iron levels ( LIP ) at 24 h post-infection ( Fig 4B ) , we hypothesized that TfR levels might be elevated in these cells . Consistent with our hypothesis , we observed increased expression of TfR1 upon UPEC infection , with a maximal induction observed at 24 h ( Fig 5B ) . This suggests that during infection , the observed consumption and depletion of cellular labile iron pools by UPEC will in turn trigger increased expression levels of TfR . This upregulation of TfR expression might facilitate increased internalization of Fe-Transferrin complexes into the cell . Because there is a constant struggle between the host and bacteria to secure iron for their respective uses , we wondered how the genetic regulatory system in UPEC would respond to the significant reduction in availability of cellular iron caused by Rab35 depletion . In response to iron limitation in the urinary tract , UPEC produce various siderophores ( eg , EntE ) and siderophore receptors ( eg . FepA , IroN ) as well as components of energy transduction systems required for siderophore uptake ( eg . TonB , ExbB ) . In addition , UPEC can also utilize ferrous iron via the iron transporter FeoA and host heme stores ( via ChuA , the receptor for hemin ) [32 , 33] . We first determined the levels of several genes known to be involved in iron acquisition in UPEC ( feoA , tonB , entE , fepA , exbB , sitA , chuA , iroN , and iroB ) at 24 h post-infection in Rab35-sufficient BEC cells by qRT-PCR analysis . We found that expression of two of the nine genes tested were significantly up-regulated in UPEC at 24 h post-infection in comparison to lab grown bacteria: sitA , encoding an ABC type iron transporter subunit; and iroB , encoding a putative glycosyltransferase involved in salmochelin glucosylation ( Fig 6A ) . In Rab35-silenced cells , five of the UPEC genes tested were up-regulated at 24 h post-infection: exbB , entE , fepA , chuA , and feoA ( Fig 6B ) . Intriguingly , the expression of the two genes sitA and iroB that were up-regulated in infected control cells were found to be either down-regulated ( sitA ) or did not change significantly ( iroB ) in Rab35 silenced cells at 24 h post-infection . Thus , intracellular UPEC display a specific response to iron limitation resulting from the abrogation of Rab35 expression , further supporting a role for Rab35 in facilitating iron acquisition during intracellular UPEC infection .
Numerous studies have described the complexities and consequences of intracellular UPEC infections in mice , humans , and cell culture . In general , studies on intracellular UPEC infections resembling those found in vivo have been limited partly because in vitro models do not seem to recreate the same structures . Several features of in vitro infections of cultured 5637 cells , however , have been validated using mouse models of infection , particularly for early stages of invasion [60 , 61] . We have used this in vitro system to identify a role for host Rab35 in intracellular UPEC survival . At late time points ( > 2 weeks ) during experimental mouse infections , UPEC is reported to reside within LAMP1-positive and Cathepsin D-negative compartments ( resembling late endosomes ) in vivo [11 , 62] . How UPEC survive within these acidic environments and how the phagosome is modified or phagosome maturation is arrested has yet to be fully determined . In our in vitro cell culture infection system , we also found that UCVs are LAMP1 positive . The UCVs are also positive for Rab35 and TfR; therefore , the UCV compartment shows some characteristics of late endosome and early/recycling endosomes . Accordingly , we found that in experimentally infected mice , UPEC within the uroepithelium also colocalize with Rab35 and the QIR marker LAMP1 . The observation that UCV’s were also associated with the autophagy marker LC3 also indicate that UCV may also possess some characteristics of autophagosomes , although more studies are required to characterize this further . We therefore add Rab35 to the list of QIR markers in vivo ( LAMP1-positive , LC3-positive , ATG16L1-positive , and Cathepsin D-negative ) and for the first time demonstrate that UCVs found during in vitro infection of 5637 cells carry similar vesicular markers , suggesting that intracellular infection of 5637 cells may be useful as a model for QIR development and therefore recurrent UTI . Survival of UPEC within UCVs carrying these unique vesicular markers could be one of the mechanisms by which the bacteria avoid endosomal maturation/lysosomal fusion . It is also worth noting that Rab35 deficiency resulted in reduced survival of UPEC within bladder cells in culture and this is supported by the observation of an increased percentage of UPEC-containing Cathepsin D-positive compartments ( late lysosomal/phagolysosomal ) upon Rab35 silencing . The percentage of UCV positive for Rab35 continuously increased throughout the course of infection further highlighting the requirement of this pathway for UPEC persistence within bladder cells . A model for how Rab35 promotes UPEC survival within BEC is given in Fig 7 . The compartment occupied by UPEC resembles that of A . phagocytophilum where the bacterium has been shown to preferentially recruit Rab GTPases ( including Rab35 ) associated with the endocytic recycling pathway to its vacuole in order to facilitate intracellular survival [50] . For the intracellular bacteria M . avium [44] and M . tuberculosis [63] , bacteria-containing phagosomes are also known to fuse with recycling/early endosomes to acquire iron and to halt the phagosome maturation process . The recruitment of host Rab35 to the UCV observed during UPEC infection hints at the possibility that it is a bacteria-induced phenomenon . Future studies will focus on the exact mechanism by which UCV hijacks Rab35 to its vacuole and the bacterial proteins ( if any ) that are involved in mediating this process . As a part of host defense mechanisms against bacterial infection , mucosal surfaces like the urinary bladder mucosa often limit the availability of essential nutrients , such as iron . To counteract this , bacteria including UPEC have developed or acquired apparently redundant systems to acquire free or host protein-bound iron , including siderophores , outer membrane iron compound receptors and TonB-dependent receptors . At least one of the two major siderophores ( enterobactin or aerobactin ) were required for the efficient host colonization of UPEC [38] . Our study provides a preliminary understanding of the expression status of various UPEC genes associated with iron acquisition during intracellular survival of UPEC within urinary bladder cells . It is interesting to note that only two bacterial genes , sitA and iroB ( out of 9 tested ) , were significantly up-regulated in intracellular UPEC infection in comparison to the extracellular form . Intriguingly , UPEC in Rab35 silenced cells showed a significant down-regulation in the expression of sitA , while the expression of iroB remained unchanged . The significance of this observation is currently unclear; although functional redundancy of the iron acquisition systems in UPEC [64] could be one of the contributing factors . In contrast , the Rab35 silenced cells showed an increase in the mRNA levels of genes such as tonB , fepA ( a TonB-dependent active transporter that recognizes extracellular ferric enterobactin and translocates it into the periplasm ) , entE ( enterobactin synthetase ) , chuA ( heme-derived ferric iron receptor ) , feoA ( ferrous iron transport protein ) , and exbB ( component of TonB complex ) . Increases in the mRNA levels of these genes were also previously reported in a comparison of UPEC in IBCs with UPEC in the distal intestine , further supporting their role in iron acquisition by intracellular UPEC [32] . Until now , only a few reports have examined the requirement of bacterial iron acquisition systems in the extracellular and IBC forms of UPEC [32–35] , and there has been only one study to report the up-regulation of host genes associated with iron regulation ( TfR and Lcn2 ) in the urothelial cells associated with IBCs [32] . Our study demonstrates for the first time that UPEC adopts a unique strategy by exploiting the fast endocytic recycling pathway component Rab35 to sequester TfR , in turn will increasing the availability of iron for the intracellular UPEC . In the absence of Rab35 expression , the TfR will not be efficiently recycled to the cell surface , leading to insufficient replenishment of intracellular iron , ultimately hampering the growth of UPEC ( by the fusion of UCV with lysosomal compartments ) . The iron supplementation experiments confirmed the notion that iron is critical for the survival of both extracellular and intracellular UPEC . The intracellular labile iron pool within bladder epithelial cells decreased to ~38% within the first 24 h following UPEC infection , suggesting that UPEC derives majority of its iron from the labile iron pool of the host cell . Two major sources for the labile iron pool are ( i ) transferrin-bound iron , internalized via transferrin receptors on the cell surface and ( ii ) ferritin , the intracellular iron storage protein . Silencing of TfR almost completely eliminated the labile iron pool at 24 h , suggesting that this is the major pathway by which the iron pool is maintained within the UPEC-infected cell . In further support of this , silencing of Rab35 also reduced intracellular iron . The fact that Rab35/TfR-silenced cells were unable to take up extracellular iron and support UPEC growth also illustrates the requirement of Rab35 in transferrin receptor-mediated iron uptake during UPEC infection . TfR level is controlled by levels of intracellular iron . When the cellular iron pool drops , iron-regulatory proteins IRP1 and IRP2 are activated , and they bind iron-responsive elements ( IRE ) to upregulate TfR by a post-transcriptional mRNA stability mechanism [65] . We have indeed observed an increased TfR protein expression during intracellular UPEC infection . Since our studies have identified that intracellular UPEC utilize iron for survival within BEC , a major question that remains to be resolved is the source of iron utilized by the Rab35/TfR positive intravacuolar bacteria . Iron taken up by cells through the TfR-Tf complex first reaches endosomes from where it is exported into cytoplasm , leading to the formation of the cellular labile iron pool . Thus , intravacuolar pathogens can acquire iron from two possible sources: ( a ) extracellular ( transferrin-bound iron ) that is used “directly” within the vacuole; or ( b ) intracellular ( the cytoplasmic labile iron pool ) . Future studies will determine whether Rab35/TfR+ve UCV utilizes vacuolar iron or the cytoplasmic labile iron pool . Given the molecular similarities with QIRs , UCVs may also contain UPEC that are not actively replicating . If the acquired iron is not utilized for growth , an interesting question that remains to be resolved is the purpose of acquisition of iron by UPEC at this stage . UPEC at this stage could be comparable to intravacuolar persistent stage of M . tuberculosis where the bacterium attains a seemingly non-replicating ( quiescent ) or slowly replicating state of growth [66] . Bacteria in a quiescent state display nominal metabolic capacity , maintain membrane potential , and do not undergo obvious morphological differentiation [67 , 68] . This state allows the bacteria to escape from host-stress responses such as low iron , low oxygen , and low pH , and to maintain a viable bacterial population during the stress period . When conditions are favorable , these bacteria will then resume growth and start multiplying actively . Our studies show that iron is one of the major limiting nutrients that UPEC exploits to survive within the vacuole of BEC . The iron sequestered by the UPEC may be used for maintaining the basal metabolism of the persistent bacteria . In support of this notion , our data shows that iron depletion reduces the survival of UPEC , while supplementation of iron triggers multiplication/ bacterial growth . On the other hand , it should be noted that too much iron is also detrimental to the bacteria , since iron levels and oxidative stress are directly linked [69] . Iron itself is a mediator of oxidative stress , as it chemically generates hydroxyl radicals that are capable of damaging DNA and proteins [70 , 71] . To prevent iron-dependent cytotoxicity , bacteria also store excess iron ( similar to the host ) with ferritin , bacterioferritin , and Dps proteins [72] to be utilized when conditions are favorable . Thus the bacterial iron storage proteins store iron as well as protect the bacteria from iron-mediated oxidative stress conditions . Supporting this , it was reported that ferritins , Dps , or bacterioferritins enhance the growth of iron-deprived pathogens such as E . coli [73] , Campylobacter jejuni [74] , and Mycobacterium tuberculosis [75] , protect them against oxidative stress and support their survival within the host [76] . UPEC in the UCVs could therefore be actively sequestering iron from the host to meet one or more of the following purposes: ( 1 ) maintaining its basal metabolism and survival during stressed conditions ( low iron , low oxygen , or low pH ) ; ( 2 ) sequestering excessive Fe as a stress resistance mechanism as well as for storage; and ( 3 ) for active multiplication when conditions are favorable . Further studies are necessary to distinguish among these possibilities . There is therefore a delicate balance between the host response ( by sequestering iron to prevent bacterial multiplication ) and the bacterial response ( by exploiting iron to a level at which it is not toxic to the bacteria ) . The identification and characterization of Rab35 as a critical regulator of UPEC intracellular survival may open up new avenues for the therapeutic intervention for the elimination of chronic or persistent UPEC infections .
All animal experiments were performed in accordance with protocols that were reviewed and approved by the A*STAR Biological Resource Center Institutional Animal Care and Use Committee ( protocol #130853 ) . These protocols were approved to be in accordance with the prevailing Singapore National Advisory Committee for Laboratory Animal Research ( NACLAR ) guidelines . The clinical UTI isolate E . coli CI5 strain [77 , 78] was kindly provided by Prof . Soman N Abraham ( Duke and Duke-NUS ) and was used throughout the in vitro study . The prototypic cystitis strain UTI89 [52] transformed with plasmid pANT4 expressing GFP [79] ( referred to as strain SLC-295 ) was used for in vivo infection experiments in mice . The K12/mCherry strain ( SLC-720 ) is E . coli K12 substr . MG1655 transformed with an mCherry expression vector ( pSLC-229 ) . pSLC-229 was generated by replacing the GFP gene in plasmid pANT4 [79] with mCherry amplified from pXJ40_2 ( a kind gift from Sohail Ahmed ) . Briefly , mCherry was amplified using PCR ( using the primers 5’-GACTCTGAATTCATGGTGAGCAAGGGCGAGGA and 5’-GATCCTGCATGCTTACTTGTACAGCTCGTCCATGC ) . The expected 700bp product was digested with EcoRI and SphI and cloned into the same sites of pANT4 to yield pSLC-229; the correct predicted sequence was verified by sequencing the ligation junctions and the entire mCherry gene . pSLC-229 was then transformed into MG1655 to yield SLC-720 . Human Bladder epithelial cell line 5637 ( ATCC HTB-9 ) was maintained at 37°C and 5% CO2 in RPMI 1640 media ( Gibco ) supplemented with 10% Fetal Calf Serum ( Gibco ) . All the Rab GTPase plasmids used in this study including the EGFP tagged wild type Rab35 , and mutants ( Rab35 Q67l and Rab35 S22N ) were as described before [50] . The other GFP/RFP tagged Rab GTPases used for the microscopy-based study were: Rab1 , Rab2A , Rab3A , Rab4A , Rab5 , Rab6A , Rab8A , Rab10 , Rab11A , Rab14 , Rab18 , Rab22A , Rab27A , and Rab33A . RFP-tagged LAMP1 and Cathepsin D plasmids were obtained from ( Addgene ) . Transferrin receptor plasmid was a kind gift from Dr . Kuni Matsumoto . Human Rab35 ( 9690 ) and Cathepsin D antibody ( 2284 ) were obtained from Cell Signaling Technology . Transferrin Receptor ( sc-32271 ) ) and LAMP1 ( sc-20011 ) antibodies was obtained from Santa Cruz . Anti GFP antibody ( ab540 ) was obtained from Abcam . Mouse Rab35 ( NB20042 ) antibody was purchased from Novus Biologicals . Rabbit polyclonal antibody to LC3 ( ab58610 ) was purchased from Abcam . Mouse LAMP1 antibody was obtained from Abcam ( ab25245 ) . BEC 5637 cell monolayers grown in 24-well tissue culture plates were infected with CI5 UPEC at a multiplicity of infection of 500 bacteria per host cell . For generation of heat-killed UPEC , the bacteria were suspended in PBS and heated at 100°C for 30 min . The non-viability of bacteria was confirmed by plating the bacteria on LB agar plates . To facilitate and synchronize bacterial contact with the host cells , plates were centrifuged at 600 × g for 5 min . After 2 h incubation at 37°C , cells were washed three times with RPMI to remove nonadherent bacteria . Monolayers were then incubated for 1 h with complete RPMI medium plus 100 μg/ml of gentamicin ( Gibco ) to kill extracellular bacteria . Subsequently cells were washed and further incubated in fresh medium containing gentamicin ( 10 μg/ml ) for the entire duration of the experiment . At designated times monolayers were washed with PBS and subsequently lysed in PBS plus 0 . 1% Triton X-100 , and bacteria present within the lysates were enumerated by plating serial dilutions on LB agar plates . To check for invasion , cells were washed with PBS directly after 1h of 100 μg/ml of gentamicin treatment and lysed in PBS-Triton–X 100 . Bacteria exocytosis assay was performed as described before [19] . Briefly , cells were infected as described above . After Gentamycin ( 100μg/ml ) treatment , cells were washed twice and left in fresh culture medium containing 100mM methyl-α-D-mannopyranoside ( to prevent reattachment and entry of bacteria into BEC ) . At indicated time points , the culture medium was collected and plated for CFU counts . The small interfering RNA targeting human Rab35 ( 5’-GCUCACGAAGAACAGUAAA-3’ ) [80] was purchased from Sigma . Two siRNAs targeting human Transferrin Receptor ( 5’-GCACAGCUCUCCUAUUGAA-3’ and 5’-GCUGAAAGCUUAAAUGCAA-3’ [81] were purchased from SABio . The TfR siRNAs were pooled and used for experiments . The siRNAs targeting the 3’UTR of Rab35 were ( 5’-GCGAGGGUGUGCUUGCAAAUU-3’ and 5’-GCAAAUUCAAGCAAUAAGAUU-3’ ) and siRNAs targeting the 3’UTR of TfR were ( 5’-CAGAAACCAGTTATGTGAATGATCT-3’ , 5’- GGTTCAACTGTTGATTGCAGGAATA-3’ , 5’-CAGACTCAGTTTGTCAGACTTTAAA-3’ , and 5’- TCGGAGACAGTGATCTCCATATGTT-3’ ) . The siRNAs were pooled and used for the experiments . The non-targeting siRNA ( si-NT ) from SABio was used as a negative control . Cells were transfected with siRNA using Lipofectamine 2000 ( Invitrogen ) according to manufacturer’s instructions and were used for further experiments 48 h post transfection . For transfection experiments , BEC-5637 cells were plated in a 24 well plate at a density of 1 x 105 cells/ well . Next day cells were transfected with 1 μg DNA using Polyethylenimine ( PEI ) ( 1:3 ratio DNA: PEI ) . Cells were washed 6 h after transfection and incubated in fresh culture medium for 20 h , before proceeding for further experiments . For siRNA rescue experiments , BEC cells were transfected with 3’ UTR siRNA against Rab35 or TfR using Lipofectamine 3000 ( Invitrogen ) according to manufacturer’s instructions . 24 h later cells were washed and transfected with 1μg control plasmid ( pEGFPC1 for Rab35 plasmid and pCMV-Flag for TfR plasmid ) or Rab35/TfR expression plasmids . 24 h later cells were washed and left in serum free media for 4 h . Cells were subsequently infected with UPEC ( MOI 500 ) under serum free conditions . The cells were finally left in 10 μg/ml Gentamycin—RPMI with 3% serum . 24 h later the intracellular bacterial load was determined as described earlier . Bacterial colocalization with various markers was performed using Confocal Microscopy . Briefly , cells were grown on coverslips and transfected with plasmids for various fluorescently tagged proteins as described above . After 20 h cells were washed and infected with UPEC . At different time points , cells were fixed with 4% paraformaldehyde and stained for nuclei with DAPI . The coverslips were mounted using Prolong Gold Antifade ( Molecular Probes ) and examined using Zeiss confocal Microscope with appropriate filter sets . For cell surface colocalization of Rab35 and Transferrin receptor , cells were transfected with GFP Rab35 expressing plasmid . After infection the cells were fixed and cell surface Transferrin Receptor was stained with transferrin receptor antibody . The samples were analyzed by confocal microscopy as mentioned above . For the colocalization of bacteria with lysosomal compartment , infected cells were treated with LysoTracker Red ( Life Technologies ) for 2 h at 37°C . Cells were then fixed and analyzed by confocal microscopy . All the images were acquired on LSM 710 Carl Zeiss microscope using Plan-Apochromat 63x/1 . 40 oil DIC objective . Images were acquired at 16 bit depth at a resolution of 1024x1024 pixels . Z sections of images were acquired at 63X magnification and were analyzed using Zen 2010 software and were processed using Adobe photoshop7 software . For images with XZ/ YZ projections , serial Z sections of the Z stack were taken at 0 . 5μm thickness . The selected UCVs , containing detectable Rab35 and membrane marker were used for pixel quantification and statistical analysis . Quantification of the colocalization of Rab35 with LAMP1 or TfR was done using Zen 2010 software ( Zeiss ) , which calculates the Mander’s overlap coefficient ( R ) [82] . The values for the overlap coefficient range from 0 to 1 . An Overlap Coefficient with a value of 1 indicates high colocalization and 0 represents low colocalization . The overlap coefficients were calculated using data from at least 3 independent experiments ( n ≥ 50 ) . Cells grown on coverslips were analyzed for transferrin uptake at 24 h post infection . Cells were washed with RPMI and were incubated in serum free media for 1h to remove any traces of Transferrin and then were exposed to 50 μg/ml tansferrin conjugated to Alexa Flour 548 ( invitrogen ) at 37°C for 20’ . Internalization was stopped by chilling the cells on ice . External transferrin was removed by washing with PBS , which was followed with washes with PBS ( pH 5 ) to remove bound transferrin . Cells were finally washed with PBS ( pH 7 ) and analyzed for transferrin uptake by confocal microscopy as described above . Fluorescence intensity was analyzed by Zen software . For Transferrin recycling cells were first allowed to uptake Transferrin as mentioned above . Following final wash with PBS ( pH 7 ) , cells were incubated in RPMI in presence of 50μg/ml holotransferrin . Cells were fixed with 4% paraformaldehyde after 5 , 15 and 30 minutes . Intracellular Transferrin was analyzed by confocal microscopy as described above . Transferrin receptor recycling/ Transferrin release was measured as ( Intracellular Transferrin at 0h - Intracellular Transferrin at that time point ) . At designated time points , BEC-5637 cells were lysed in RLT buffer ( Qiagen ) and RNA was isolated using RNeasy kit ( Qiagen ) . For isolating RNA from bacteria from infected BEC-5637 , cells were seeded and infected in 6 well plates in triplicates . At designated time points , cells were lysed by passage through 20G needle 6–7 times . The lysate was spun at 1000xg for 5 minutes to remove the cell debris . The supernatant was then spun at 10 , 000xg for 20 minutes to pellet down the bacteria . The bacterial pellet was subsequently lysed in RLT buffer and processed for RNA isolation as described above . cDNA synthesis was carried out by iScript cDNA synthesis kit ( Bio-Rad ) as per manufacturer’s instructions . Rab35 and the bacterial genes were amplified from cDNA by q PCR . Primer sequences for iron-related genes of UPEC were as described before [32] . Sequences for all other primers used are listed in the S1 Table . Reactions were performed in Roche light cycler 480 Real-time PCR system under following conditions—94°C/2 minutes for 1 cycle; 94°C/30 seconds , 55°C/30seconds , 72°C/60 seconds for 40 cycles; 72°C/7 minutes for 1 cycle . Results were analyzed using delta-delta-Ct algorithm . For protein expression analysis cells were lysed at 4°C for 15 minutes in RIPA buffer— ( 20 mM Tris-HCl ( pH 7 . 5 ) , 150 mM NaCl , 1 mM Na2EDTA , 1% NP-40 , 1% sodium deoxycholate , 2 . 5 mM sodium pyrophosphate ) containing protease and phosphatase inhibitor cocktail ( Roche ) . The supernatant of the lysate spun at 12000 g for 10’ was estimated for protein concentration using Lowry assay . Equal protein concentrations were run on 12% SDS gel and analyzed for protein expression by Western blotting . Labile iron pool was calculated by using standard Calcein-AM protocol [83] . Briefly cells were washed with PBS-BSA ( 1mg/ml ) -Hepes ( 20mM ) and then treated with 0 . 15uM Calcein ( life technologies ) in PBS-BSA-Hepes for 10’ at 37°C . Cells were washed thrice with PBS-BSA ( 1mg/ml ) -Hepes ( 20mM ) and resuspended in same buffer . Fluorescence was measured in iTecan Fluorimeter at Excitation- 488nm , Emission- 520nm for 10 minutes to establish stable baseline signal ( F1 ) . After this , 1000uM Dipyridyl ( Sigma ) in PBS-BSA ( 1mg/ml ) -Hepes ( 20mM ) was added to cells . Fluorescence was measured again until a stable base line signal was established ( F2 ) . Labile Iron Pool was calculated as F2-F1 . siNT , siRab35 and siTfR cells were infected with UPEC under serum free conditions . After gentamycin ( 100μg/ml ) treatment , cells were left in 10μg/ml gentamycin containing RPMI alone or along with Holotransferrin ( 50μg/ml ) or deferroxamine ( 100μM ) . The bacterial load was enumerated at 24 h post infection . UPEC cultures were diluted to OD600 of 0 . 01 in 3 ml RPMI medium with vehicle control , 10μM Ferric chloride ( Sigma ) or 200μM deferoxamine . Bacteria were grown at 37°C with shaking . OD600 was recorded at serial intervals . A GFP-expressing derivative of UTI89 ( SLC-295 ) was grown at 37°C in 10mL of LB media without shaking for 24h , followed by a 1:1000 dilution and a second 24h of growth at 37°C in LB without shaking . Bacteria were harvested by centrifugation and resuspended in PBS to an OD600 of 0 . 5 ( 1–2×107 CFU/50μL ) and used directly as the inoculum . 7–8 week old female C57BL/6 mice ( In Vivos , Singapore ) were transurethrally inoculated with 50μL of this inoculum or sterile PBS ( negative control ) . At 6h ( 5 mice ) , 24 h ( 5 mice ) or 2 weeks ( 5 mice ) post-infection , mice were sacrificed and their bladders aseptically harvested . Bladders were then hemisected and incubated in 10% formalin at 4°C overnight . The processing of the bladder samples was carried out at the A*STAR/IMCB histology facility ( A*STAR , Singapore ) . Briefly , the bladder tissues were embedded in 2% agar for paraffin processing . For IFA , 4- to 5μm serial sections were cut longitudinally , deparaffinized in xylene ( twice for 5 min at room temperature ) , rehydrated in 100% ethanol ( twice for 2 min at room temperature ) , 95% ethanol ( for 1minute ) , and 80% ethanol ( for 1 minute ) . Antigen retrieval was performed by boiling slides for 30 min in 1mM EDTA buffer , pH 8 . Slides were blocked in 1% FBS 0 . 4% TritonX-100 for 1 h at room temperature , and subsequently incubated overnight at 4°C with the following primary antibodies- goat anti GFP antibody ( Abcam 1:500 dilution in blocking buffer ) and rabbit anti Rab35 antibody ( Novus biological , 1in 50 dilution in blocking buffer ) . For LAMP1 triple staining , the sections were additionally stained with rat anti LAMP1 antibody ( abcam 1:200 blocking buffer ) . Sections were washed thrice with blocking buffer and were subsequently incubated with Alexa Fluor 488 ( anti-rabbit ) , Alexa 594 ( anti-goat ) and Alexa 350 ( anti-rat ) conjugated secondary antibodies ( 1:500; Molecular Probes ) . The sections were then stained with DAPI and were analyzed by confocal microscopy . P values were determined by unpaired two-tailed Student’s t test . P values less than 0 . 05 were considered statistically significant . RAB35 ( Species: Homo sapiens , Gene ID: 11021 ) Transferrin receptor ( Species: Homo sapiens , Gene ID: 7037 ) | Urinary tract infections ( UTIs ) are common and costly infectious diseases , affecting half of all women . Many women suffer from recurrent UTIs , for which no effective therapy currently exists . Intracellular persistence within bladder epithelial cells ( BEC ) by uropathogenic E . coli ( UPEC ) contributes to recurrent UTI in mouse models of infection . In the current study , we specifically asked whether and how UPEC co-opt any of the host proteins regulating vesicular trafficking for intracellular infection . Our study demonstrates a novel mechanism by which UPEC exploit a host endocytic recycling pathway protein ( Rab35 ) to acquire the critical nutrient iron and to prevent lysosomal degradation , thereby promoting intracellular survival within BEC . The results of this study may highlight new avenues for therapeutic intervention in recurrent UTI . In addition , knowledge gained from this study can also be extended to understand the general principles by which other intracellular bacterial pathogens acquire essential nutrients , leading to additional strategies to combat these infectious diseases . | [
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] | [] | 2015 | Intracellular Uropathogenic E. coli Exploits Host Rab35 for Iron Acquisition and Survival within Urinary Bladder Cells |
Visceral leishmaniasis ( VL ) can be lethal if untreated; however , the majority of human infections with the etiological agents are asymptomatic . Using Illumina Bead Chip microarray technology , we investigated the patterns of gene expression in blood of active VL patients , asymptomatic infected individuals , patients under remission of VL and controls . Computational analyses based on differential gene expression , gene set enrichment , weighted gene co-expression networks and cell deconvolution generated data demonstrating discriminative transcriptional signatures . VL patients exhibited transcriptional profiles associated with pathways and gene modules reflecting activation of T lymphocytes via MHC class I and type I interferon signaling , as well as an overall down regulation of pathways and gene modules related to myeloid cells , mainly due to differences in the relative proportions of monocytes and neutrophils . Patients under remission of VL presented heterogeneous transcriptional profiles associated with activation of T lymphocytes via MHC class I , type I interferon signaling and cell cycle and , importantly , transcriptional activity correlated with activation of Notch signaling pathway and gene modules that reflected increased proportions of B cells after treatment of disease . Asymptomatic and uninfected individuals presented similar gene expression profiles , nevertheless , asymptomatic individuals exhibited particularities which suggest an efficient regulation of lymphocyte activation and a strong association with a type I interferon response . Of note , we validated a set of target genes by RT-qPCR and demonstrate the robustness of expression data acquired by microarray analysis . In conclusion , this study profiles the immune response during distinct states of infection of humans with Leishmania infantum with a novel strategy that indicates the molecular pathways that contribute to the progression of the disease , while also providing insights into transcriptional activity that can drive protective mechanisms .
Infections with the protozoan parasites Leishmania donovani or L . infantum ( chagasi ) result in clinical outcomes that range from asymptomatic infection to active visceral leishmaniasis ( VL ) . When disease occurs , symptoms often include fever , hepatosplenomegaly , cachexia , pancytopenia and hypergammaglobulinemia [1] , while the lethality of VL correlates with severe symptoms such as secondary infections , hemorrhage , liver failure and cardiotoxicity due to treatment [2] . Depressed cellular immunity is considered a hallmark of VL , which is evidenced by the inability of VL patients to develop a positive delayed type hypersensitivity ( DTH ) in Montenegro skin tests in spite of infection [3] , and the absence of IFN-γ in cultures of peripheral blood mononuclear cells stimulated with leishmanial antigens [4] . On the other hand , whole blood assays showed that VL patients do not lack the ability to mount Leishmania specific IFN-γ responses [5] . Furthermore , peripheral blood or splenic CD4+ T lymphocytes from VL patients produce IFN-γ in response to leishmanial antigens , which is also crucial to limit parasite replication in splenic aspirate cultures [6] . These findings indicate that progression of VL involves other molecular mechanisms besides failures in activation and differentiation of CD4+ T lymphocytes . Development and severity of VL have been associated with several pro-inflammatory and immunoregulatory factors such as cytokines [7 , 8] , lipopolysaccharide [9] , mannan-binding lectin [10] , C reactive protein and patterns of IgG Fc N-glycosylation [8] . In addition , studies addressing features of infected asymptomatic individuals point towards a fine regulation of several immune compartments thought to control parasites without damage to the host [8 , 11 , 12] . Thus , particular clinical outcomes after infections with L . infantum are influenced by complex multi-factorial immunological processes . Re-circulation between central and peripheral lymphoid organs has a major impact on effective immune responses and infections and inflammation cause cell migration via lymphatic and circulatory systems [13] . During physiological or pathological events in which factors are released systemically , features of peripheral cell re-circulation provide an informative platform to study the human immune system with molecular methods of genomic scale , which have been used to investigate blood transcriptional and immunological profiles during human infections , including parasitic diseases [14–16] . Genome-wide profiling strategies have been employed to evaluate in vitro systems of infection with Leishmania and in vivo models of VL [17–20] , while studies in humans are limited to biopsies from patients with cutaneous leishmaniasis [21 , 22] . We hypothesized that a global overview of gene expression in the peripheral blood of humans presenting with distinct states of infection with L . infantum could reveal unappreciated immunological features that account for pathological or protective responses . To address this issue , we undertook a series of molecular approaches and functional analyses to uncover the transcriptional activity of the immune response that extend the understanding and provide new insights into the immunobiology of human VL .
This study was conducted as per protocols approved by the Research Ethics Committee of the Clinics Hospital of the Ribeirão Preto Medical School—USP ( protocol 2347/2012 ) . All the methods were carried out in accordance with approved guidelines . Informed written consent was obtained from all of the participants or their parents or legal guardians . Whole peripheral blood was collected from patients with symptoms of VL admitted to Natan Portella Institute of Tropical Diseases , UFPI , Teresina-PI , Brazil . Diagnosis was confirmed by identification of Leishmania amastigotes in Giemsa-stained smears of bone marrow aspirate , and patients diagnosed with VL received treatment according to Brazilian guidelines [23] . Additionally , whole peripheral blood was collected from a distinct group of VL patients at 2 to 5 months after the beginning of therapy with pentavalent antimonial , which were under remission of the disease ( Table 1 ) . Study subjects also included healthy individuals living in the same areas and considered to be asymptomatically infected with L . infantum , who were identified by a positive delayed type hypersensitivity ( DTH ) to leishmanial antigens ( Table 1 ) . Controls included individuals from different regions of Brazil ( Teresina-PI and Ribeirão Preto-SP ) who presented a negative DTH to leishmanial antigens ( Table 1 ) . The groups did not present significant differences with respect to age ( ANOVA P value = 0 . 370 ) or sex distribution ( Chi-square P value = 0 . 4181 ) . Whole peripheral blood samples were stabilised in PAXgene Blood RNA tubes ( PreAnalitiX , Hombrechtikon , Switzerland ) and stored at -80°C . Isolation and purification of total RNA was performed using the PAXgene Blood RNA Kit ( PreAnalytix ) according to the manufacturer’s instructions . RNA concentration was verified with NanoDrop 1000 spectrophotometer ( NanoDrop Technologies , Wilmington , DE , USA ) and the RNA integrity was determined using an Agilent 2100 Bioanalyzer ( Agilent Technologies , Foster City , CA , USA ) . The RNA samples were submitted to microarray hybridization at the Functional Genomics Unit of the Roy J . Carver Biotechnology Center , University of Illinois , Urbana-Champaign , Illinois , USA . All procedures were performed according to the manufacturer’s instructions . Briefly , cRNA amplification and labelling was carried out on 1 ug of total RNA by using an Illumina TotalPrep Amplification kit ( Ambion , Austin , TX , USA ) . The samples were then hybridized onto on Illumina HumanHT-12 v4 Expression BeadChips that were scanned with an Illumina iScan System ( Illumina , San Diego , CA , USA ) . Illumina´s Beadstudio software was used to generate signal intensity values from the scans . Raw data were processed using the R Language and Environment for Statistical Computing ( R ) 3 . 2 . 0 [24] in association with Bioconductor 3 . 1 [25] . The lumi package for R [26] was used to perform quality control , log2 transformation and normalization with robust spline normalization ( RSN ) method . This processing pipeline was based on the comparison and variation of transformation and normalization methods and optimized according to the number of samples , as well as the array technology [27] . Data was filtered to remove unexpressed genes based on detection call p-values computed for each probeset of the > 47 , 000 probes present on the Illumina HumanHT-12 v4 array and 17 , 015 probes were retained for further analysis . Probe-level expression data files were deposited at the Gene Expression Omnibus ( GEO ) repository under accession number GSE77528 . The patterns of differential gene expression between the study groups were evaluated by generating linear models and moderated t-statistic or ANOVA with the package Limma for R [28] . P values were adjusted with Benjamini-Hochberg false discovery rate ( FDR ) correction , whereby differentially expressed probes were identified by a FDR <0 . 01 and mean fold-difference ≥ 1 . 5 between VL patients and controls or asymptomatic individuals; or mean fold-difference ≥ 1 . 3 between patients under remission and VL patients , controls or asymptomatic individuals . Two different cut-offs of fold-differences were chosen in order to avoid over-estimating differentially expressed probes or penalizing particular transcriptional profiles in pathway analyses . Differentially expressed probes were collapsed into genes using the function collapseRows ( ) and “Max-Mean” method [29] from the WGCNA package for R [30] . On the basis of differentially expressed genes ( DEGs ) between the study groups , a heat map was generated to visualize the resulting hierarchical clustering of expression data performed with Euclidian distance and complete algorithm linkage . DEGs lists were incorporated to the GeneGo MetaCore pathway analysis tool ( Thomson Reuters , NY ) and used to identify genes that overlap within curated biological processes and pathways at a higher frequency than would normally be expected to occur for a randomly selected set of genes . A FDR <0 . 05 was used as a threshold to determine whether a process or pathway was statistically represented by DEGs . Co-expressed genes across the whole data set ( n = 45 samples in the same analysis ) were selected using the weighted gene co-expression network analysis ( WGCNA ) package for R [30] . Log-transformed , normalized expression data were filtered by the 3700 most variant genes . A soft threshold power beta was chosen based on the scale-free topology criterion [31] . Constructed gene networks were then used to identify modules from the topological overlap matrix with the functions cutreeDynamic ( ) and mergeCloseModules ( ) and imported for network visualization into Cytoscape v 3 . 2 . 1 . Gene Set Enrichment Analysis ( GSEA ) [32] was used to determine significant associations between blood transcriptional patterns of each study group and the modules identified by WGCNA , which were loaded as gene sets . In addition , we also implemented GSEA based on a framework of Blood Transcriptional Modules ( BTM ) which was previously constructed from over 30 , 000 human blood transcriptomes derived from more than 500 studies available in public databases [33] . GSEA parameters included weighted enrichment statistic and Signal2Noise metric , with 1 , 000 permutations . To estimate relative abundance of cell subsets from whole blood expression profiles we implemented the meanProfile method with the CellMix package for R [34] . We applied this method using previously published signatures for erythroblasts , megakaryocytes , granulocytes , monocytes , NK cells , CD4+ T lymphocytes , CD8+ T lymphocytes and B lymphocytes [35] . Reverse Transcription followed by quantitative PCR ( RT-qPCR ) was performed using Complementary DNA was synthesized starting from 200 ng of RNA using High Capacity cDNA Reverse Transcription Kit ( Applied Biosystems , Foster City , CA ) . SYBR Green real-time amplifications were performed on a Rotor-Gene 6000 instrument ( Corbett Life Science , Valencia , CA , USA ) using a set of designed primers ( S1 Table ) . Samples were analyzed in duplicate and values obtained by RT-PCR for target genes were normalized to the average of cycling threshold from "housekeeping" genes ACTB , B2M , RNA18S5 and PPIA . Fold changes were calculated according to the 2 ( -ΔΔCt ) method . Data analysis was performed with GraphPad Prism V 5 . 0 . One-way ANOVA with Bonferroni´s multiple-comparison test or one-sample t test were used to evaluate differences among independent groups . Spearman’s rank correlation was applied to assess nonparametric associations . P values less than 0 . 05 were considered significant .
To determine transcriptional signatures associated with distinct states of infection with L . infantum , we evaluated the patterns of gene expression of whole blood from active VL patients , from patients that received treatment and were considered to be under remission of disease , from healthy individuals that exhibited a positive delayed type hypersensitivity reaction to leishmanial antigens and that were considered to be asymptomatically infected with L . infantum , and control individuals that exhibited a negative delayed type hypersensitivity to leishmanial antigens and were considered to be uninfected ( Table 1 ) . Principal component analysis ( PCA ) of the 17 , 105 annotated probe sets ( 12 , 491 genes ) resulted in a consistent pattern of clustering for half of the VL patients , whereas PCA based on expression data from uninfected controls and asymptomatic individuals indicates similar global transcriptional profiles between those subjects ( Fig 1A ) . In addition , the transcriptional profile of patients under remission of disease exhibited an intermediary pattern of clustering between VL patients and uninfected controls or asymptomatic individuals ( Fig 1A ) . Linear model-based statistical analysis with a FDR < 0 . 01 identified 817 or 799 differentially expressed genes ( DEGs ) between VL patients and uninfected controls or asymptomatic individuals , respectively ( Fig 1B—upper left and middle panels—and S1 Data ) . Applying this statistical method , we observed that asymptomatic individuals did not present significant differences in whole blood gene expression when compared to uninfected controls ( Fig 1B—upper right panel ) . Further analysis resulted in 324 , 459 or 528 DEGs between expression data from patients under remission and VL patients , uninfected controls or asymptomatic individuals , respectively ( Fig 1B—lower panels—and S1 Data ) . To evaluate whether transcriptional signatures based on differential expression analysis could segregate subjects from distinct states of infection , we applied an unsupervised hierarchical clustering on expression data from highly significant DEGs between all groups ( ANOVA P < 0 . 001 , 2232 genes ) , shown in Fig 1C . The analysis resulted in two main clusters of individuals . The first cluster was comprised only by VL patients ( Fig 1C ) . The second cluster resolved into two sub-clusters; one composed mainly by uninfected controls , but which also contained asymptomatic individuals; and a second sub-cluster formed by patients under remission of disease , asymptomatic individuals and uninfected controls ( Fig 1C ) . Of interest , most patients under remission of disease clustered together into a unique group within this sub-cluster ( Fig 1C ) . Taken together , these results demonstrate that infections with L . infantum induce significant changes in the abundance of blood transcripts and that the patterns of gene expression depend on the clinical status after infection or activity of the disease . To understand the biological processes reflected by the identified transcriptional signatures , we used the GeneGO Metacore platform for functional analysis to retrieve the ontology of immunity related genes that were differentially expressed , whereby their expression changed according to each of the comparisons between the states of infection ( Fig 2A ) . Relative to uninfected controls or asymptomatic individuals , the expression of genes annotated into processes such as leukocyte chemotaxis ( CCR1 , CCR3 , CXCR1 , CXCR4 , CXCL16 , CXCL8 ) or neutrophil activation ( CXCL8 , FPR1 , C5AR1 ) were down-regulated in VL patients ( Fig 2A ) . On the other hand , up-regulated genes were mainly enriched into network processes such as NK cell cytotoxicity ( GZMA , GZMB , PRF1 ) or TCR signaling ( CD3D , CD3G , CD8A , LAT ) ( Fig 2A ) . Compared to uninfected controls , VL patients exhibited a wide modulation of genes enriched into the interferon signaling network process ( IDO1 , IFI35 , IFIT1 , IFITM2 , IFNG , SOCS1 , STAT1 , STAT2 ) ( Fig 2A and 2B ) . Yet , compared to asymptomatic individuals , GBP2 , IDO1 , IFI35 , STAT2 , TAP1 were not differentially expressed in VL patients , indeed most of the interferon signaling related genes were down-regulated in this group of individuals ( Fig 2A and 2B ) . The majority of genes enriched for the BCR-pathway were down-regulated in VL patients when compared to both uninfected controls and asymptomatic individuals ( Fig 2A ) . However , compared to other clinical-epidemiologic groups analyzed herein , genes enriched for the BCR-pathway ( BTK , CD19 , CD72 , CD79A , CD79B , LYN ) were up-regulated in patients under remission of disease ( Fig 2A ) . To obtain insights into the regulation of canonical pathways reflected by the transcriptional profiles from distinct states of infection with L . infantum , we analyzed the enrichment of up-regulated or down-regulated DEGs on pathway maps annotated in the GeneGO Metacore database ( Fig 2C ) . Compared to uninfected controls or asymptomatic infected individuals , up-regulated DEGs from VL patients were enriched in pathways such as: "antigen presentation by MHC class I" ( S1 Fig ) , "differentiation and clonal expansion of CD8+ T cells" and “Granzyme A signaling” ( Fig 2C—left panel ) . Moreover , only when compared to uninfected controls , up-regulated DEGs from VL patients were enriched in pathways as: "T regulatory cell-mediated modulation of antigen-presenting cell functions" , "IFN alpha/beta signaling" and "antiviral actions of interferons" ( Fig 2C—left panel ) . Those results suggest that VL patients exhibit an increased activation of cytotoxic T lymphocytes , which is in agreement with the up-regulation of genes related to TCR signaling ( Fig 1A ) . Moreover , these results also represent the first evidence of an increased activity of type I interferon signaling in humans infected with L . infantum . Compared to VL patients , up-regulated DEGs from patients under remission were enriched in pathways such as: "integrin inside-out signaling in neutrophils" , and "Notch signaling pathway" ( Fig 2C—left panel ) . Moreover , compared to uninfected controls or to asymptomatic individuals , up-regulated DEGs from patients under remission were enriched into metabolic pathways ( Fig 2C—left panel ) . Compared to uninfected controls or asymptomatic individuals , down-regulated DEGs from VL patients were enriched for pathways such as: "integrin inside-out signaling in neutrophils" ( S2 Fig ) , chemokine and cytokine signaling and immune receptor signaling as shown in the right panel of Fig 2C . Compared to VL patients , down-regulated DEGs identified for patients under remission were significantly enriched into pathways such as: "T regulatory cell-mediated modulation of antigen-presenting cell functions" , "initiation of T cell recruitment in allergic contact dermatitis" ( Fig 2C—right panel ) . Furthermore , compared to uninfected controls or asymptomatic individuals , down-regulated DEGs from patients under remission exhibited significant enrichments in pathways previously associated with expression data from VL patients , except for: “lipoxin inhibitory action on formyl-Met-Leu-Phe-induced neutrophil chemotaxis” , “chemokine signaling ( CCL2 , CCR3 , CXCL16 , CXCR4 ) ” , “cytokine signaling ( IL-6 , IL-8 , IL-9 , IL-10 ) “and “Fc gamma R-mediated phagocytosis in macrophages” ( Fig 2C—right panel ) . Overall , those results indicate that upon development of VL , several pathways related to the immune response are subjected to profound perturbations and suggest that the innate immune response is mainly down-regulated . In contrast , treatment might trigger the activation of pathways as Notch signaling ( Fig 2C—left panel ) or even down-regulate the transcriptional activity of pathways as “T regulatory cell-mediated modulation of antigen presenting cell functions” or “NETosis in SLE” ( Fig 2C—right panel ) . Although analysis at the level of single genes has been widely used for interpretation of expression data and discovery of biomarkers , the large number of comparisons are permissive to noise and may lack power to detect subtle , but important features of gene expression datasets [32] . Therefore , in order to obtain an additional perspective about the nature of responses reflected by transcriptional profiles from the subjects evaluated herein , we performed a weighted gene co-expression network analysis ( WGCNA ) , which is based on coordinately expressed genes for the identification of gene modules . First , we detected the 3 , 700 most variable genes from the study population , which included all forty-five samples irrespective of state of infection or treatment . Next , a hierarchical clustering was applied to expression data from those most variable genes , which identified thirteen color-coded co-expression modules ( Fig 3A—Merged dynamic ) . Eleven modules could be annotated with GeneGo Metacore and were enriched in network processes and/or pathway maps described in Table 2; genes composing specific modules are detailed in S2 Data . Demonstrative networks of genes clustered into the cyan module ( Type I interferon ) or light-green module ( antigen presentation by MHC class I ) are depicted in S3 Fig . WGCNA relies entirely on a data-driven process , which reflects fluctuations in blood transcript abundance measured across an entire population irrespective of state of infection or treatment . Therefore , the transcriptional profiles of the study subjects were graphically represented for individual modules ( Fig 3B ) . Those results demonstrate coordinated expression of genes retained in specific clusters ( Fig 3A ) and also suggest differential activity of gene modules among distinct clinical-epidemiologic groups ( Fig 3B ) . To further address this question , we conducted gene set enrichment analysis ( GSEA ) using WGCNA modules as customized gene sets and a FDR <0 . 05 ( Fig 3C ) . Compared to uninfected controls and asymptomatic individuals , we highlight that the transcriptional profiles of VL patients were associated with a positive regulation of gene modules annotated as: "TCR signaling and antigen presentation" ( blue module ) and "antigen presentation" ( light-green ) ; at the same time , transcriptional profiles of VL patients were associated with a negative regulation of gene modules annotated as: "cell adhesion and neutrophil migration" ( brown module ) , "Notch signaling pathway" ( gray60 module ) and "cell adhesion and LTBR1 signaling" ( light-cyan module ) ( Fig 3C ) . Of note , a negative regulation of the module annotated as the "B lymphocyte related module" ( salmon ) was associated with the transcriptional profile of VL patients only when compared to uninfected controls ( Fig 3C ) . Noteworthy is the fact that a positive regulation of the cyan module ( type I interferon , S3A Fig ) was also associated with the transcriptional profile of VL patients when compared to uninfected controls , however the same module was negatively regulated in VL patients when compared to asymptomatic individuals ( Fig 4C ) . Overall , compared with VL patients , uninfected controls or asymptomatic individuals , the transcriptional profiles of patients under remission were associated with a positive regulation of modules annotated as: "TCR signaling and antigen presentation" ( blue module ) , "Notch signaling pathway" ( Gray60 module ) and "B lymphocyte related module" ( salmon module ) ( Fig 3C ) . Yet , the regulation of several modules associated with transcriptional profiles of patients under remission was dependent on specific comparison with the other clinical-epidemiologic groups ( Fig 3C ) . We also evaluated differences between the transcriptional profiles of asymptomatic individuals in comparison to those of uninfected controls using this same approach . We highlight associations with positive regulations of modules annotated as: "type I interferon signaling" ( green-yellow module ) , "Notch signaling pathway" ( gray60 module ) , "cell adhesion and LTBR1 signaling" ( light-cyan module ) and "antigen presentation" ( light-green module ) ( Fig 3C ) ; Moreover , asymptomatic individuals exhibited negative regulation of modules as: "TCR signaling and antigen presentation" ( blue module ) and "B lymphocyte related module" ( salmon ) ( Fig 3C ) . Using a different strategy of analysis , we found that results from WGCNA are highly correlated with those from single gene level , capturing significant perturbations of TCR signaling and antigen presentation , as for cell adhesion and neutrophil-related modules in VL patients . Those results also reinforce the fact that treatment of VL patients triggers the activation of Notch signaling pathway and increases the transcriptional activity of B lymphocytes . Of note , using this strategy we identified that , independently of the clinical outcome , infection with L . infantum induces a transcriptional signature of type I interferon . However , this response exhibits a degree of association with distinct statuses of infection , whereby asymptomatic individuals presented with the strongest associations with activation of this module , followed by VL patients and then by patients under remission of disease . We also employed GSEA ( FDR <0 . 05 ) with a previously constructed framework of Blood Transcription Modules ( BTMs ) [33] to expand the modular analyses obtained by WGCNA and further evaluate the association of transcriptional profiles with distinct status of infection with L . infantum . Thus , transcriptional profiles of VL patients were associated with a positive regulation of several modules mainly enriched in NK cells ( Fig 4A ) , T lymphocytes ( Fig 4B ) and type I interferon response ( Fig 4C ) ; and cell cycle , synthesis and metabolism ( Fig 4D ) . On the other hand , the transcriptional profiles of VL patients were associated with a negative regulation of several modules related to myeloid cells ( Fig 4A ) , B lymphocytes and effector responses such as cell adhesion and chemotaxis , immune activation with innate sensing and signaling ( Fig 4C ) . Those results are in agreement with data from previous sections , demonstrating up-regulation of genes related to NK cells ( Fig 2A ) or activation of T lymphocytes ( Figs 2 and 3 ) , as well as for an overall down-regulation of the transcriptional activity of the innate immune response ( Figs 2 and 3 ) . Compared to VL patients , transcriptional profiles of patients under remission were mainly associated with a positive regulation of modules related to monocytes and neutrophils ( Fig 4A ) , B lymphocytes ( Fig 4B ) , as well as with a few effector pathways such as coagulation and complement systems ( Fig 4C ) . However , relative to uninfected controls and/or asymptomatic individuals , several of those same modules were actually down-regulated , whereby positive associations were found mainly for modules related to NK cells ( Fig 4A ) , both B and T lymphocytes ( Fig 4B ) and to cell cycle , synthesis and metabolism ( Fig 4D ) . Taken together , those data suggest that treatment induces significant recovery of circulation of neutrophils and monocytes , however not comparable to that seen in healthy individuals . Furthermore , the results suggest that after treatment , there is a more substantial circulation of both B and T lymphocytes , indicating that treatment might function by restoring a balance to adaptive responses ( Fig 4B ) . Additionally , compared to uninfected controls , transcriptional profiles of asymptomatic individuals were associated with a positive regulation of several innate immune cells , including those related to dendritic cells ( Fig 4A ) whereas modules related to lymphocytes were mainly down-regulated ( Fig 4B ) . Furthermore , several modules related to effector and regulatory pathways were up-regulated in asymptomatic individuals and we highlight the up-regulation of all modules related to type I interferon response , which support the finding that those individuals indeed exhibit the strongest type I interferon response among distinct statuses of infection with L . infantum . Collectively , those data point to significant differences between blood transcriptional profiles which can reflect molecular mechanisms associated with pathogenic or protective responses during infections with L . infantum . Modular analyses are highly informative for capturing differences in immune-related processes during disease , however , recent work demonstrates that gene modules are not independent and are subjected to higher coordinated regulation [36] . In view of that we sought to understand the relationship among the BTMs that were associated to the transcriptional profiles evaluated in this study . Using PCA , we extracted scores from the principal component 1 ( PC1 ) for each of the 101 modules depicted in Fig 4 and performed a hierarchical clustering for coefficients of correlation among PC1 scores , which resulted in 5 main meta-modules ( S4 Fig ) . We highlight meta-module II , which was highly enriched for modules depicting type I interferon signaling , dendritic cells and innate immune activation . Those results corroborate GSEA with BTMs . As an example , asymptomatic individuals indeed exhibited positive associations with several modules involving dendritic cells , innate immune activation and all modules related to type I interferon signaling ( Fig 4A and 4C ) , suggesting a role for dendritic cells in the strong type I interferon signature observed in asymptomatic infection . Indeed , infections with L . major induce a type I interferon signature in human dendritic cells , which is required for production of IL-12 [37] . In addition , meta-module IV was highly enriched for modules involving B and T lymphocytes , as well as cell cycle ( S4 Fig ) , which is in agreement with the proliferative characteristics of those cells . VL patients exhibited positive associations with modules depicting T lymphocytes and cell cycle , while patients under remission presented up-regulation of modules related to both B and T lymphocytes , as well as cell cycle ( Fig 4B and 4D ) . As expected , those data indicate that BTMs are correlated and support the concept that infections with L . infantum elicit the coordinated activity of a multi-factorial network of biological processes rather than perturbations in a particular compartment of the immune response . Whole blood presents a heterogeneous environment composed by numerous distinct , yet interacting cell populations , thus the transcriptional signatures from different states of infection with L . infantum could represent altered proportions of several cellular subsets . In view of these facts , we undertook a cell deconvolution analysis based on previously published cell signatures [35] . The expression signatures from erythroblasts , megakaryocytes , granulocytes , monocytes , NK cells , CD4+ T lymphocytes , CD8+ T lymphocytes and B lymphocytes of each study subject are shown in Fig 5A . Compared to uninfected controls , the relative abundance of erythroblasts increased significantly in VL patients ( Fig 5B ) . In contrast , compared to uninfected controls and asymptomatic individuals , the relative abundance of monocytes and granulocytes decreased significantly in VL patients ( Fig 5B ) . Patients under remission exhibited an increase in the relative abundance of CD8+ T lymphocytes only when compared to asymptomatic individuals ( Fig 5B ) . However , compared to VL patients , uninfected controls or asymptomatic individuals , the relative abundance of B lymphocytes increased significantly in patients under remission ( Fig 5B ) . Of interest , the relative abundance of megakaryocytes , NK cells and CD4+ T lymphocytes did not change among the clinical-epidemiologic groups ( Fig 5B ) . Indeed , those results correlate with those of the modular analyses , which also demonstrate negative associations of myeloid cells with transcriptional profiles from VL patients and patients under remission of disease ( Fig 4A ) , while modules related to B lymphocytes were highly associated with transcriptional profiles of patients under remission of disease ( Fig 4B ) . In view of that , it should be considered that the overall down-regulation of pathways ( Fig 2C ) or effector and regulatory modules ( Figs 3C and 4C ) related to the innate immune response observed for VL patients and patients under remission could be driven mainly by decreased proportions of circulating myeloid cells . This decrease , in turn , might be due to entrapment of these cells into the spleen/liver or even be related to defects of the bone marrow and release of cells into the circulation . To validate the expression obtained by microarray profiling , we also evaluated the expression of a set of genes by RT-qPCR ( S1 Table ) . Relative to the expression of housekeeping genes , fold changes of selected genes were strongly correlated with those obtained with microarray expression profiling ( Fig 6A ) . A detailed analysis of the relative expression of target genes demonstrated that microarray profiling was robust enough to capture significant differences in gene expression of highly modulated blood transcriptional profiles , such as those of VL patients and patients under remission of disease ( Fig 6B ) . Moreover , we found that compared to uninfected controls , asymptomatic individuals exhibited significant modulations on relative expression of the majority of the target genes . Those results support the benefits of combining distinct functional analysis methods in blood transcriptomics and corroborate the findings obtained with WGCNA and GSEA .
The mechanisms that drive progression towards disease or protect individuals from developing symptoms while infected with Leishmania parasites remain poorly understood . Using a genome-wide approach to investigate patterns of gene expression from whole blood , we identified transcriptional profiles that shed light on pathways and/or gene expression modules associated with distinct states of human infections with L . infantum . It is noteworthy that the transcriptional signatures identified in this study discriminated between VL patients from patients under remission of disease and healthy individuals ( Fig 1C ) . Importantly , by assessing the levels of expressions of a set of target genes by RT-qPCR we validated the robustness of the expression data acquired by microarray analysis ( Fig 6 ) . Transcriptional signatures from human samples have been shown to be sensitive to factors such as age and sex [38] and sample size [39] . The groups evaluated in this study did not exhibit significant differences in distribution of age or sex . Similar numbers of patients infected with L . braziliensis and controls were evaluated by pioneering studies that not only identified unique transcriptional signatures , but were also able to recapitulate previously described immunopathological responses in lesions of individuals with cutaneous leishmaniasis [21 , 22 , 40] . Of note , samples from patients under remission of disease were collected during distinct time points after the beginning of therapy , which could influence their blood transcriptional profiles . Nonetheless , we were able to identify transcriptional signatures that segregated patients under remission of disease from VL patients before therapy ( Fig 1C ) ; concomitantly , relative to asymptomatic individuals or uninfected controls , the majority of patients under remission of disease exhibited strong correlations of levels of expression for DEGs ( Fig 1C ) . Furthermore , linear model-based statistical analysis and adjusted P values ( FDR ) did not detect significant differences between the transcriptional profiles of asymptomatic individuals and uninfected controls ( Fig 1B and 1C ) . However , uncorrected P values retrieved 620 differentially expressed probes ( S1 Data ) , which included probes for genes shown to be differentially expressed by RT-qPCR ( Fig 6 ) . These results demonstrate that , compared to uninfected controls , asymptomatic individuals present only a subset of differentially expressed genes , whereby the large number of comparisons between 17 , 105 probes [12 , 491 genes ) can lead to a type II statistical error and inflate the rate of false negatives [41] . To overcome this issue , we conducted distinct approaches with the ability to estimate the differences between the transcriptional profiles of asymptomatic individuals and uninfected controls; the combination of distinct functional analyses and common features retrieved by them support the robustness of the immunological signatures identified for distinct states of infections with L . infantum . Therefore , we propose that in-depth analysis of transcriptional profiles from such populations , as well as longitudinal studies including patients followed throughout treatment can be useful for the prospection of new biomarkers of VL or asymptomatic infection , as well as for the prognosis after treatment and remission of disease . Previous analyses demonstrated that the in vitro infection of monocyte-derived macrophages ( MDM ) and dendritic cells ( MDC ) with Leishmania elicits both species and cell-specific expression signatures [17] . Moreover , macrophage cultures infected with species of L . donovani complex exhibit an overall suppression of gene expression , suggesting a failure of proper macrophage activation [18 , 42] . However , the regulation of gene expression in MDM infected with L . chagasi was significantly impacted by the co-culture with autologous Leishmania-naïve T cells [18] , suggesting that the inflammatory milieu of complex microenvironments such as the infection foci and peripheral blood influence the transcriptional programs of immune cells . Indeed , gene expression profiling of liver-resident macrophages ( Kuppfer cells ) from mice infected with L . donovani identified a key transcriptomic network centered around the retinoid X receptor alpha , which was only active in bystander uninfected Kupffer cells exposed to the inflammatory factors in infected livers [19] . As observed for experimental VL [43] , our study supports the view of compartmentalized responses , i . e . , the dynamics of dominant pathways in specific cells of the spleen , liver , bone marrow and peripheral blood might be differentially associated with pro-inflammatory or regulatory processes during the course of the infection . For instance , there are strong evidences that IL-10 plays a role in the suppression of the response in the spleen of VL patients [44 , 45] , but , despite increased serum concentrations or production of IL-10 in whole blood assays [8 , 46] and elevated expression of IL-10 found herein by RT-qPCR , we were unable to identify an up-regulation of the "IL-10 signaling" pathway in the peripheral blood of VL patients . On the other hand , we did identify a negative regulation of "IL-10 signaling" pathway in the transcriptional profiles of patients under remission , which might correlate with decreased levels of this cytokine and recovery after therapy [47] . We highlight that , regardless of clinical status , expression data from individuals exposed to L . infantum display positive regulations of pathways and gene modules related to "type I interferon signaling" when compared to uninfected individuals , suggesting that IFN-αβ might play important roles in infections with L . infantum . Although the role of IFN-γ in infections with Leishmania has been extensively explored , the function of type I interferon signaling is not clear [48] . Of note , transcriptomic profiling of lesions from patients infected with L . braziliensis identified a positive regulation of type I interferon signaling [22] , while L . major induces a type I transcriptional signature in human dendritic cells [37] , indicating common responses from both cutaneous and visceral infections with Leishmania . Of interest , our analyses suggest that the response induced by IFN-αβ signaling might depend on the context and clinical status of infection with L . infantum . In other words , while type I interferon signaling is elicited in both VL patients and asymptomatic individuals , it might present differential regulation of its transcriptional program in these two states of infection . In line with this concept , other work showed that only low doses of IFN-β protected BALB/c mice from progressive cutaneous disease [49] . Furthermore , the "type I interferon signaling" gene module identified herein is composed by some interferon regulatory factors ( IRF ) , in which IRF-7 was shown to exhibit a crucial role for parasite control in mice infected with L . donovani [50] . Thus , a fine regulation of IFN-αβ expression and of the transcriptional programs induced by those cytokines could promote a protective response in asymptomatically infected individuals . In contrast , an unbalanced signaling by these cytokines during chronic VL can play a similar pathological role as that observed in infections with Mycobacterium tuberculosis and Plasmodium [14 , 51] . Indeed , chronic exposure to type I interferon could impact homeostasis of CD4+ T cells [52] or even counter-regulate signaling by IL-1β [53] and limit protective mechanisms against infections with Leishmania [54] . In addition , IFNAR-deficient mice present enhanced immunity against L . amazonensis , which correlated with a critical role of neutrophils in parasite clearance [55] . The reason for such differences between VL patients and asymptomatic individuals might depend on several factors that include the genetic background of strains of both host and parasite , host nutritional status , history of exposure to vectors , co-infections with other pathogens , among other factors known to influence the magnitude and regulation of the immune response . Although the expression data of VL patients seems to depict a general suppression of pathways and gene modules associated with innate immune response , a decrease in proportions neutrophils and monocytes was suggested by gene expression modular analyses ( Figs 3 and 4 ) and validated with cell decovolution analysis ( Fig 5 ) , prompting careful interpretation . Indeed , neutropenia has been shown to be an independent risk factor for death in children with VL [56] , indicating that the low proportion of circulating granulocytes and monocytes translate into the down-regulation of genes coding for chemokine receptors and chemokines as CCR1 , CXCR1 , CXCL8 or even neutrophil activation receptors as FPR1 , C5AR1; in contrast , the up-regulation of IFNG underscores the increased levels of IFN-γ present in serum from VL patients [8] , whereas up-regulation of both IFNG and CXCL10 support the activation of T lymphocytes and development of a Th1 response during active disease [6] . Accordingly , we were unable to identify significant differences in the relative proportions of T lymphocytes from VL patients compared to other groups ( Fig 5B ) , indicating that DEGs annotated into processes as TCR signaling were not influenced by the relative proportion of those cells . Dysfunctional responses during chronic VL might originate from failures in proper antigen presentation and stimulation , indicated by the significant associations between polymorphisms in the HLA class II region and susceptibility to visceral leishmaniasis [57] , as well as by the negative regulation of gene modules related to major histocompatibility complex ( MHC ) class II identified in expression data from VL patients ( Fig 4C ) . Yet , the extensive up-regulation of pathways and gene modules related to TCR signaling and antigen presentation through MHC class I suggests a chronic stimulation of CD8+ T lymphocytes during VL and correlates with previous findings from studies that used different analytical approaches [58 , 59] . Persistent cross-linking of TCR and MHC class I without appropriate co-stimulation of CD8+ T lymphocytes results in an exhausted cellular phenotype , which is characterized by the expression of the inhibitory receptors PD-1 , CTLA-4 , LAG3 , TIM3 and TIGIT [60] . Indeed , CD8+ T lymphocytes isolated from VL patients exhibit increased expression of inhibitory surface receptors [58] . We also identified increased expression of PD-1 , CTLA-4 and LAG3 in expression data from VL patients , while expression of such genes was down-regulated in patients under remission of the disease ( S1 Data ) . Those findings suggest that perturbations in antigen presentation pathways may lead to inefficient activation and differentiation of CD4+ T lymphocytes , promote the exhaustion of CD8+ T lymphocytes and account for parasite evasion from the host response during VL . Proper antigen presentation is crucial for T cell activation and differentiation , but other factors might impact lymphocyte function during VL . Indeed , T cell-specific deletion of Notch 1 and Notch 2 demonstrated that they are required for efficient development of Th1 immune responses and resistance in mice infected with L . major [61] , thus polymorphisms affecting such molecules and transcriptional programs induced by their activation may influence T cell responses during infections with L . infantum . Indeed , a genome-wide association study in mixed-breed dogs with VL identified a marker located between two predicted transcription factor binding sites that regulate the expression of TLE1 , a molecule involved in the Notch signaling pathway [62] . Another perspective is given by the demonstration that Notch 1 signaling pathway drives the activation of mouse macrophages into a M1 phenotype through metabolic up-regulation of mitochondrial oxidative phosphorylation and attendant reactive oxygen species [63] , molecules that are implicated in the killing of L . braziliensis by human classical monocytes [64] . In the light of those findings , the positive associations between "Notch signaling pathway" with transcriptional profiles of patients under remission of disease and asymptomatic individuals support a protective role of this pathway in human infections with L . infantum . Cell deconvolution analysis corroborates previous investigations focused on lymphocyte proportions in peripheral blood of VL patients [45 , 65] , whereby strong associations between transcriptional profiles of patients under remission with B lymphocyte-related modules likely reflect higher abundance of those cells in the peripheral blood after therapy [65] . Despite this , hypergammaglobulinemia is frequently observed in VL patients due to polyclonal activation of B lymphocytes [66] . However , modules related to B lymphocytes were mainly down-regulated in VL patients and asymptomatic individuals , which suggests that after infections with L . infantum , activated B lymphocytes undergo differentiation to plasma cells and migrate to specific niches such as the bone marrow [67] , while those remaining in the periphery might display unique transcriptional programs and functions . Indeed , follicular T cell-mediated regulation of the B lymphocyte compartment may account for beneficial or pathogenic responses during distinct states of infection with L . infantum [68] , a hypothesis that is supported by significant differences in structural and functional features of immunoglobulin G isolated from VL patients and asymptomatic individuals [8] . In conclusion , this is the first attempt to screen for blood transcriptional signatures from distinct states of infection of humans with L . infantum . Future studies including individuals of other populations , as well as investigations focused on specific pathways highlighted by these signatures shall confirm and extend the hypotheses discussed herein . These signatures point to novel directions for studying human immune responses after infections with L . infantum , which can guide development of new strategies of intervention . | Infections of humans with the protozoan parasites L . donvani and L . infantum can lead to the development of the disease visceral leishmaniasis , but also to an asymptomatic status . However , the mechanisms that result in these clinical outcomes after infection are poorly understood . In this study , we applied a data-driven approach to obtain insights into the immunological processes linked to the progression of the disease or to protective mechanisms . For this purpose , we evaluated the patterns of expression for genes that code proteins from the entire human genome in the peripheral blood from patients with visceral leishmaniasis , from individuals who remained asymptomatic after infections with L . infantum , from patients who were recovering from disease after treatment and from uninfected individuals . By employing computational analysis to evaluate the blood transcriptional activity of each group , we identified transcriptional signatures that correlate with previous findings obtained through different analytical methods . Moreover , our analyses uncovered hitherto unidentified molecular pathways and gene networks associated with the transcriptional profiles of individuals recovering from disease or that did not develop symptoms after infection . This suggests that activation of protective responses can be useful targets for the development of new therapies for visceral leishmaniasis . | [
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] | 2016 | Blood Transcriptional Profiling Reveals Immunological Signatures of Distinct States of Infection of Humans with Leishmania infantum |
Genetic recombination is an important mechanism for increasing diversity of RNA viruses , and constitutes a viral escape mechanism to host immune responses and to treatment with antiviral compounds . Although rare , epidemiologically important hepatitis C virus ( HCV ) recombinants have been reported . In addition , recombination is an important regulatory mechanism of cytopathogenicity for the related pestiviruses . Here we describe recombination of HCV RNA in cell culture leading to production of infectious virus . Initially , hepatoma cells were co-transfected with a replicating JFH1ΔE1E2 genome ( genotype 2a ) lacking functional envelope genes and strain J6 ( 2a ) , which has functional envelope genes but does not replicate in culture . After an initial decrease in the number of HCV positive cells , infection spread after 13–36 days . Sequencing of recovered viruses revealed non-homologous recombinants with J6 sequence from the 5′ end to the NS2–NS3 region followed by JFH1 sequence from Core to the 3′ end . These recombinants carried duplicated sequence of up to 2400 nucleotides . HCV replication was not required for recombination , as recombinants were observed in most experiments even when two replication incompetent genomes were co-transfected . Reverse genetic studies verified the viability of representative recombinants . After serial passage , subsequent recombination events reducing or eliminating the duplicated region were observed for some but not all recombinants . Furthermore , we found that inter-genotypic recombination could occur , but at a lower frequency than intra-genotypic recombination . Productive recombination of attenuated HCV genomes depended on expression of all HCV proteins and tolerated duplicated sequence . In general , no strong site specificity was observed . Non-homologous recombination was observed in most cases , while few homologous events were identified . A better understanding of HCV recombination could help identification of natural recombinants and thereby lead to improved therapy . Our findings suggest mechanisms for occurrence of recombinants observed in patients .
RNA viruses are rapidly adapting to their environment . The error-prone viral polymerases and the lack of proofreading mechanisms for most RNA viruses lead to high mutation rates . Genetic recombination between viral genomes is an additional mechanism increasing genetic diversity , which has proven to be epidemiologically relevant and allows RNA viruses to adapt to their surroundings [1] . Recombination could allow escape from natural or therapeutically induced immunity [2] , or during antiviral treatment constitute an escape mechanism to antiviral compounds with an otherwise high barrier to resistance [3] . In addition , viral recombination has been associated with increased pathogenicity [4] , and has caused the emergence of new human pathogens , such as Western equine encephalitis virus [5] . The use of live attenuated viral vaccines has led to re-emergence of disease due to recombination of vaccine strains with related viruses [6] , [7]; this remains a problem in poliovirus eradication . Thus , understanding the nature of viral recombination has general evolutionary implications , and might affect treatment and vaccination for important human pathogens . Significant differences have been reported in recombination frequencies for different virus families , with high frequencies among Picornaviridae and lower frequencies among Flaviviridae and Alphaviridae [8] . Although hepatitis C virus ( HCV ) belongs to the Flaviviridae family , several epidemiologically important recombinant strains have been reported [9]–[11] . HCV constitutes a major public health burden with 130–170 million people chronically infected . Infection leads to increased risk of hepatitis , liver cirrhosis and hepatocellular carcinoma . The single positive-stranded HCV RNA genome of around 9600 nucleotides encodes one long open reading frame ( ORF ) flanked by 5′ and 3′ untranslated regions ( UTRs ) . The HCV polyprotein is co- and post-translationally processed into structural ( Core , E1 and E2 ) , and nonstructural proteins ( p7 , NS2 , NS3 , NS4A , NS4B , NS5A and NS5B ) . Significant diversity is found among HCV isolates , which are grouped into seven major genotypes and many subtypes [12] . Genotypes , subtypes and isolates/strains differ at around 30% , 20% and 2–10% , respectively , at the nucleotide and amino acid levels . The epidemiologically most important HCV recombinant is the homologous recombinant of genotype 2k/1b that was first identified in St . Petersburg [13] . Since then , a number of naturally occurring inter- and intra-genotypic recombinants have been reported [9]–[11]; most inter-genotypic recombinants have junction in or close to the NS2 gene . Further , naturally occurring subgenomic deletion mutants have been described to persist in around 20% of patients [14] , [15] . The prevalence of recombinants might be underestimated due to lack of routine screening; in addition , recombination events between isolates of the same subtype could be difficult to identify and distinguish from new isolates [16] . While mechanisms and kinetics remain problematic to study in patients , in vitro systems could provide a better understanding of HCV recombination , leading to improvements in detection of natural recombinants . Treatment with interferon-α and ribavirin leads to sustained viral response for only around half of HCV infected patients , and many cannot be treated due to side effects or contraindications . The recent approval of novel directly acting antiviral compounds is expected to increase successful treatment rates . Great HCV genotype-specific differences exist in the outcome of antiviral therapy , and in the recommended treatment regimens [17] , [18] . Thus , genotyping from a single gene region could mislead decisions on treatment regimens for recombinant viral strains . In addition , RNA recombination could function as an escape mechanism to therapy with novel directly acting antiviral compounds . Two possible mechanisms of RNA recombination are generally considered for RNA viruses: replicative and non-replicative . In the replicative copy-choice mechanism , the viral polymerase changes template during RNA synthesis whereas in the non-replicative mechanism , RNA breakage and rejoining occur . Both mechanisms can in principle lead to homologous and non-homologous recombinants . The copy-choice mechanism is the best characterized [1] , [19] , [20] , and was first described for poliovirus [21] . Productive non-replicative recombination was so far only demonstrated in few studies on poliovirus [22] and bovine viral diarrhea virus ( BVDV ) [23] , which belongs to the HCV-related pestiviruses . Few experimental studies have investigated recombination of HCV , and our understanding of its mechanisms is still limited . One study examined HCV recombination in co-infected chimpanzees and identified homologous recombinants between genotypes 1a and 1b [24] . In another recent study , recombination frequency was investigated using the bicistronic selectable HCV replicon system [25] . In the present study , we aimed at investigating the nature of HCV recombination in infectious cell culture systems .
To study HCV recombination , an assay was established using the Huh7 . 5 hepatoma cell line . Since recombination of HCV is thought to be a relatively rare event , HCV genomes lacking viability in vitro were co-transfected to facilitate the identification of viable recombinants . RNA transcripts of the JFH1ΔE1E2 genome were transfected alone or in combination with either the J6CF or J6/JFH1-GND genome ( all genotype 2a , Figure 1 ) . JFH1ΔE1E2 carries a partial deletion of the envelope genes , which allows replication but not viral particle production . The consensus full-length clone of the J6 isolate , J6CF , does not replicate in Huh7 . 5 cells [26] but has a functional 5′UTR-NS2 region in vitro [27] , while the replication-deficient J6/JFH1-GND , carries an NS5B polymerase mutation in the viable J6/JFH1 background [28] . In all experiments , around 30% of cells were positive for HCV Core one day after transfection ( Figure 2A ) ; this percentage rapidly decreased due to lack of spread of infection and growth advantages of untransfected cells , as previously shown [29] . HCV RNA levels in the supernatant were comparable for all cultures during the first 8 days ( Figure 2B ) and no infectious particles were released from any of the cultures on day 3 and 6 ( Figure 2C ) . An increase in percentage of HCV positive cells and HCV RNA levels was observed for the culture co-transfected with JFH1ΔE1E2 and J6CF from day 10 and infection spread to the almost entire culture on day 13 . Similarly , infection spread to the majority of cells around day 36 in the culture co-transfected with JFH1ΔE1E2 and J6/JFH1-GND . After spread of infection in culture , infectivity titers of around 104 focus-forming units ( FFU ) /mL or 103 FFU/mL , respectively , were observed in supernatant from the two cultures ( Figure 2C ) . After passage of supernatant from the J6CF co-transfected culture to naïve cells , HCV RNA titers above 107 IU/mL and infectivity titers around 104 FFU/mL were produced . Two additional co-transfections of JFH1ΔE1E2 and J6CF led to similar results , with spread of infection to the majority of the culture after 8 and 25 days , respectively . To determine the nature of the infectious HCV genomes from the original co-transfection of JFH1ΔE1E2 with J6CF after passage to naïve cells , we performed direct sequencing of 12 overlapping PCR amplicons covering the entire ORF . While amplicons 1–2 ( 5′UTR-E2 ) had J6 sequence , amplicons 3–12 ( E2-3′UTR ) had JFH1 sequence; amplicons 2 and 3 contained overlapping sequence in E2 from both strains , which indicated the presence of a duplicated region . This was further analyzed for all three cultures co-transfected with JFH1ΔE1E2 and J6CF by cloning of longer PCR amplicons and amplicons generated by inverted primer sets . The resulting sequences revealed non-homologous recombinant genomes with different genomic structures . The first recombinant had J6 sequence from the 5′UTR to nucleotide ( nt ) 2986 ( NS2 ) , recombined with JFH1ΔE1E2 from nt 872 ( Core ) to the 3′UTR ( Rec#1; including the envelope deletion from nt 991 to 2040 ) ( Figure 3 ) . This recombination produced an in-frame non-homologous recombinant HCV ORF containing 1065 duplicated nts ( 355 amino acids ) with a total predicted genome length of 10743 nts , compared to 9678 for JFH1 and 9711 for J6CF . A second recombinant had J6 sequence from the 5′UTR to nt 2870 ( NS2 ) , recombined with JFH1ΔE1E2 at nt 561 ( Core ) ( Rec#2 ) ( Figure 3 ) . The third recombinant had breakpoint further downstream with J6 sequence from the 5′UTR to nt 4254 ( NS3 ) joined to JFH1ΔE1E2 from nt 796 ( Core ) ( Rec#3 ) . The resulting genome had a predicted length of more than 12 kb , over 2400 nucleotides longer than natural HCV isolates . While this is longer than typical infectious HCV reporter constructs expressing fluorescent or luminescent markers [30] , much longer BVDV recombinants ( up to around 20 kb ) were identified in similar cell culture recombination experiments [23] . It was previously demonstrated that the NS3 helicase contributes to the unique replication abilities of the JFH1 isolate [31] . Since this might have restricted the region of recombination in co-transfections of JFH1ΔE1E2 and J6CF , we investigated whether a different type of recombination event had occurred in the culture co-transfected with JFH1ΔE1E2 and J6/JFH1-GND , where both genomes carried an NS3 protein of JFH1 origin . After passage of viral supernatant to naïve cells , sequencing of the entire ORF from recovered viruses again showed J6 sequence for amplicons 1–2 and JFH1 sequence for amplicons 3–12 . In further analysis , PCR amplicon clones covering the junction revealed a recombinant genome with J6/JFH1-GND sequence from the 5′UTR to nt 2971 ( NS2 ) , followed by JFH1ΔE1E2 from nt 860 ( Core ) to 3′UTR ( Rec#4 ) ( Figure 3 ) , similar in structure to those already identified . In the initial recombination assay , a replicating genome ( JFH1ΔE1E2 ) was co-transfected with a non-replicating genome ( J6CF or J6/JFH1-GND ) . To determine whether putative low-level replication of J6CF or replication of J6CF in trans by the JFH1 replicase played a role in recombination , we co-transfected JFH1ΔE1E2 with J6Δ3′ . J6Δ3′ was produced by linearization of the DNA in the beginning of NS5B and would therefore not express the polymerase or carry a 3′UTR ( Figure 1 ) . This experiment led to results similar to co-transfections of JFH1ΔE1E2 with J6CF , with spread of infection to the majority of the culture after 13 days . After passage to naïve cells , sequencing of the replicating genome demonstrated a junction from NS2 of J6Δ3′ to Core of JFH1ΔE1E2 ( Rec#5 , Figure 3 ) . Thus , a functional J6 polymerase and a complete 3′UTR was not a requirement for recombination , which apparently did not depend on replication of both genomes . To determine whether at least one functional HCV polymerase would be required for recombination , we co-transfected two non-replicating genomes . Four replicate co-transfections were performed using J6CF , which is unable to replicate in vitro , and JFH1Δ5′ , which lacks the entire 5′UTR and therefore cannot undergo translation or replication ( Figure 1 ) , such that no viral replication could occur in the transfected cells . In addition , JFH1Δ5′ was co-transfected with J6Δ3′ ( one replicate ) or with transcripts from the pJ61–7666 plasmid ( four replicates ) , which was constructed to only contain J6 5′UTR-NS5A sequence , thus ensuring that no polymerase protein was produced ( Figure 1 ) . In these experiments , no or very few HCV positive cells were observed by immunostaining one day after transfection . However , infection emerged in few cells in all cultures by day 4 and spread to the majority of all nine cultures in 10–32 days . After passage to naïve cells , replicating genomes were characterized by sequencing . Three of the four recombinants from the cultures co-transfected with complete J6CF genomes had structures similar to those identified in the JFH1ΔE1E2 co-transfections; one had junction from p7 to E2 ( Rec#6 ) , another from NS2 to Core ( Rec#7 ) , and the third from NS3 to E1 ( Rec#8 ) . Interestingly , the last recombination event was homologous with breakpoint between nt 2710–2717 in p7 ( Rec#9 ) ( Figure 3 ) . In the culture co-transfected with J6Δ3′ , we identified a heterologous recombinant with a short duplication of just 33 nts and junction from nt 2811 ( NS2 ) to nt 2779 ( p7 ) ( Rec#10 ) ( Figure 3 ) . Heterologous recombinants were also observed in all four cultures after co-transfection with J61–7666 , with junctions from NS2 to Core ( Rec#11 ) , from NS2 to E2 ( Rec#12 ) or from NS2 to p7 ( Rec#13 and Rec#14 ) ( Figure 3 ) . To validate that no translation was occurring from JFH1Δ5′ leading to the presence of HCV polymerase , we generated a JFH1Δ5′-RLucΔ40 reporter construct with renilla luciferase inserted into NS5A [30] , and measured low-level translation from transfected input RNA in luciferase assays . In measurements from 4–48 hours post transfection luciferase signals were observed for the positive control , J6/JFH1-RLucΔ40 , and 4–8 hours after transfection for J6/JFH1-GND-RLucΔ40 , for which translation but not replication could occur . In contrast , signals for JFH1Δ5′-RLucΔ40 were comparable to the background signal for all time points ( Figure 4 ) . Thus we concluded that a functional HCV polymerase was not required for recombination to occur in cell culture . To confirm that the identified non-homologous recombinants were viable , two representative clones , J6/JFH1ΔE1E2 ( Rec#1 ) and J6/JFH1 ( Rec#10 ) were generated based on the original J6CF , JFH1ΔE1E2 and JFH1 consensus clones . After transfection into Huh7 . 5 cells , J6/JFH1ΔE1E2 ( Rec#1 ) and J6/JFH1 ( Rec#10 ) immediately spread in culture and produced infectivity titers greater than 104 FFU/mL ( Figure 5 ) . Similar infectivity titers were produced after passage of J6/JFH1ΔE1E2 ( Rec#1 ) and J6/JFH1 ( Rec#10 ) supernatant to naïve cells . Sequencing of the entire ORF confirmed the identity of the replicating recombinants . J6/JFH1 ( Rec#10 ) did not acquire mutations , while J6/JFH1ΔE1E2 ( Rec#1 ) had acquired A2071S and C2574R ( A1712S and C2215R according to the H77 reference polyprotein , AF009606 ) . These changes were not observed from the original co-transfected culture . Thus , the recombined genomes were fully viable in cell culture and the initially identified genomic structures were confirmed . To determine whether sequential recombination events could occur on the same genome , we performed long term passaging of the J6/JFH1ΔE1E2 ( Rec#1 ) and J6/JFH1 ( Rec#10 ) recombinants by serial inoculation of naïve cells with supernatant from fully infected cultures . Interestingly , after three passages to naïve cells a novel recombinant was detected in the J6/JFH1ΔE1E2 ( Rec#1 ) culture . The genetic structure of the new genome showed that an additional non-homologous recombination event had taken place and removed most of the duplicated region , resulting in a junction from nt 2823 ( NS2 ) of J6 to nt 2638 ( p7 ) of JFH1 ( Rec#1 . 1 , Figure 3 ) . This second-generation recombinant was detectable from passage 3 and dominated the virus population from passage 6 ( Figure 6A and B ) . The peak supernatant infectivity titer increased in passage eight , where the shorter Rec#1 . 1 genome dominated ( Figure 6A and C ) . In contrast , no changes occurred to the comparably short duplicated junction region of J6/JFH1 ( Rec#10 ) during 8 serial passages . Infectivity titers of almost 105 FFU/mL were observed in most passages for this apparently genomically stable recombinant ( Figure 6C ) . Thus , sequential recombination events could take place in culture to eliminate long duplicated and presumably non-functional genome regions , apparently leading to increase of viral fitness . All 14 co-transfection experiments with J6 and JFH1-based genomes performed so far led to emergence of viable recombinants . To get a more quantitative understanding of recombination frequencies we re-plated cells co-transfected with JFH1Δ5′ and J61–7666 into 96-well format before virus production was expected to occur . This would allow an estimation of recombination frequency between the genotype 2a isolates J6 and JFH1 over the Core-NS5A region . Through 22 days of follow up , 8 controls transfected with J6/JFH1 were positive , while recombination occurred in 4/72 ( 5 . 6% ) co-transfected wells ( Figure 7 ) . Taking into account that 7000 cells were plated per well and that the transfection efficiency was 50% ( assuming that co-transfection had the same efficiency as observed when evaluating NS5A positive cells one day post transfection of J6/JFH1 ) this equals to one productive recombination event for every 63 , 000 co-transfected cells , or recombination in 0 . 0016% of the cells . Intergenotypic recombinants were previously identified in vivo [9]–[11] , and synthetic intergenotypic recombinants could establish infection in cell culture [32] , [33] . Thus , we next investigated whether recombination in vitro could also occur between isolates of different genotypes . Since efficient replication in the infectious cell culture system at the outset of this study relied on the JFH1 isolate , we co-transfected JFH1ΔE1E2 with consensus clones of genotype 1a ( H77C and HC-TN ) , 1b ( J4L6S ) , 3a ( S52 ) or 4a ( ED43 ) or with 3′ truncated versions ( truncation in NS5B ) of the same genomes . Similarly to J6CF , these clones are infectious in chimpanzees but cannot replicate in Huh7 . 5 cells [26] . RNA transcripts of JFH1ΔE1E2 were co-transfected with H77C , HC-TN ( 3 replicates each ) , H77CΔ3′ , HC-TNΔ3′ , J4L6S , J4L6SΔ3′ , S52 , S52Δ3′ , ED43 or ED43Δ3′ ( 1 replicate each ) . The percentage of HCV positive cells in most cultures was similar to transfection of JFH1ΔE1E2 alone , with a rapid decrease leading to no positive cells from around day 20 . However , few HCV positive cells remained in the culture co-transfected with S52Δ3′ and infection eventually spread to the almost entire culture after 82 days ( Figure 8 ) . After passage of supernatant to naïve cells , cloning of PCR amplicons identified intergenotypic non-homologous recombination events . Of 13 clones , 6 contained S52 sequence until nt 2835 ( NS2 ) and JFH1 sequence from nt 2291 ( E2 ) ( Rec#15a ) , while 7 clones had a slightly different junction between nt 2893 ( NS2 ) of S52 and nt 2397 ( E2 ) of JFH1 ( Rec#15b ) ( Figure 3 ) . While only two mutations were identified after passage in culture of the genotype 2a/2a recombinant Rec#1 , direct sequencing of the almost entire ORF of the S52/JFH1 ( 3a/2a ) recombinant identified a number of mutations , including coding mutations in Core , E1 , E2 , p7 , NS4B and NS5A . This indicated a need for adaptive mutations for functional interaction of isolates from different genotypes . We previously demonstrated that most synthetic JFH1 recombinants with genotype-specific Core-NS2 relied on adaptive mutations for efficient production of intracellular infectious particles [32] , [34] . Since many recombination events identified in this study occurred in the NS2 region , we speculated that recombination between genomes carrying previously identified adaptive mutations might enhance the production of functional intergenotypic recombinants in our assay . We thus co-transfected JFH1ΔE1E2 with J4L6SF886L or ED43T827A , T977S that carried mutations previously shown to confer adaptation to the Core-NS2 recombinants , J4/JFH1 and ED43/JFH1 [32] , [35] . While no recombination occurred in triplicate co-transfections with ED43T827A , T977S , co-transfection with J4L6SF886L resulted in spread of infection to the majority of cells 93 days post-transfection ( Figure 8 ) . After passage to naïve cells , sequencing identified intergenotypic non-homologous recombination . The replicating genome contained J4L6SF886L sequence from the 5′UTR to NS3 and JFH1 from NS2 to the 3′UTR , and carried the introduced mutation F886L ( Rec#16 , Figure 3 ) . Thus , introduction of mutations conferring adaptation to synthetic intergenotypic JFH1-based Core-NS2 recombinants had only limited effect , on recombination frequency . While all intragenotypic co-transfections performed with high input RNA led to emergence of viable recombinants , only two intergenotypic recombination events were identified from a total of 18 co-transfection experiments . Considering all co-transfection experiments with JFH1ΔE1E2 and different clones of other genotypes , an estimated generalized recombination frequency would be one productive recombination event per million co-transfected cells , or recombination in 0 . 0001% of the cells , taking into account two productive recombination events , the starting number of 400 , 000 cells in each of 18 experiments and an estimated average transfection efficiency of 30% . The recombination events identified so far all had breakpoints in the p7-NS3 region of the 5′ fragment and the Core-NS2 region of the 3′ fragment . Due to the lack of functional envelope genes in the JFH1ΔE1E2 construct , many recombination breakpoints were however restricted from occurring further upstream . Likewise , due to the importance of the NS3 helicase for the unique replication abilities of the JFH1 isolate [31] , breakpoints could be restricted from occurring further downstream of non-JFH1 genomes . To investigate whether recombination events could occur in other regions , we co-transfected JFH1Δ5′ with versions of J6CF truncated at nt 708 , 1344 , 2407 , 2564 , 2972 or 3479 ( Figure 1 ) . While no spread of infection was identified in two cultures ( J61–708 and J61–2564 ) , the majority of cells in the other cultures became infected after 13–22 days . Recombined genomes were identified after passage to naïve cells . Another case of homologous recombination was identified in the culture co-transfected with J61–1344 , occurring in the nt 858–883 region ( Core ) ( Rec#17 ) . The three other recombination events were non-homologous with junctions from E1 to Core ( Rec#18; J61–2407 ) , a mixed population of 2878 ( NS2 ) /2261 ( E2 ) and 2901 ( NS2 ) /2521 ( E2 ) ( Rec#19a/b; J61–2972 ) , and from NS2 to E2 ( Rec#20; J61–3479 ) ( Figure 3 ) . Thus , recombination of J6 and JFH1 occurred outside the NS2 region , even in the most upstream gene , Core . Next , we wanted to determine whether recombination could occur downstream of NS3 . Since JFH1 exhibits efficient function of the NS3-NS5B region in Huh7 . 5 cells , we transfected 5′ truncated transcripts of J6/JFH1 together with J6/JFH1/3′X , which carried the 5′UTR-NS2 from J6CF , NS3-3′UTR ( polyU ) from JFH1 and an irrelevant human mRNA sequence replacing the 3′X region ( Figure 1 ) . No HCV positive cells were observed when any of these genomes were transfected alone . Thus , J6/JFH1/3′X was co-transfected with J6/JFH1Δ5′ lacking the 5′UTR , J6/JFH1Δ ( 5′-p7 ) , J6/JFH1Δ ( 5′-NS4A ) , J6/JFH1Δ ( 5′-NS4B ) , or J6/JFH1Δ ( 5′-NS5A ) . While no productive recombination occurred in the J6/JFH1 ( Δ5′-NS4B ) co-transfected culture , infection spread in all other cultures after 8–17 days . Interestingly , identical recombinants were identified after passage of virus from all four positive cultures to naïve cells . The breakpoint was in NS5B from nt 9338 to nt 8517 ( Rec#21 ) ( Figure 3 ) ; this recombination took place in a region where 11 of 12 consecutive bases were conserved . Depending on the primers used , wild-type NS5B sequence could also be amplified from these cultures . Independent confirmation of the junction site by RT-PCR excluded cross contamination between the samples with identical breakpoint . Since four identical recombinants were observed , we also cloned this recombinant type , J6/JFH1 ( Rec#21 ) , and analyzed it in reverse genetic studies . Surprisingly , the input recombinant with the duplicated region could only be detected one day after transfection , while wild-type virus was detected thereafter . A silent mutation introduced in NS4B was amplified together with the duplicated region to exclude contamination . Thus , Rec#21 apparently resulted from one recombination event leading to a transient state , which was rapidly followed by a second recombination event leading to wild-type J6/JFH1 sequence . The presence of wild-type NS5B sequence also in the original cultures was in accordance with Rec#21 representing a transient state . Thus , efficient recombination was demonstrated also in the 3′-end of the HCV genome . In co-transfections unbiased by the selection of HCV isolates [both J6/JFH1Δ5′ and J6/JFH1/3′X carried the complete J6/JFH1 ORF] , a longer stretch of conserved nucleotides seemed to be preferred over the NS2 region for the recombination breakpoint .
In this study , efficient HCV RNA recombination leading to robust virus production was demonstrated in cell culture . Most recombination events were non-homologous with large in-frame insertions of up to 2400 nucleotides , while fewer homologous events were identified . Almost all recombinants identified from replication defective genomes were of different nature , and we thus found no strong site specificity . Further , recombination occurred most efficiently between isolates of the same genotype . Most identified recombinants maintained at least one complete copy of each HCV protein and many recombinants carried two copies of one or more genes . It remains to be determined whether such duplications could produce two different functional protein copies , e . g . leading to viral particles carrying envelope proteins of different isolates or give any advantage to the virus . Only one recombinant type did not have at least one intact copy of all HCV genes ( Rec#21 ) . Though this recombinant type had an internal junction in NS5B , it carried an intact globular finger-palm-thumb structure followed by duplicated sequence and finally the C-terminal membrane anchor [36] . Interestingly , HCV RNA recombination did not depend on HCV replication as co-transfection of two replication incompetent genomes led to productive recombination ( Rec#6-14 and #17-21 ) . Further , the frequencies of recombination and the time until spread of infection in culture did not seem to differ between co-transfections with and without replication competent genomes . A non-replicative mechanism for HCV recombination is in agreement with findings in cell culture for the related BVDV [23] and for poliovirus [22] . This type of recombination was shown primarily to take place at single-stranded RNA structures [37] , and it is hypothesized to occur through endoribonucleolytic cleavage and subsequent ligation of 3′-phosphate and 5′-hydroxyl partners . It remains to be determined by which mechanism ( s ) HCV recombination occurs in patients . The replicative copy-choice mechanism has previously been favored , since it is straightforward to envision how this strategy could produce the homologous recombinants observed in vivo . Accordingly , a model that could explain the generation of the 2k/1b recombinant from St . Petersburg by template switching was previously suggested [38] . Here we demonstrated that homologous recombinants could be produced through a non-replicative mechanism ( Rec#9 and Rec#17 ) , which could represent an alternative or parallel pathway to replicative recombination in vivo . After long-term passage in culture of the non-homologous recombinant J6/JFH1ΔE1E2 ( Rec#1 ) a more fit variant emerged , replacing the original replicating genome and leading to higher viral titers . This new recombinant resulted from a second recombination event and carried a duplication of only 186 nts compared to the original 1065 nts . Since the original recombinant was cloned and the second event occurred after a new transfection and subsequent cell-free passages , recombination must have occurred from the same genome or among genomes with identical structures and sequentially led to a more fit variant with a smaller insertion . Similar deletions of heterologous sequences have been observed in cytopathogenic BVDV genomes with heterologous sequences [39] , and HCV genomes with inserted reporter genes in cell culture and in vivo [30] , reflecting the virus ability to evolve and increase its fitness . Non-homologous recombinants have not been observed in patients [9]–[11] , potentially due to strong fitness selection for homologous recombinants . However , non-homologous recombinants could represent precursors to more fit homologous recombinants through sequential recombination events , as we observed in reverse genetic experiments with J6/JFH1 ( Rec#21 ) . Co-transfections with two genomes of the same genotype led to productive recombination events in 22 of 25 experiments ( 86% ) or 0 . 0016% of cells , whereas only 2 out of 18 ( 11% ) or 0 . 0001% of cells in intergenotypic experiments led to productive recombination . Except for two cases of homologous recombination , all identified events were non-homologous . Reiter et al . previously described homologous recombination in the HCV replicon system [25] . However , since duplicated regions generated by non-homologous recombination between fragments of the same isolate could be obscured in direct sequencing from PCR products , non-homologous recombinants could possibly also have been occurring in that study . The recombination frequency in the replicon-based study ranged from one event per 3 , 000 to 30 , 000 cells , depending on the length of the genomic region available for recombination , or 0 . 003 to 0 . 03% of cells replicating wild-type replicons in parallel experiments [25] . This was slightly higher than frequencies observed in the present study , however in the replicon system , selection could allow less fit recombinants to survive and some recombination events might be compatible with replication but not with the complete viral life-cycle . In a study of cells infected with a non-cytopathogenic BVDV strain , which were subsequently transfected with a defective cytopathogenic genome , recombination events were observed in 33–58% of cultures when electroporated cells were plated in 24-well format , or roughly equivalent to one event per 0 . 001% of cells ( assuming around 105 cells per culture ) [23] . This was in the range of what was observed in the present study on HCV . A notable difference , however , is that for BVDV this occurred for a viable genome , while observation of similar recombination frequencies for HCV depended on two non-viable genomes . A direct comparison of frequencies is complicated , since recombination is thought to be affected by the length of the genomic region available for recombination [25] , replication capacity and constraints on genome organization of productive recombinants . Studies with poliovirus and BVDV previously showed that the frequency of homologous recombination decreased with decreasing sequence homology between the RNA molecules [21] , [39] , and that non-homologous recombination was the most frequent for recombination between different BVDV strains . In this study , productive recombination more often took place between isolates of the same HCV genotype . The identification of several recombination events at a conserved nucleotide sequence in NS5B supports the importance of similar sequences for recombination to occur . Another explanation could be the higher functional compatibility between proteins of the same genotype expressed by the recombined RNA . The lack of sequence conservation at a number of recombination sites ( Figure 3 ) indicates that sequence similarity is not a prerequisite for non-homologous recombination to occur . On the other hand , the high frequency of ambiguous nucleotides in recombination sites in this study ( residues around the recombination site that are identical in the two parental sequences; Figure 3 ) , indicates a role for primary sequences in dictating junction sites . Random joining would leave one ambiguous nucleotide in one of four recombination events , two ambiguous nucleotides in one of 16 events etc . Thus , the frequency of ambiguous nucleotides in cross over sites in this study is higher than expected . The low frequency of intergenotypic recombination events identified in this study is in some contradiction to the ratio of inter- and intragenotypic recombinants identified in patients [10] . However , since intragenotypic recombinants by nature are harder to define , their existence could be underrepresented in the literature . The recombination frequency calculations from the replicon study indicated that no recombination hotspots are present in the HCV genome [25] . This is in agreement with our findings that productive HCV recombination in the infectious cell culture system is not restricted to certain regions of the genome . However , several cases of recombination between two nearly identical 12 nt stretches in NS5B indicated some preference for conserved sequences . Interestingly , the experimental setup in the replicon study did not allow recombination to occur at this potential hotspot [25] . Recombination site specificity remains to be fully investigated in the absence of constraints using identical HCV isolates covering the entire genome . It could be speculated that some restrictions on recombination sites could apply at least to non-homologous recombination . Interestingly , all recombination sites identified in this study fall in regions where recombination of natural strains was also described [10] . All recombination events identified in the present study led to joining of viral RNA fragments . While insertion of cellular sequences has been reported for several other viruses [4] , [40]–[42] , and is an important regulatory process for cytopathogenicity of the related BVDV [40] , [43] , this has not been reported for HCV in vivo . However , by cell culture transfection of deletion mutants of stem loop I of the HCV 5′UTR , we previously recovered viable genomes that acquired RNA stem loop structures derived from viral or host sequences compensating for the deletion [27] . Now knowing that replication independent recombination is possible for HCV , these variants could have arisen by such a mechanism . Non-homologous recombination could initiate important evolutionary steps in generation of novel types of viral genomes or cause diversity in genome regions tolerating insertions and deletions . Such productive non-homologous recombination events might potentially be followed by another recombination event to get rid of duplicate fitness-lowering sequences . The importance of RNA recombination for the evolution of RNA viruses is well documented [19] , and many recently emerged human diseases are caused by viruses that display active recombination or reassortment [1] , [5] . The presence of reverse transcriptase could even fix such sequences in the cellular genome [44] . Thus , RNA recombination could have played an important role in cellular and viral genetic evolution . The prevalence of HCV recombinants in patients is relatively low , which could in part be caused by the super-infection exclusion principle [45] , [46] , which would reduce the chance of having two different HCV strains replicating in the same cell . In vivo , the amount of replicating RNA is further expected to be much lower than the amounts of RNA present after co-transfection in vitro . Thus , the recombination frequency reported here could well be overrepresented compared to the in vivo setting . Further , fitness of novel recombinants in vivo should be high for the recombinant to eventually dominate over the parental strains . In a treatment setting this might however be accomplished , e . g . if parental genomes each carried resistance to one of two antiviral compounds in a combination therapy , with recombination leading to a double-resistant recombinant genome . Subgenomic deletion mutants [14] , [15] are naturally occurring in patients and are similar in structure to the JFH1ΔE1E2 construct used in this study . These could therefore constitute a reservoir of independent genomes that could potentially recombine with the wild-type to generate treatment-resistant or otherwise high-fitness genomes . With an increased knowledge on HCV recombination , better diagnosis of clinically important recombinants could become available , thereby facilitating selection of optimal therapeutic regimens for the patients . Our findings shed new light on how HCV recombination could occur in patients . Further , viral recombination might be an important escape mechanism to specific antiviral therapy in general , which could be important to consider in design of treatment regimens for certain viruses .
The HCV plasmids pJFH1ΔE1E2 [29] , pJ6/JFH1 [28] , pJ6/JFH1-GND [28] , pJ6CF [47] , pH77C [48] , pHC-TN [49] , pJ4L6S [50] , pS52 [26] and pED43 [26] were previously described . Introduction of single mutations and construction of pJFH1Δ5′ , pJ6/JFH1Δ5′ , pJ61–7666 , pJ6/JFH1Δ ( 5′-p7 ) , pJ6/JFH1Δ ( 5′-NS4A ) , pJ6/JFH1Δ ( 5′-NS4B ) , pJ6/JFH1Δ ( 5′-NS5A ) , pJ6/JFH1/3′X , pJ6/JFH1ΔE1E2 ( Rec#1 ) , pJ6/JFH1 ( Rec#10 ) , pJ6/JFH1 ( Rec#21 ) , pJFH1Δ5′-RlucΔ40 and pJ6/JFH1-GND-RlucΔ40 was done using standard fusion PCR and cloning methods . The J6/JFH1/3′X mutant contained a fragment of human cAMP-dependent protein kinase mRNA , replacing the HCV 3′X region . The complete HCV sequence of final plasmid preparations was confirmed . Culturing of Huh7 . 5 hepatoma cells was done as previously described [51] . One day before transfection or infection , 4×105 cells per well were plated in six-well plates . Before RNA transcription , plasmids were linearized with XbaI to generate the HCV 3′-end . To produce Δ3′ transcripts ( Figure 1 ) , linearization was done using EcoRV ( nt 7764 ) for pJ6CF , NotI ( nt 9221 ) for pH77C and pHC-TN , AflII ( nt 9399 ) for pJ4L6S , NotI ( nt 8549 ) for pS52 and KpnI ( nt 9014 ) for pED43 . Shorter truncated versions of pJ6CF were generated using ClaI ( nt 709 ) , BsiWI ( nt 1345 ) , SalI ( nt 2408 ) , BsaBI ( nt 2565 ) , NdeI ( nt 2973 ) or AleI ( nt 3480 ) . In addition , digestion with XbaI was performed to avoid influence of minus-strand synthesis from a reverse T7 promoter . In vitro transcription of RNA was performed as previously described [35] . To exclude that recombination during in vitro transcription led to emergence of recombinant viral genomes , each transcript was synthesized separately . For transfections , 1 . 25 µg RNA of each construct ( a total of 2 . 5 µg in co-transfections , as estimated by gel-electrophoresis ) were incubated with 5 µL Lipofectamine2000 ( Invitrogen ) in 500 µL Opti-MEM ( Invitrogen ) for 20 min at room temperature . Cells were incubated with transfection complexes for 16–24 hours in growth medium or in pure Opti-MEM . For infection experiments , cells were inoculated with virus-containing supernatant for 16–24 hours . Cell cultures were split every 2–3 days and monitored by immunostaining using mouse anti-HCV-Core-protein monoclonal antibody ( B2 , Anogen ) or anti-NS5A 9E10 [28] as previously described [35] , [51] . Supernatants collected during experiments were sterile-filtered and stored at −80°C . HCV RNA titers were determined as previously described [51] . Infectivity titers were determined by adding 100 µL of triplicate sample dilutions ( diluted 1∶2 or more ) to 6×103 Huh7 . 5 cells/well plated 24 hours before infection on poly-D-lysine-coated 96-well plates ( Nunc ) . Cells were fixed 48 hours post-infection and immunostained for HCV following a previously established protocol [51] using anti-NS5A 9E10 as primary antibody [28] . FFU were defined by clusters of infected cells separated by at least two uninfected cells . The number of FFU was determined by manual counting or by using an automated counter ( ImmunoSpot Series 5 UV Analyzer , CTL Europe GmbH ) with customized software , as previously described [26] , [52] . To analyze recombination frequency , cells were split 18 hours after transfection and 7000 cells were plated per well in 96-well format . Starting day 5 , cells were split every 2–3 days using split ratios adjusted to ensure >50% confluency in all wells throughout the experiment . At each split a replica 96-well plate was plated and incubated for 2–3 days before staining as described above for infectivity titration . In this experiment , single infected cells were counted using the ImmunoSpot Series 5 UV Analyzer . For luciferase assays , RNA was transfected into 105 Huh7 . 5 cells/well of 24-well plates . At indicated time points , cells were lysed for 15 min according to the Renilla Luciferase Assay System ( Promega ) protocol , and luciferase signals were measured in 5 replicates using optical bottom 96 well plates on a FluoStarOptima ( BMG ) plate reader . HCV RNA was extracted from culture supernatant using High pure viral nucleic acid kit ( Roche ) . For direct sequencing of the complete HCV ORF , reverse transcription , 1st round PCR covering the entire ORF and 12 overlapping 2nd round PCR amplifications were performed as previously described for J6/JFH1 [51] . For J4L6S and S52 intergenotypic recombination events , primers designed for the corresponding JFH1-based recombinants were used [34] , [51] . Non-homologous recombination events that resulted in duplicated primer binding sites for the 2nd round PCR could not be identified using the direct sequencing approach . In these cases , additional 2nd round PCRs were set up with forward primers downstream of reverse primers ( inverted primer sets ) , to specifically amplify the region containing a non-homologous recombination breakpoint with duplicated sequence . For supernatants originating from co-transfection of different HCV isolates , the initial 12 amplicon ORF direct sequencing was used to determine in which region such primers should be designed . For supernatants originating from co-transfection of RNA from the same HCV isolate , a scanning approach was used , in which inverted primer pairs placed for each ∼500 nts were tested positive or negative by PCR . Amplified PCR bands were sequenced to identify breakpoints of non-homologous recombination . In selected cases , and in cases where the recombination site could not be uniquely identified by direct sequencing , PCR products were TOPO-cloned ( Invitrogen ) and sequenced . The occurrence of sequential recombination events for Rec#1 over time was monitored by PCR using primers JF1848 ( CTGTGTGTGGCCCAGTGTAC ) and 2763R_J6 ( AGCGTGAGCCCTGACGAAGTACGG ) on cDNA . The amplified product sizes varied according to the recombinant and thereby allowed differentiation . Sequence analysis was performed with Sequencher ( Gene Codes Corp . ) . As a control for correct identification of recombinant junctions , independent RNA extraction and RT-PCR were done on supernatant of selected recombinants ( Rec#1 , Rec#17 and all four cultures leading to Rec#21 ) . This confirmed the identified breakpoints . It was further confirmed that recombinant-specific PCR products could be amplified from supernatant-derived cDNA and from cloned recombinant plasmids but not from pJ6/JFH1 using inverted primer sets JF2845 ( CACCCCCGGGTATAAGACC ) /2111R_JFH1 ( TGTACGTCCACGATGTTCTGGTG ) ( Rec#1 ) or JF1848 and the junction-specific reverse primer Rec10_R ( CGTGCACAGGTGCGTCATAGGCTCCTATCTGGCCATGCACAG ) ( Rec#10 ) . To exclude in vitro introduced recombination during T7-driven transcription or during reverse transcription after RNA extraction from supernatant , RNA produced by T7 transcription was subjected to 3 sequential rounds of DNAseI ( Fermentas ) digestion using the RNeasy kit ( Qiagen ) , mixed to yield combinations of 5′ and 3′ partners that previously led to successful recombination in cell culture , diluted to 50 pg ( equivalent to around 107 copies ) and subjected to RT-PCR . PCR amplification using inverted primer sets JF2845/2111R_JFH1 on J6CF and JFH1ΔE1E2 RNA ( Rec#1 ) , JF1848 and the junction-specific reverse primer Rec10_R on J6CF and JFH1ΔE1E2 RNA ( Rec#10 ) , or inverted primer sets JF8806 ( CAGATACTACCTGACCAGAGAC ) /JR8688 ( TCCGTGAAGGCTCTCAGGTTC ) on J6/JFH1/3′X and J6/JFH1 ( Δ5′-NS5A ) RNA ( Rec#21 ) , did not lead to the specific amplicons that were observed for the cloned plasmids of the respective recombinants . Further , the viable phenotypes of all cloned recombinants and the fact that all recombination events identified led to in-frame recombinant ORFs supported that RT-PCR-induced artifacts were not misleading our conclusions . | Genetic recombination is the alternative joining of nucleic acids leading to novel combinations of genetic information . While DNA recombination in cells is of importance for evolution and adaptive immunity , RNA recombination often has only transient effects . However , RNA viruses are rapidly evolving and recombination can be an important evolutionary step in addition to mutations introduced by the viral polymerase . Recombination can allow escape from the host immune system and from antiviral treatment , and recombination of live attenuated viral vaccines has led to re-emergence of disease . Hepatitis C virus ( HCV ) is an important human pathogen that chronically infects more than 130 million worldwide and leads to serious liver disease . For HCV , naturally occurring recombinants are rare but clinically important . HCV recombination constitutes a challenge to antiviral treatment and can potentially provide an escape mechanism for the virus . In this study , we established an assay for HCV RNA recombination and characterized the emerging homologous and non-homologous recombinant viruses . Interestingly , recombination did not depend on viral replication , occurred most efficiently between isolates of the same genotype and did not occur with strong site-specificity . Better diagnosis of clinically important recombinants and an increased knowledge on viral recombination could strengthen antiviral and vaccine development . | [
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] | 2013 | Productive Homologous and Non-homologous Recombination of Hepatitis C Virus in Cell Culture |
The need for economical rabies post-exposure prophylaxis ( PEP ) is increasing in developing countries . Implementation of the two currently approved economical intradermal ( ID ) vaccine regimens is restricted due to confusion over different vaccines , regimens and dosages , lack of confidence in intradermal technique , and pharmaceutical regulations . We therefore compared a simplified 4-site economical PEP regimen with standard methods . Two hundred and fifty-four volunteers were randomly allocated to a single blind controlled trial . Each received purified vero cell rabies vaccine by one of four PEP regimens: the currently accepted 2-site ID; the 8-site regimen using 0 . 05 ml per ID site; a new 4-site ID regimen ( on day 0 , approximately 0 . 1 ml at 4 ID sites , using the whole 0 . 5 ml ampoule of vaccine; on day 7 , 0 . 1 ml ID at 2 sites and at one site on days 28 and 90 ) ; or the standard 5-dose intramuscular regimen . All ID regimens required the same total amount of vaccine , 60% less than the intramuscular method . Neutralising antibody responses were measured five times over a year in 229 people , for whom complete data were available . All ID regimens showed similar immunogenicity . The intramuscular regimen gave the lowest geometric mean antibody titres . Using the rapid fluorescent focus inhibition test , some sera had unexpectedly high antibody levels that were not attributable to previous vaccination . The results were confirmed using the fluorescent antibody virus neutralisation method . This 4-site PEP regimen proved as immunogenic as current regimens , and has the advantages of requiring fewer clinic visits , being more practicable , and having a wider margin of safety , especially in inexperienced hands , than the 2-site regimen . It is more convenient than the 8-site method , and can be used economically with vaccines formulated in 1 . 0 or 0 . 5 ml ampoules . The 4-site regimen now meets all requirements of immunogenicity for PEP and can be introduced without further studies . Controlled-Trials . com ISRCTN 30087513
Rabies is a neglected disease affecting particularly tropical developing countries [1] . Estimates of the Global use of rabies post-exposure prophylaxis ( PEP ) are rising . In China , it was 8 million in 2005 [2] , yet rabies currently kills more people than any other infectious disease there . Rabies deaths are underreported and misdiagnosed , for example as cerebral malaria [3] . As the obsolete nervous tissue-based rabies vaccines are replaced by expensive tissue culture vaccines , there is increasing need to reduce the cost of post-exposure prophylaxis . In Africa , the average cost of a standard intramuscular ( IM ) course of vaccine is $39 . 6 , equivalent to 50 days wages [1] . There is a shortage of affordable rabies vaccines of reliable quality in the developing world [4] . Economical PEP regimens employ multiple site intradermal ( ID ) injections , saving 60% of the vaccine used in the standard IM method ( Table 1 ) . Increasing the number of sites of injection is designed to stimulate several different groups of lymph nodes to initiate antibody production . Two economical regimens are now recommended [5] , an 8-site [6] and a 2-site [7] method ( Table 1 ) . The urgency of PEP demands a rapid induction of neutralising antibody using minimal amounts of vaccine in all recipients including the ∼3% of the population who are ‘low responders’ [8] and the many others whose immune response is impaired [9]–[12] . The original IM PEP vaccine regimen is the most widely used globally . In Asia only 3% of tissue culture rabies vaccine treatments use economical ID regimens [1] , and they are rarely used in Africa . The reasons are misgivings about reducing the vaccine dosage in prevention of a fatal disease [13] , confusion over regimens , and the competence of staff giving ID inoculation . Economical regimens require sharing of ampoules between patients , but rabies vaccines have no added preservative and so the reconstituted ampoule of vaccine should be used within a day . The use of economical regimens is therefore mainly confined to large treatment centres , yet 90% of rabies deaths occur in rural areas [4] . Evidence to date indicates that the 8-site regimen is more immunogenic than the 2-site regimen [14] , [15] . However the 8-site method is not economical when used with one of the two major vaccines , purified vero cell rabies vaccine ( PVRV ) ( Verorab™ Sanofi Pasteur ) , because this vaccine is relatively concentrated: an IM dose is 0 . 5 ml , in contrast to the equivalent 1 ml dose of the other widely used vaccine , purified chick embryo cell vaccine ( PCECV ) ( Rabipur™; Novartis ) [15] . Although the 8-site regimen has some advantages and was recommended by some authorities for use when rabies immunoglobulin ( RIG ) was not available [15] , the 2-site regimen is more acceptable and convenient . The total dose of vaccine should be the same with the two regimens . The only difference between the two schedules is that with the 8-site a large dose of vaccine is given on the first day , whereas with the 2-site regimen this is divided between days 0 and 3 , entailing an extra treatment visit [14] . Ambrozaitis et al . [16] demonstrated that the 4-site regimen was apparently immunogenic with both PVRV and PCEC vaccines , but there was no comparison with any current PEP method and historical controls are unreliable . For all these reasons , a single , simple , acceptable , immunogenic and economical PEP regimen is needed , suitable for use with all vaccines fulfilling WHO requirements . We tested a 4-site PEP regimen which allows the 8-site regimen principle to be used economically with PVRV . We also investigated whether injecting the same amount of vaccine between 4 instead of 8 sites affected immunogenicity . The new 4-site regimen and the currently used ID regimens were compared with the standard IM method in a single blind , randomised , controlled trial .
Healthy volunteers were recruited in Oxford and Bristol UK , between June 2002 and April 2005 . The exclusion criteria were: previous rabies vaccine treatment; pregnancy; a recent blood transfusion; taking immunosuppressive drugs; receiving another killed vaccine or chloroquine treatment [17] within 2 weeks , or any live virus vaccine within 3 weeks of a rabies vaccine dose . The Oxfordshire Clinical Research Ethics Committee approved the project ( ref . C01 . 078 ) , conducted in accordance with GCP regulations ( EU Directive 2001/20/EC ) . Each participant was allocated to one of four rabies PEP regimens according to a computer generated list with fixed blocks of 12 . Group A received the 4-site regimen; group B , the 8-site regimen; group D , the 2-site regimen and group E , the IM regimen . Allocations were concealed in opaque serially numbered sealed envelopes , opened once written informed consent had been obtained . All laboratory staff were blinded to the treatment allocation . The vaccine used was PVRV , ( Verorab™ Sanofi Pasteur ) Lot no XO291-1 potency 5 . 3 IU/dose in 165 subjects , and Lot no . U0271 potency 8 . 4 IU/dose in 64 subjects . The Medicines and Healthcare products Regulatory Agency granted exemption from a licence . The 2-site and IM regimens were according to standard methods ( Table 1 ) [15] . For the 4-site regimen , on day 0 the entire contents of the 0 . 5 ml PVRV vial are injected ID , divided between 4 sites over the deltoids and thighs ( approximately 0 . 1 ml per site ) . On day 7 , 0 . 1 ml is injected ID at 2 sites ( deltoids ) . Single site injections are given on days 28 and 90 . The 8-site regimen is the exact equivalent of the current 8-site method [6] , [15] , using a vaccine containing 0 . 5 ml/ampoule . The entire contents of the vial are divided between 8 ID sites on day 0: ( deltoids , thighs , suprascapular , lower anterior abdominal wall ) . The dose per site is approximately 0 . 05 ml . All the ID regimens use the same total amount of vaccine . There is a little inevitable wastage in syringes . Opened ampoules were refrigerated and used or discarded within 8 hours . See Table 1 for the timing , doses , routes and sites of inoculation of all the regimens . Blood samples were taken at days 0 , 7 , 14 , 90 and 1 year . Serum aliquots were coded , stored at −70°C and assayed blind . Neutralising antibody levels were measured by an adaptation of the rapid fluorescent focus inhibition test ( RFFIT ) for 96 well plates [18] , [19] , at the Institut Pasteur , Paris . Briefly , a constant dose of challenge virus standard ( CVS ) is incubated with diluted test sera . An in-house reference serum ( SHR2 31/03/06 = 22 IU/ml ) , is calibrated against an international standard ( RAI = 30 IU/ml ) . Serum/virus mixtures are incubated , and BSR cells ( a clone of BHK-21 cells ) were added . After 24 hours incubation , the monolayer is acetone-fixed and stained with a fluorescent anti-nucleocapsid antibody ( Chemicon ) . The result in IU/ml was the mean of independent duplicate tests . Selected sera were also assessed using the fluorescent antibody virus neutralisation ( FAVN ) assay at the Veterinary Laboratories Agency , Weybridge [20] , [21] . This test is the same in principle as the RFFIT , using the same challenge virus . The FAVN and RFFIT vary in that they use a different dilution series ( 3 fold versus 5 fold ) ; the FAVN runs samples in quadruplicate; BHK-21 cells ( ATCC , USA ) are used , and the internal serum standard is the WHO human positive control ( NIBSC , UK ) . The antibody titre is based on 100% virus neutralisation for the FAVN and 50% reduction of fluorescent foci in the RFFIT . Protocol deviations were not permitted on days 7 and 14 , but flexibility was allowed if necessary: on day 28±1 day; on day 90 - 7 to +10 days , and at one year −2 weeks to +4 weeks . All records were kept in strict confidence . Volunteers kept a health record diary for a week after each vaccine dose . The aim was to demonstrate that the 4-site test regimen was at least as immunogenic as the standard regimens . The primary outcome is the proportion of participants reaching the WHO criterion for post-exposure regimens: a minimum neutralising antibody level of 0 . 5 IU/ml by day 14 . The failure rate for the current regimen in meeting this threshold is less than one in 1000 . At this rate , the expected number of failures in the control group is likely to be zero . The sample size calculation was based on the assumption that the new regimen was just as effective ( i . e . rate of less than 1 in 1000 ) and was computed by simulation method using exact methods for estimating the confidence interval ( CI ) for the difference . The initial protocol envisaged the recruitment of 75 participants per group to make 5 comparisons over 7 regimens expecting zero events to be observed , giving 90% power to show that the difference in failure rates was at most 6 . 2% ( adjusting for pre-planned multiple comparisons ) . Because the trial failed to recruit at an adequate rate , the revised sample size of 55 participants per group ( Protocol S1 ) was calculated for a total of 6 comparisons among four groups giving 90% power to show that the difference in failure rates was at most 9% by day 14 . Proportions and 95% CI for the difference in proportions were calculated using the method based on Wilson's score [22] . Agreement between the results of the two antibody tests was assessed by the Bland-Altman method [23] . Titre concentrations were log transformed and groups were compared using analysis of variance . Results were deemed statistically significant at P<0 . 05 . Fisher's exact test was used to compare side effects between groups . Post-hoc pairwise comparisons were also carried out on any local reactions ( redness , swelling hardness , or tenderness/pain ) and on any local or generalised signs or symptoms . P-values were adjusted if multiple comparisons were performed .
Two hundred and fifty four subjects were recruited . Data from 229 were complete up to day 90 , and used in the final analysis . Twenty five were excluded , usually because they failed to keep appointments ( for details see Figure 1 ) . Three sera taken on day 14 were lost during storage . Subjects were aged between 18 and 50 years . Ages and sex ratios were similar between the groups ( Table 2 ) . One person withdrew within the first week because of transient arthralgia , possibly related to the vaccine . Local reactions to the vaccine observed by 229 volunteers are shown in Table 3 . Redness ( erythema ) , swelling ( inflammation ) and hardness ( induration ) were more frequent in all ID groups than in the IM group ( E ) ( P<0 . 0001 ) . The incidence of local tenderness or pain was similar in all groups . Itchiness and local lymphadenopathy ( tenderness at regional lymph nodes ) was not solicited , but was volunteered more often in the ID groups ( P<0 . 0001 ) . Volunteers were asked to report any generalised symptoms , whether or not listed in their reaction diary . Some were attributable to causes unrelated to vaccination . The incidence of each of the generalised symptoms was lower with the IM regimen but this only reached significance when compared with all three ID groups together ( P<0 . 001 ) ( Table 4 ) . The lower limit of detection of antibody was 0 . 06 IU/ml , while the threshold for a positive result , was 0 . 3 IU/ml , as naïve sera can range between 0 and 0 . 3 IU/ml . Two sera gave pre-vaccination results above this threshold ( the means of two tests were 0 . 38 IU/ml and 0 . 46 IU/ml ) . These subjects denied previous rabies immunisation and subsequent titres did not suggest a secondary immune response , but they were excluded from the analysis . Data for the remaining 227 people were analysed . Undetectable titres were assigned the value of 0 . 02 . Geometric mean titres ( GMTs ) on day 7 for the 4 treatment groups ( Table 5 , Figure 2 ) , showed that group E ( IM ) had a lower GMT than group D ( 2-site ) ( P<0 . 001 ) and group B ( 8-site ) ( P = 0 . 01 ) . Group A ( 4-site ) was lower than group D ( P = 0 . 01 ) . The percentage of people with detectable antibody >0 . 3 IU/ml was 60% , 77 . 6% , 86 . 2% and 62 . 5% for groups A , B , D and E respectively . The day 7 results were no different with the two batches of vaccine ( data not shown ) . On day 14 all subjects had antibody levels >0 . 5 IU/ml ( Table 5 , Figure 2 ) . The 95% confidence intervals for the differences in proportions between any two regimens indicated that differences could at most be between 6% and 7% ( Figure 3 ) . The only significant difference between the GMTs is that Group E ( IM ) was lower than group D ( 2-site ) ( P = 0 . 04 ) . On day 90 , GMTs were similar ( Table 5 , Figure 2 ) . At 1 year all ID recipients had detectable antibody , but two people in group E ( IM ) had <0 . 3 IU/ml . Eight had levels between 0 . 3 and 0 . 5 IU/ml: 2 in group A , 2 in group B and 6 in group E . All the ID regimens induced more persistent antibody than the IM group ( P<0 . 001 for groups B and D , P<0 . 02 for A ) . The 2-site ( D ) GMT was greater than the 4-site ( A ) ( P<0 . 04 ) . Before the serological data were decoded , some unusual results were identified . Antibody levels were unexpectedly high on days 7 and 14 , compared with other clinical trials [6] , [7] , [16] . Two subjects were excluded because they had pre-vaccination antibody levels above the 0 . 3 IU/ml threshold . Two people had antibody levels >3000 IU/ml on day 14 ( both of them later proved to be in group D ) . The next highest were seven subjects with levels between 1000 and 1500 IU/ml . The other results for these people were well within the range of the rest . After decoding , none of the high results was found to be among group A subjects . They were individual high titres , without any suggestion of an anemnestic response . To confirm the results of the trial , 224 ( of the original 229 ) day 7 samples available , and a few others ( see below ) were tested blind in another laboratory which uses the FAVN method . The FAVN lower limit of detection of antibody was 0 . 05 IU/ml . The threshold for a positive result is >0 . 13 as naïve sera can range up to 0 . 1 IU/ml . The day 7 results for 224 subjects , including the 2 excluded because of high initial RFFIT titres , showed GMTs between 1 . 044 IU/ml for group D ( 2-site ) and 0 . 573 IU/ml for E ( IM ) ( P<0 . 01 ) ( Table 6 , Figure 4 ) . There were no other significant differences and GMTs were in the same order as the RFFIT results for all 4 treatment groups . The percentage of people with detectable antibody , >0 . 13 IU/ml , was 96 . 3 % , 93 . 0% , 96 . 6 % and 87 . 3 % for groups A , B , D and E respectively . This comparison showed general consistency but considerable individual variation , as demonstrated graphically in a Bland-Altman plot ( Figure 5 ) . Further analysis was not appropriate in such a small sample . All the FAVN results were <6 IU/ml , except one of 13 . 5 IU/ml ( the RFFIT result was 2 . 8 IU/ml ) . For the RFFIT , all titres were <7 IU/ml , except two of 8 . 39 and 9 . 36 IU/ml ( the FAVN results were 1 . 14 and 3 . 42 IU/ml respectively ) . The day 0 sera with RFFIT results >0 . 3 IU/ml , and day 14 sera with RFFIT results >3000 IU/ml , were included in a group of 38 otherwise randomly selected sera to be tested by the FAVN method . The day 0 results of 0 . 38 and 0 . 46 IU/ml were both 0 . 06 IU/ml by the FAVN . RFFIT results of 3711 . 5 and 3021 . 5 IU/ml were 53 . 3 and 121 . 2 IU/ml respectively by the FAVN .
This study demonstrates that ID rabies vaccination is at least as immunogenic as the standard IM regimen and induces greater persistent immunity . ID regimens are therefore recommended anywhere in the world where the cost of PEP is critical . All three ID regimens required the same total amount of vaccine and proved equally immunogenic , but the 4-site ID regimen has several key advantages . First , the 4-site needs one less clinic visit ( omitting day 3 ) . WHO now recommends omitting the day 90 dose of ID regimens , and doubling the day 28 dose [24] , [25] , [26] . The 4-site regimen would then require only 3 visits ( days 0 , 7 and 28 ) the same as the current 3 dose IM pre-exposure regimen , but using only about half the amount of vaccine . Secondly , the 4-site regimen is safer than the 2-site as it uses a whole ampoule of vaccine divided between intradermal sites on the crucial first day . If some vaccine were inadvertently injected subcutaneously , the wide margin of safety would ensure an adequate immune response [27] . Thirdly , sharing of ampoules of vaccine between patients is only necessary on days 7 and 28 . The 4-site regimen can therefore be started in a rural clinic with referral a week later . It is economical anywhere if two or more people are treated on the same day . The 4-site regimen can be used economically with current vaccines formulated in 0 . 5 and 1 . 0 ml ampoule sizes . Our results show that there is no need to divide the initial dose between 8 sites , because it was equally immunogenic in 4 sites . We injected over the deltoid and thigh areas , whereas Ambrozaitis et al . [16] used deltoid and suprascapular sites . The choice might be important in cultures where there is reluctance to expose the thighs . The efficacy of the 8-site regimen has been demonstrated in patients bitten by proved rabid animals , with and without concomitant RIG [6] . Since the 4-site method has the same timing of doses and amount of vaccine , and is equally immunogenic , it can be inferred that RIG treatment would not be significantly immunosuppressive . All authorities recommend the combination of RIG with vaccine for PEP , especially for high risk exposure to rabies . Treatment failures are inevitable in severe cases ( bites on the head , neck or hands or multiple bites ) if vaccine is given alone . However RIG is not generally available or affordable in developing countries where it is given to <1% of PEP patients for whom it is recommended [28] . The 4-site regimen fulfils WHO requirements for immunogenicity for PEP and so could be introduced without further studies . WHO recommendations have changed since 1997 , when the difference in dilution was recognised [15] , to the latest rule that an ID dose of either vaccine is 0 . 1 ml [5] , [26] . Other studies of 8-site PVRV have used 0 . 1 ml per ID site [29] , as recommended by WHO [5] , which almost doubles the amount of vaccine used . The results for the 2-site regimen we report here apply to PVRV , the equivalent dose for PCECV would be 0 . 2 ml per site . Ambrozaitis et al . [16] have tested this 4-site regimen to compare different doses of vaccine . Using PCECV , which is formulated in 1 ml ampoule , they showed that 0 . 1 ml per ID site , a lower dose , was as immunogenic as 0 . 1 ml per ID site of PVRV . This confirms the safety of our 4-site method , in which 0 . 25 ml of PCECV would be injected at each ID site on day 0 , and 0 . 2 ml per site subsequently . Using the lower dose of 0 . 1 ml per site would sacrifice the advantages of using a whole ampoule on the first day , but would be more economical in large treatment centres [13] . The FAVN and the RFFIT tests are identical in principle but differ in the way their results are read . A comparison between these tests , performed within the same laboratory , showed close correlation [20] , but there has been no report of inter-laboratory comparisons . Our data were too few for substantial analysis . In this study , at least one unusually high level was seen with one test , but not confirmed by the other . These results were used in the analysis but did not affect the overall findings or conclusion . Similarly high individual results have been reported previously , but not explained [30] , [31] , [32] . Rabies immunisation is expensive and unusual in the UK . Thorough investigations excluded previous immunisation in the group analysed and so the high titres cannot be dismissed as an anamnestic response . Antibody GMTs on days 7 and 14 were much higher , both by the RFFIT and FAVN than in some other recent studies [16] , [30] . Over 30 years , no difference has been reported in serological responses to tissue culture rabies vaccines between people in America , Europe and Asia . The higher levels found here remain unexplained . In a 2-site ID vaccine trial in Thailand , antibody levels varied 2 . 2 fold between different hospitals [30] . Economical rabies PEP regimens using 2 , 4 or 8 initial ID sites are as immunogenic as the standard IM regimen , but they use 60% less vaccine . The 4-site regimen has several practical advantages over both currently used regimens , and is the most economical since only 3 or 4 clinic visits are needed ( on days 0 , 7 and 28 with optional day 90 ) . Our finding that ID regimens were at least as immunogenic as the “gold standard” 5 dose IM regimen should increase confidence in multiple-site ID techniques . The 4-site regimen is suitable for use anywhere in the world where there are financial constraints , and especially where 2 or more patients are likely to be treated on the same day . | All human deaths from rabies result from failure to give adequate prophylaxis . After a rabid animal bite , immediate wound cleaning , rabies vaccine and immunoglobulin injections effectively prevent fatal infection . Immunoglobulin is very rarely available in developing countries , where prevention relies on efficacious vaccine . WHO approved vaccines are prohibitively expensive , but 2 economical regimens ( injecting small amounts of vaccine intradermally , into the skin , at 2 or 8 sites on the first day of the course ) have been used for many years in a few places . Practical or perceived difficulties have restricted widespread uptake of economical methods . These could largely be overcome by introducing a new , simpler regimen , involving 4 site injections on the first day . We vaccinated volunteers to compare the antibody levels induced by the 4-site intradermal regimen with those induced by the current 2-site and 8-site regimens and the “gold standard” intramuscular regimen favoured internationally . All the economical intradermal regimens were at least as immunogenic as the intramuscular method . The results provide sufficient evidence that the 4-site regimen meets the criteria necessary for its recommendation for use wherever the cost of vaccine is prohibitive and especially where 2 or more patients are treated on the same day . | [
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] | 2008 | A Simplified 4-Site Economical Intradermal Post-Exposure Rabies Vaccine Regimen: A Randomised Controlled Comparison with Standard Methods |
Dengue fever , caused by the dengue virus ( DENV ) , is now the most common arbovirus transmitted disease globally . One novel approach to control DENV is to use the endosymbiotic bacterium , Wolbachia pipientis , to limit DENV replication inside the primary mosquito vector , Aedes aegypti . Wolbachia that is naturally present in a range of insects reduces the capacity for viruses , bacteria , parasites and fungi to replicate inside insects . Wolbachia’s mode of action is not well understood but may involve components of immune activation or competition with pathogens for limited host resources . The strength of Wolbachia-based anti DENV effects appear to correlate with bacterial density in the whole insect and in cell culture . Here we aimed to determine whether particular tissues , especially those with high Wolbachia densities or immune activity , play a greater role in mediating the anti DENV effect . Ae . aegypti mosquito lines with and without Wolbachia ( Wildtype ) were orally fed DENV 3 and their viral loads subsequently measured over two time points post infection in the midgut , head , salivary glands , Malpighian tubules , fat body and carcass . We did not find correlations between Wolbachia densities and DENV loads in any tissue , nor with DENV loads in salivary glands , the endpoint of infection . This is in contrast with strong positive correlations between DENV loads in a range of tissues and salivary gland loads for Wildtype mosquitoes . Lastly , there was no evidence of a heightened role for tissues with known immune function including the fat body and the Malpighian tubules in Wolbachia’s limitation of DENV . We conclude that the efficacy of DENV blocking in Wolbachia infected mosquitoes is not reliant on any particular tissue . This work therefore suggests that the mechanism of Wolbachia-based antiviral effects is either systemic or acts locally via processes that are fundamental to diverse cell types . We further conclude that the relationship between DENV blocking and Wolbachia density is not linear in mosquito tissues
Dengue fever , caused by the dengue virus ( DENV ) , is the most prevalent arthropod transmitted virus , endemic in over 100 countries [1 , 2] . The virus is comprised of four antigenically distinct serotypes ( 1–4 ) [3 , 4] . DENV is transmitted by Aedes aegypti and Ae . albopictus with the former being the principal vector [5] . With no specific antiviral drugs , management of the disease has mainly relied on relieving the associated symptoms of fever , headache and rash [6] . As the current tetravalent dengue vaccine offers incomplete protection [7] , vector control remains the primary means of reducing disease prevalence . One example of an emerging vector control strategy involves the use of a bacterial endosymbiont , Wolbachia pipentis that is naturally present in 40% of arthropods [8] and 28% of mosquito species , including Ae . albopictus , and Ae . notoscriptus . Interestingly , Ae . aegypti is not naturally infected with the symbiont [9] . Over the last decade , three different Wolbachia strains have been transinfected into Ae . aegypti where they form stable , inherited infections including; wMelPop-CLA and wMel , both from Drosophila melanogaster , wAlbB from Ae . Albopictus and wMelwAlbB , which is a superinfection from both host donors [10–13] . In these mosquito vectors , Wolbachia demonstrates an ability to limit or “block” the success of infection by viruses , nematodes and parasites [14–16] . This effect forms the basis of Wolbachia-based biocontrol trials to interrupt disease transmission in the human population via the vector [17] . The most advanced of such trials are focused on DENV control where the wMel strain has been released into wild Ae . aegypti mosquitoes and successfully spread [18] . Despite widespread field-testing , the mechanistic basis of Wolbachia-DENV blocking is poorly understood . Pathogen blocking has been partly attributed to the ability of the bacterium to increase the basal immune activity of the host thereby enabling it to resist subsequent DENV infection in a process known as ‘immune priming’ [19–21] . Wolbachia-DENV inhibition may also be as a result of competition between the symbiont and viruses for vital host nutrients such as cholesterol , as demonstrated in Drosophila [22] . Such competition may be expected given that the Wolbachia genome lacks a range of key genes in lipid biosynthesis pathways [23] and because viruses are heavily reliant on host cholesterol for replication [24 , 25] . Neither immune priming nor cholesterol competition however , can completely explain Wolbachia-DENV blocking . The strength of blocking appears to correlate with Wolbachia density , whereby higher densities of the symbiont are associated with greater viral inhibition [11 , 26–28] . In mosquito cell lines , only highly infected cells show almost complete DENV inhibition [27 , 28] . The same relationship has been documented in other insects . In Drosophila simulans , the wMel , wAu and wRi strains grow to high densities and provide protection against Drosophila C virus ( DCV ) . In contrast , the wHa and wNo strains that grow to very low densities show little blocking [29] . The fact that wAlbB is unable to block DENV in its natural host Ae . albopictus has also been attributed to low symbiont numbers . In Ae . aegypti where wAlbB has been introduced and hence grows to higher densities , DENV blocking is much stronger [28] . The correlation is further shown in Ae . aegypti by the disparity in blocking between the virulent wMelPop-CLA strain , which grows to very high densities compared to the wMel strain which grows to moderate densities [11] . Several studies have reported that Wolbachia is found at different densities in various tissues of the mosquito body , with the ovaries and Malpighian tubules tending to have high densities [14 , 21 , 28 , 30 , 31] . Osborne et al . , [32] have suggested that Wolbachia density within the head , gut and Malpighian tubules correlated with the ability to mediate protection against DCV in D . simulans . These different tissues may be of varying importance for pathogen blocking as predicted by their Wolbachia densities or if they play a particular functional role in Wolbachia-based pathogen blocking . For example , the fat body is mainly involved in pathogen defence [33 , 34] and the Malpighian tubules , that happen to have very high Wolbachia densities now appear to have immune function [35] . It is unknown if there is a correlation between the Wolbachia encountered by DENV in these tissues and the subsequent progression of infection to the salivary glands as the endpoint of transmission . When a mosquito takes a viremic blood meal , the virus first infects the midgut and then it disseminates to other tissues such as the Malpighian tubules , fat body , trachea and the salivary glands , where it can be transmitted to a human via the saliva on a subsequent bite [5] . The rate of DENV transmission correlates with the titre of virus in the salivary glands when studied in animal models [36] and mosquito infection rate is also known to correlate with virus infectious dose [37] . Even though several studies have suggested that intermediate mosquito tissues are infected by the virus differentially over time [38–41] , it is not clear if there is a correlation between DENV infection in these tissues and that in the salivary glands . Here we have examined the infectivity and viral load of a DENV serotype 3 strain in the tissues of Wildtype and wMel-infected Ae . aegypti . Specifically we have assessed whether Wolbachia densities predict DENV load in the same tissue and if densities in intermediate tissues predict subsequent DENV loads in the salivary glands . We found that there was a positive correlation between DENV loads in intermediate tissues and salivary glands in Wildtype but not Wolbachia-infected mosquitoes . There was also no correlation between Wolbachia densities and DENV loads in any particular tissue . Together , these findings suggest that no one tissue is particularly important for Wolbachia-based blocking and that Wolbachia may simply be limiting virus at the level of each individual cell , by fundamental processes shared by diverse cell types .
Two mosquito lines were used for this experiment; Wolbachia infected [11] and Wolbachia uninfected Ae . aegypti mosquitoes designated wMel . F and Wildtype [31 , 42] , respectively . The wMel . F mosquito line was collected in 2012 from field release sites in Cairns , Australia [18] while the Wildtype line was collected in 2014 from Babinda , Australia . The Wildtype mosquito line was used within four generations of field collection to limit inbreeding . At every generation , the wMel . F mosquito line was outcrossed with 20% Wildtype males to prevent genetic drift between the two lines . Adult mosquitoes were maintained on 10% sucrose while the larvae were fed TetraMin® fish food ( Melle , Germany ) ad libitum . Mosquitoes were reared under standard conditions of 25°C temperature , 65% relative humidity and photoperiod 12 hours light: dark . The DENV 3 strain used for this experiment was sampled from a patient during an outbreak in Cairns , Australia in 2008/2009 [43] . This strain was selected because it caused one of the largest dengue outbreaks in Australia [43] and because it has been demonstrated to infect both wMel and Wildtype mosquitoes at a high rate [44] . Passage 6 of DENV 3 ( PFU 106 ) was propagated using the protocol by Ye et al . , [45] and stored in single use aliquots of 1mL at -80°C . The virus was mixed with defibrinated sheep’s blood in the ratio 1:1 and fed through a membrane feeder to three to five day old mosquitoes . The mosquitoes were starved for 24 hours prior to oral infection . The wMel . F and Wildtype mosquitoes were both fed simultaneously over a period of three hours [44] . Mosquitoes were then anesthetized on ice and females that did not feed were sorted out and discarded . Engorged mosquitoes were maintained on 10% sucrose at 25°C until they were dissected . The midguts , salivary glands , head , fat body , Malpighian tubules and carcass were dissected from each individual mosquito . These tissues were chosen mainly based on their functional role in DENV infection , dissemination and transmission in the mosquito . DENV first infects and replicates in the midgut before being disseminated to other tissues [5] . The end point of disseminated DENV is the salivary glands from where it is transmitted to the human host through the saliva when the mosquito takes a blood meal [5] . Assessment of DENV dissemination in mosquitoes is commonly done using the head tissue given ease of dissection [21 , 42] . Tissues were dissected on 8 and 14 days post infection ( dpi ) . These time points were chosen to reflect the early stage of infection where DENV would have disseminated from the midgut to other tissues and the late stage of infection where infection would have been well established [44] . Dissections were done in 1X phosphate buffered saline ( PBS ) . Tissues of each individual mosquito were placed in 96-well PCR plates ( VWR LabAdvantage , Australia ) containing 200ul of extraction buffer ( 0 . 01M Trizma base , 0 . 001M EDTA , 0 . 05M NaCl and 2 . 5ul proteinase K ) and 2-mm-diameter glass beads ( Merck KGaA , Darmstadt , Germany ) . Ovaries were separated from the carcass and discarded to ensure that Wolbachia density was not unduly influenced by gravid females . To minimize contamination within mosquito lines the dissecting pins were immersed in 80% ethanol for ~10 seconds between individual mosquitoes and discarded after every 20 individuals . New dissecting pins were used for each line to avoid cross contamination between Wildtype and wMel . F mosquitoes . All tissues were stored at -80°C prior to RNA/DNA co-extraction . The entire experiment was replicated three times . Plates containing dissected tissues were homogenized for 1 min 30 seconds in a mini-Beadbeater ( BioSpec Products , Bartlesville , OK ) . They were then incubated in a thermo cycler ( C1000Tm Thermal cycler , Bio-Rad , California USA ) at 56°C for 5 min , then 98°C for 5 min for the simultaneous extraction of RNA and DNA . The extracted RNA/DNA was stored at -80°C and subsequently used for the quantification of DENV 3 RNA copies and Wolbachia density . Taqman qPCR was used to quantify DENV 3 RNA copies in LightCycler480 ( Roche , Applied Science , Switzerland ) . The RealTime Ready RNA Virus Master ( ©1996–2016 Roche Diagnostics ) was used for concurrent cDNA synthesis and DENV 3 RNA copies quantification following manufacturer’s protocol . Primers for DENV were designed from the 3’UTR region with HEX labelled probes [46] . The following qPCR cycling conditions were used: reverse transcription at 50°C for 10 min , initial denaturation at 95°C for 30s , 45cycles of amplification at 95°C for 5s and 60°C for 30s and a final cooling step at 40°C for 10s . Absolute quantification of DENV 3 RNA copies for individual tissues was extrapolated from a standard curve as previously reported [14] . Taqman multiplex qPCR was used for the quantification of the WD0513 Wolbachia gene [47] in LightCycler480 ( Roche , Applied Science , Switzerland ) . The WD0513 gene was normalised to the mosquito housekeeping gene RPS17 [48 , 49] to account for different tissue sizes . The qPCR cycling conditions used are as follows: An initial incubation at 90°C for 5min followed by 45 cycles of amplification at 95°C for 10s , 60°C for 15s and 72°C for 1s and a final cooling step of 40°C for 10s . Relative quantification of Wolbachia was done using the inbuilt algorithm of LightCycler480 . Tissue infectivity ( proportion infected ) of DENV 3 was analysed using the binary logistic function in a generalized linear model with presence or absence of DENV 3 infection as the response variable and tissue type and time as predicting factors . DENV 3 RNA copies ( DENV load ) in tissues was analysed using the tweedie distribution with log link function in a generalized linear model with DENV load as the response variable and tissue type and time as predicting factors . Wolbachia density in tissues was analysed using the tweedie distribution with log link function in a generalized linear model with Wolbachia density as the response variable and time and tissue as the predictive factors . Models were run separately for the Wildtype and wMel . F mosquito lines . Non-Parametric Spearman correlation co-efficient was used to test for correlation between the following: ( 1 ) DENV loads in intermediate tissues and salivary glands , ( 2 ) Wolbachia density in tissues and DENV load in salivary glands and ( 3 ) DENV load and Wolbachia density in the same tissue . All statistical analyses were performed in SPSS® ( IBM SPSS Statistics for Windows , Version 20 . 0 . Armonk , NY )
To determine if time post infection and tissue type had an effect on DENV 3 infectivity , we examined head , salivary glands , midgut , Malpighian tubules , fat body and carcass at 8 and 14 dpi . There was a significant effect of tissue for both Wildtype ( Wald = 139 . 60; df = 5; p< 0 . 0001 ) and wMel . F ( Wald = 40; df = 5; p< 0 . 0001 ) mosquitoes ( Fig 1 ) . The head was the least infected tissue in both Wildtype and wMel . F mosquitoes , failing to recapitulate patterns of infection in other disseminated tissues including the salivary glands . In a previous study [42] where DENV 3 infection rates in the mosquito head and body were examined , head infection rates were significantly lower than that of the body at 7 dpi in wMel . F mosquitoes . However by 14 dpi in the same study there was no difference between head and body infections in wMel . F mosquitoes . Furthermore , in the Wildtype mosquitoes , head infection rates were lower than that of the body at both 7 and 14 dpi but these differences were not significant [42] . The disparity observed in head infection rates between the present and previous study could possibly be due to the comparatively small sample size used by the previous study . Midgut and carcass were the most highly infected tissues in both Wildtype and wMel . F mosquitoes , respectively . In Wildtype mosquitoes ( Fig 1A ) there was a significant interaction between tissue and time ( Wald = 15; df = 5; p = 0 . 011 , ) . Midgut infections decline with time , becoming less of a source of infection beyond 8 days . Conversely , salivary glands are still becoming increasingly infected post 8 days . Interestingly , the pattern of infection across hemocoel-associated tissues indicates early dissemination and a plateau of infection rates as well as a similarity in the capacity for these tissues to support DENV replication . There was a clear effect of time ( Wald = 10; df = 5; p = 0 . 002 ) in wMel . F mosquitoes with infectivity increasing from 8 to 14 dpi across all tissues ( Fig 1B ) . Across the board , tissue infection rates are reduced in wMel . F mosquitoes as expected [14 , 42] but unlike in Wildtype mosquitoes , more tissues show rising infection rates with time , suggesting the power of blocking is strongest early in infection . To determine if time post infection and tissue type had an effect on DENV load in tissues , the DENV loads of all the tissues were compared at 8 and 14 dpi . There was a significant variation in DENV loads in tissues of both Wildtype ( Wald = 1497; df = 5; p< 0 . 0001 ) and wMel . F ( Wald = 50; df = 5; p<0 . 0001 ) mosquitoes . Midguts had the highest DENV load in the Wildtype mosquitoes while head had the lowest load in the wMel . F mosquitoes ( Fig 2 ) . Even though time did not have a significant effect on DENV loads in tissues of both Wildtype ( Wald = 2 . 1; df = 1; p = 0 . 144 ) and wMel . F ( Wald = 1 . 9; df = 1; p = 0 . 166 ) mosquitoes , there was a significant interaction between time and tissue for both Wildtype ( Wald = 131; df = 5; p< 0 . 0001 ) and wMel . F mosquitoes ( Wald = 180; df = 5; p<0 . 0001 ) . For instance while DENV load in the carcass decreased over time that of the salivary glands increased in Wildtype mosquitoes ( Fig 2A ) . On the other hand , DENV loads in the carcass increase over time while that of the salivary glands decreased in the wMel . F mosquitoes ( Fig 2B ) . For the most part , however , DENV appears to infect tissues early , reach a peak DENV load and remain relatively stable in Wildtype mosquitoes . In general , wMel . F mosquitoes , exhibited greater variation in DENV load across time and tissues and between individual mosquitoes than is seen for Wildtype possibly demonstrating variation in the efficacy of blocking . We examined if DENV load in a range of tissues early in the infection process was predictive of loads in the salivary glands by testing for correlations . In the wMel . F mosquito line , the efficacy of blocking effect rendered many mosquitoes uninfected . As such sufficient numbers of DENV positive heads were not obtained for either time point and all tissues at 8 dpi had to be excluded . For Wildtype mosquitoes head DENV loads were not predictive of salivary gland DENV loads at either time point , 8dpi ( r = 0 . 250; p = 0 . 516 ) or 14dpi ( r = -0 . 071; p = 0 . 867 ) ( Fig 3C ) . There was a significant correlation between midgut and salivary gland DENV loads only at 14 dpi ( r = 0 . 701; p<0 . 0001 ) ( Fig 3A ) in the Wildtype but not in the wMel . F mosquitoes ( r = 0 . 460; p = 0 . 550 ) at 14 dpi ( Fig 3B ) . In the Wildtype , salivary glands DENV loads were positively correlated to that of the Malpighian tubules at both 8 ( r = 0 . 684; p<0 . 0001 ) and 14 ( r = 0 . 783; p<0 . 0001 ) dpi ( Fig 4A ) . A positive correlation was also found between fat body and salivary gland DENV loads at both 8 ( r = 0 . 594; p = 0 . 002 ) and 14 ( r = 0 . 684; p<0 . 0001 ) dpi ( Fig 4C ) . Carcass DENV loads were positively correlated to salivary gland DENV loads only at 14 dpi ( r = 0 . 701; p<0 . 0001 ) ( Fig 4E ) . Malpighian tubule ( r = -0 . 260; p = 0 . 917 ) , fat body ( r = 0 . 299; p = 0 . 188 ) and carcass ( r = 0 . 127; p = 0 . 545 ) DENV loads were not predictive of DENV loads in wMel . F mosquito salivary gland ( Fig 4B , Fig 4D and Fig 4F ) . In summary , these findings show that DENV load in upstream tissues may predict salivary gland loads in Wildtype but not wMel . F infected Ae . aegypti . To determine if time and tissue type affect Wolbachia density in wMel . F mosquitoes , we compared Wolbachia density in the head , salivary glands , midgut , Malpighian tubules , fat body and carcass over two time points ( 8 and 14 dpi ) ( Fig 5 ) . We observed that time had no effect ( Wald = 0 . 18; df = 1; p = 0 . 671 ) on Wolbachia density in tissues . There was a significant tissue effect ( Wald = 3423; df = 5; p<0 . 0001 ) demonstrating that Wolbachia density varied across tissue types . For instance , Wolbachia was most abundant in the Malpighian tubules with the head having the lowest bacterial density . There was an interaction between time and tissue type ( WALD = 28; df = 5; p<0 . 0001 ) . For example in the Malpighian tubules , Wolbachia density decreased from 8 dpi to 14 dpi while that of the carcass increased from 8 to 14 dpi ( Fig 5 ) . In summary , Wolbachia density varied across different tissue types with the Malpighian tubules and head harbouring the highest and the least number of Wolbachia respectively . We first examined if the Wolbachia density in particular tissues was predictive of DENV load in that same tissue . In the carcass Wolbachia density was negatively correlated ( r = -0 . 580; p = 0 . 005 ) with DENV load at 8 dpi , but this was not the case at 14 dpi ( r = 0 . 160; p = 0 . 898 ) ( Fig 6E ) . At 8 dpi Wolbachia density in the midgut ( r = -0 . 125; p = 0 . 601 ) , salivary glands ( r = 0 . 453; p = 0 . 0680 ) , Malpighian tubules ( r = -0 . 095; p = 0 . 700 ) and fat body ( r = 0 . 070; p = 0 . 765 ) were not correlated with DENV load ( Fig 6A–6D ) . Neither was there a significant correlation between Wolbachia density in the midgut ( r = -0 . 060; p = 0 . 702 ) , salivary glands ( r = -0 . 063; p = 0 . 626 ) , Malpighian tubules ( r = -0 . 026; p = 0 . 873 ) and fat body ( r = 0 . 0044; p = 0 . 784 ) and DENV load at 14 dpi ( Fig 6A–6D ) . Infection rates for both DENV and Wolbachia were too low to be statistically analysed in the head for both 8 and 14 dpi . These findings demonstrate that Wolbachia density is not predictive of DENV load within any of tissue types tested . To determine if Wolbachia density in any particular tissue has an effect on DENV load in the salivary gland , we compared Wolbachia density in all the five dissected tissues to DENV load in the salivary glands . At 8 dpi there was no correlation between Wolbachia density in midgut ( r = 0 . 21; p = 0 . 940 ) , Malpighian tubules ( r = 0 . 412; p = 0 . 101 ) , fat body ( r = 0 . 221; p = 0 . 395 ) and carcass ( r = 0 . 051; p = 0 . 844 ) and DENV load in the salivary glands ( Fig 7A–7D ) . Neither was there a significant correlation at 14 dp between the Wolbachia density in midgut ( r = 0 . 155; p = 0 . 232 ) , Malpighian tubules ( r = 0 . 046; p = 0 . 732 ) , fat body ( r = 0 . 060; p = 0 . 642 ) and carcass ( r = 0 . 32; p = 0 . 801 ) ( Fig 7A–7D ) . Infection rates for both Wolbachia and DENV were too low to be statistically analysed in the head at both 8 and 14 dpi . These results show that Wolbachia density in intermediate tissues does not predict salivary gland DENV load .
Our findings in Wildtype mosquitoes demonstrate that DENV disseminates from the midgut and infects mosquito hemocoel-associated tissues equally through time . They also suggest that infection of the mosquito head is not an accurate proxy for the assessment of dissemination . In terms of Wolbachia-based blocking of DENV this study reports two main findings . Firstly , the Wolbachia tissue densities in the mosquito are not linear predictors of DENV load as has been reported in cell lines where densities are usually very high . This may be related to the much lower densities naturally present in insect tissues . Secondly , DENV inhibition is unlikely to be explained by tissue specific mechanisms . Future studies seeking to dissect the involvement of either immunity , resource competition or other unknown contributors to mechanism , should focus on aspects of host cell biology that are fundamental across tissues . Generalisations from cell line based-studies are likely to be more biologically meaningful when Wolbachia densities are lower and more reflective of those found in insect tissues . | Dengue fever caused by the dengue virus ( DENV ) is transmitted by the mosquito , Aedes aegypti . To control the disease , an intracellular bacterium called Wolbachia has been introduced into Ae . aegypti where it blocks/limits success of infection of DENV . The mechanistic basis of blocking is not well understood but may involve Wolbachia activating the host immune system or competing with DENV for host resources . The strength of blocking appears to correlate with Wolbachia density . Here , we aimed to determine if any particular tissues inside the mosquito play a greater role in blocking . Tissues were chosen based on their Wolbachia density and their roles in infection and immunity . Wolbachia infected and uninfected mosquitoes were orally infected with DENV and Wolbachia density and DENV load were assessed in midgut , salivary gland , head , Malpighian tubules , fat body and carcass . Wolbachia density did not correlate with DENV loads in the same tissues nor with DENV loads in the salivary glands . We also showed that no one tissue appeared to play a greater role in blocking . In summary , these finding suggest that in the mosquito a threshold Wolbachia density may be required for DENV blocking . Our findings also suggest that blocking may involve mechanisms that are fundamental to all cells . | [
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] | 2016 | Wolbachia-Based Dengue Virus Inhibition Is Not Tissue-Specific in Aedes aegypti |
Listeria monocytogenes is a model organism for cellular microbiology and host–pathogen interaction studies and an important food-borne pathogen widespread in the environment , thus representing an attractive model to study the evolution of virulence . The phylogenetic structure of L . monocytogenes was determined by sequencing internal portions of seven housekeeping genes ( 3 , 288 nucleotides ) in 360 representative isolates . Fifty-eight of the 126 disclosed sequence types were grouped into seven well-demarcated clonal complexes ( clones ) that comprised almost 75% of clinical isolates . Each clone had a unique or dominant serotype ( 4b for clones 1 , 2 and 4 , 1/2b for clones 3 and 5 , 1/2a for clone 7 , and 1/2c for clone 9 ) , with no association of clones with clinical forms of human listeriosis . Homologous recombination was extremely limited ( r/m<1 for nucleotides ) , implying long-term genetic stability of multilocus genotypes over time . Bayesian analysis based on 438 SNPs recovered the three previously defined lineages , plus one unclassified isolate of mixed ancestry . The phylogenetic distribution of serotypes indicated that serotype 4b evolved once from 1/2b , the likely ancestral serotype of lineage I . Serotype 1/2c derived once from 1/2a , with reference strain EGDe ( 1/2a ) likely representing an intermediate evolutionary state . In contrast to housekeeping genes , the virulence factor internalin ( InlA ) evolved by localized recombination resulting in a mosaic pattern , with convergent evolution indicative of natural selection towards a truncation of InlA protein . This work provides a reference evolutionary framework for future studies on L . monocytogenes epidemiology , ecology , and virulence .
The opportunistic pathogen Listeria monocytogenes causes life-threatening infections in animal and in human populations at risk . This facultative intracellular bacterium is widespread in the environment and infections occur through ingestion of contaminated food [1] , [2] . Although the species L . monocytogenes has long been known to be genetically diverse [3] , with strains showing differences in their virulence potential [4]–[7] , detailed knowledge of strain diversity and evolution is still lacking . Several methods have been used to differentiate L . monocytogenes strains [8] . The Listeria serotyping scheme [9] based on somatic ( O ) and flagellar ( H ) antigens currently represents a common language for L . monocytogenes isolate typing and investigations into the ecological distribution , epidemiology and virulence of strains . Unfortunately , serotyping discriminates only 13 serotypes , many of which are known to represent genetically diverse groups of strains , and only four serotypes ( 1/2a , 1/2b , 1/2c , and 4b ) cause almost all cases of listeriosis in humans [1] . Given its higher discriminatory power , pulsed-field gel electrophoresis ( PFGE ) is considered accurate for epidemiological investigations and of help for surveillance and control of listeriosis [10] , [11] , but fingerprint-based methods such as PFGE or ribotyping [12] are difficult to standardize . Hence , inter-laboratory comparisons necessitate considerable harmonization [13] , which limits knowledge at the global scale . In addition , these widely used methods provide only limited information on the phylogenetic relationships among strains , which is a serious limitation to understand the evolution of important phenotypic traits such as virulence . Sequence-based or SNP-based approaches appear as promising tools for strain typing and phylogeny in L . monocytogenes [14]–[17] . Multilocus sequence typing ( MLST ) [18]–[20] can accurately define the clonal framework of bacterial species . MLST has been shown to discriminate among L . monocytogenes isolates [14] , [21] , [22] , but has not yet been applied on a large scale , and an overview of the clonal structure of L . monocytogenes is currently not available . The molecular factors that determine ecological differences among strains are also poorly understood . One salient feature of the population structure of L . monocytogenes is the distinction of three phylogenetic lineages . Initially , two major lineages were distinguished , mainly based on multilocus enzyme electrophoresis and PFGE [3] , [10] , [12] , [23] , [24] , with a third lineage being subsequently recognized based on virulence gene variation , ribotyping and DNA arrays [25]–[28] . Lineage I includes isolates of serotypes 4b , 1/2b , 3b , 4d and 4e , whereas lineage II includes serotypes 1/2a , 1/2c , 3a and 3c . Lineage III contains serotypes 4a and 4c , as well as serotype 4b as was recently discovered [27] . The relative virulence and contribution of the three lineages and their serotypes to food contamination and clinical burden is subject of debate [3] , [26] , [27] , [29]–[32] . As each lineage is genetically heterogeneous , a precise delineation of L . monocytogenes clones is needed to determine which ones mostly contribute to human or animal infection [16] , [33] , [34] , and this knowledge would set a landmark for further studies on the biological characteristics of the clones and the evolution of molecular mechanisms by which they cause disease [35] . Several virulence genes play an important role in the virulence of L . monocytogenes strains [36] , [37] . Internalin ( InlA ) is a surface protein that mediates the entry of L . monocytogenes into various non-phagocytic human eukaryotic cells expressing its receptor E-cadherin [38] , [39] and plays a key role in the crossing of the intestinal barrier , enabling the bacterium to reach the host bloodstream [40] . Almost all isolates causing listeriosis in humans express a full-length functional InlA , whereas isolates expressing a truncated form are frequently found in food items and the environment and are associated with a lower virulence potential [5] . Currently , the ecological factors that drive the evolution of these apparently attenuated strains are unknown . Evolution of virulence would be best understood by mapping the variation of virulence genes such as inlA , onto the phylogenetic framework of the genomes in which they are presently distributed . The aims of this study were to provide a robust phylogenetic framework based on MLST analysis of a highly diverse isolate collection and determine ( i ) the population structure of L . monocytogenes; ( ii ) the evolutionary origin and stability of serotypes; and ( iii ) the patterns of variation of the virulence gene inlA with respect to the evolution of the core genome .
A total of 360 Listeria monocytogenes and four L . innocua isolates were selected from the collections of the French National Reference Centre for Listeria and the WHO Collaborative Centre for foodborne listeriosis ( Table S1 ) . These 360 L . monocytogenes isolates were subdivided in three subsets , each being included in order to address specific questions: ( i ) a diversity subset of 171 isolates , which included representative isolates of the distinct L . monocytogenes serotypes , atypical strains from lineage III , isolates that caused major epidemics throughout the world , strains for which the complete genome sequence is available , 75 historical strains collected from 1924 to 1966 and belonging to H . P . R . Seeliger Listeria Culture Collection ( Würzburg , Germany ) , isolates from the environment , food or animals , and research strains from several countries used in previous studies involving the Institut Pasteur Listeria laboratory ( Table S1 ) ; ( ii ) 126 isolates selected from maternal-fetal cases , collected prospectively and exhaustively from 1987 to 2005 ( i . e . , 5 to 10 epidemiologically non-related isolates randomly selected per year ) , and which were included to probe the temporal dynamics of clone prevalence ( ‘MF chronological’ subset in Table S1 ) ; and ( iii ) 63 isolates from year 2000 , including 25 from bacteremia , 20 from central nervous system ( CNS ) infection , and 18 from maternal-fetal infection , which were included to investigate the possible association of specific clones with clinical forms ( subset ‘Human clinical , 2000’ in Table S1 ) . Isolates were identified as L . monocytogenes using API Listeria strips ( BioMerieux , La Balme Les Grottes , France ) . Identification was confirmed and subdivided into serotypes by classical serotyping [9] , which distinguishes 13 serotypes , and multiplex PCR [41] , which groups L . monocytogenes isolates into four major groups ( IIA , IIB , IIC et IVB ) corresponding to groups of serotypes ( Table S1 ) . The MLST scheme used to characterize Listeria strains is based on the sequence analysis of the following seven housekeeping genes: acbZ ( ABC transporter ) , bglA ( beta-glucosidase ) , cat ( catalase ) , dapE ( Succinyl diaminopimelate desuccinylase ) , dat ( D-amino acid aminotransferase ) , ldh ( lactate deshydrogenase ) , and lhkA ( histidine kinase ) . This MLST scheme was adapted from the MLST system proposed by Salcedo and colleagues [14] , with the following modifications . First , the template for gene ldh was extended from 354 to 453 nucleotides , thus improving strain discrimination . Second , gene templates were shortened because the extremities of the previous templates correspond to parts of the PCR primer sequences , thus possibly not corresponding totally to the genomic sequence of the isolates analyzed . Third , we incorporated universal sequencing tails to the PCR primers ( Table 1 ) , which allows to sequence PCR fragments of all genes using only two primers . DNA extraction was performed by the boiling method [41] . The PCR amplification conditions were as follows: an initial cycle of 94°C for 4 min; 25 amplification cycles , each consisting of 94°C for 30 s , 52°C for 30 s ( except for bglA which has an annealing temperature of 45°C ) , and 72°C for 2 min; and a final incubation at 72°C for 10 min . The PCR products were purified by ultrafiltration ( Millipore , France ) and were sequenced on both strands with Big Dye v . 1 . 1 chemistry on an ABI3730XL sequencer ( Applied BioSystems ) . The 2 , 400 bp long inlA gene was sequenced from 157 isolates ( Table S1 ) representing the clonal diversity of L . monocytogenes ( see below ) . DNA extraction was performed with the Wizard® kit ( Promega Corporation , USA ) . The PCR amplification conditions were as follows: an initial cycle at 94°C for 5 min; 35 amplification cycles , each consisting of 94°C for 30 s , 55 . 2°C for 30 s , and 72°C for 1 min 30; and a final incubation at 72°C for 10 min . We used external primers for amplification and internal primers for sequencing ( Table 1 ) , which was performed as described above . For each MLST locus , an allele number was given to each distinct sequence variant , and a distinct sequence type ( ST ) number was attributed to each distinct combination of alleles at the seven genes . Numbers were initially based on highest frequency for the frequent alleles and STs , and were subsequently incremented arbitrarily . In order to define the relationships among strains at the microevolutionary level , we performed allelic profile-based comparisons using a minimum spanning tree ( MST ) analysis with the BioNumerics v5 . 10 software ( Applied-Maths , Sint Maartens-Latem , Belgium ) . MST analysis links profiles so that the sum of the distances ( number of distinct alleles between two STs ) is minimized [42] . Strains were grouped into clonal complexes ( clonal families ) , defined as groups of profiles differing by no more than one gene from at least one other profile of the group [19] . Accordingly , singletons were defined as STs having at least two allelic mismatches with all other STs . Neighbor-joining tree analysis was performed using MEGA v4 [43] or SplitsTree v4b06 [44] . Calculations of recombination tests were performed using RDP3 [45] . Nucleotide diversity indices were calculated using DNAsp v4 [46] . ClonalFrame analysis [47] was performed with 50 , 000 burn-in iterations and 100 , 000 subsequent iterations . To test for phylogenetic congruence among genes , one strain of all 39 STs with allelic mismatch distance >0 . 65 was used in order to exclude the expected congruence among genes at small evolutionary scale due to common clonal descent , as proposed previously [48] . Neighbor-joining trees were generated using PAUP* v4 [49] for each gene individually and for the concatenated sequence of the seven genes . For each gene , the differences in log likelihood ( Δ−ln L ) were computed using PAUP* between the tree for that gene and the trees constructed using the other genes , with branch lengths optimized [50] . These differences were compared to those obtained for 200 randomly generated trees . The relative contribution of recombination and mutation on the short term was calculated using software MultiLocus Analyzer ( Brisse , unpublished ) and the simplest implementation of the clonal diversification method [51] , [52] . For each pair of allelic profiles that are closely related , the number of nucleotide changes between the alleles that differ is counted . A single nucleotide difference is considered to be likely caused by mutation , whereas more than one mutation in the same gene portion is considered to derive from recombination , as it is considered unlikely that two mutations would occur on the same gene while the other genes remain identical . No correction was made for single nucleotide differences possibly introduced by recombination . We used the linkage model in Structure [53] to identify groups with distinct allele frequencies [53] . This procedure assigns a probability of ancestry for each polymorphic nucleotide for a given number of groups , K , and also estimates q , the combined probability of ancestry from each of the K groups for each individual isolate . We chose three groups for this report because repeated analyses ( 200 , 000 iterations , following a burn-in period of 100 , 000 iterations ) with K between 1 and 10 showed that the model probability increased dramatically between K = 2 and K = 3 and only slowly thereafter . The population recombination rate was estimated by a composite-likelihood method with LDhat [54] . LDhat employs a parametric approach , based on the neutral coalescent , to estimate the scaled parameter 2Ner where Ne is the effective population size , and r is the rate at which recombination events separate adjacent nucleotides . The crossing-over model L was used for the analysis of biallelic sites . We also tested for presence of positively selected sites using the software omegaMap [55] . This program applies a coalescence-based Bayesian strategy that co-estimates the rate of synonymous vs . non-synonymous substitions ω and the population recombination rate ρ , thus circumventing the high rate of false positives arising from incongruent phylogenies [56] . The following prior distributions were used for the analyses: μ , κ and Φindel: improper inverse , ω: inverse with range 0 . 0001–10 , ρ: inverse with range 0 . 01–10 . The variable block model was chosen for both ω and ρ , with block sizes of 10 and 30 , respectively . We created 10 subsets of 50 randomly drawn inlA sequences each , and analyzed each subset with 50 , 000 iterations and 10 reorderings . The first 20 , 000 sequences were discarded as burn-in period . Sequences generated in this study are available at www . pasteur . fr/mlst for the seven MLST genes . inlA sequences have been deposited in GenBank/EMBL/DDBJ databases under the accession numbers FM178779 to FM178796 and FM179771 to FM179785 . Alleles of the seven MLST genes were deposited under the accession numbers FM180227 to FM180445 .
The seven gene portions , sequenced in the 360 L . monocytogenes isolates , harbored a total of 438 polymorphisms ( 13 . 3%; range 7 . 01%–17 . 7% per gene ) consisting in bi-allelic ( 404 sites ) , tri-allelic ( 32 sites ) or four-allelic ( 2 sites ) single nucleotide polymorphisms ( SNPs ) . The average nucleotide diversity π was 2 . 91% , ranging from 1 . 18% to 5 . 98% per gene ( Table 2 ) . The GC% observed in all alleles ranged from 36 . 5% to 43 . 3% , consistent with the 39% value observed across the entire L . monocytogenes EGDe genome [57] . The 126 resulting allelic profiles ( or sequence types , STs ) were distributed into twenty-three clonal complexes ( CC ) and 22 singletons ( Figure 1 ) . Five CCs ( CC2 to CC4 , CC7 and CC9 ) consisted of a central prevalent genotype associated with several much less-frequent single locus variants ( SLVs ) . CC1 was slightly more diverse , as its central genotype had two SLVs that themselves were associated to other variants . ST5 stood out among all singletons by its high frequency . Each of these CCs and singletons is likely to have descended from a single ancestral bacterium , i . e . corresponds to a clone . Remarkably , the seven above-mentioned CCs were well demarcated , as they differed by at least four genes out of seven among themselves ( with the exception of CC2 and CC3 , with three mismatches between one pair of STs ) and by at least three mismatches from all other STs ( Figure 1 , inset A ) . Together , these seven clones comprised 58 ( 47% ) STs and 245 ( 69% ) isolates , and included 73% of the 252 recent ( after 1987 ) clinical isolates . Five of these clones belonged to lineage I ( see below ) and comprised 177 of 203 ( 87% ) isolates of this lineage . Other frequent clones were CC6 , CC8 and CC101 , together representing 32 ( 9% ) additional isolates . Reference strains of large outbreaks and genome sequencing project strains were mapped on the disclosed MLST diversity ( Figure 1; Table S1 ) ; for example , ST1 , ST6 and ST11 include reference strains of epidemic clones I , II and III [33] , [34] , respectively . Remarkably , most isolates within a given clone had the same serotype , or a restricted set of serotypes . CC1 and CC2 were dominated by isolates of serotype 4b , and included all isolates of serotypes 4d and 4e . CC3 comprised a large proportion ( 19/42 , 45% ) of isolates of serotype 1/2b , and included all isolates of serotypes 3b , and all but one ( ST75 ) isolates of serotype 7 . These results suggest that serotypes 4d and 4e each derived at least twice from 4b ancestors , consistent with previous data [16] , [28] , whereas isolates of serotypes 3b and 7 ( excepted ST75 ) may be regarded as serotypic variants of serotype 1/2b CC3 isolates . CC4 ( serotype 4b ) , ST5 ( 1/2b ) , CC6 ( 4b ) , CC7 ( 1/2a ) , CC101 ( 1/2a ) and CC102 ( 4b ) were each homogeneous with respect to serotype . Finally , CC9 included all isolates of serotype 1/2c , indicating that this serotype is genetically homogeneous . Notably , the virulent strain EGDe of serotype 1/2a also fell into CC9 . EGDe only differs from ST9 ( 1/2c ) by dapE ( allele dapE-20 instead of dapE-4 in ST9 ) , while it differs from all other 1/2a strains by several genes . CC9 also comprised the only included isolate of serotype 3c . Among isolates from human cases of listeriosis , we sought to determine the possible association between clones and clinical sources of the isolates . To eliminate the possible effect of the temporal variation ( see below ) , we compared L . monocytogenes isolates from a single year ( year 2000 ) and the three major clinical presentations in humans: bacteremia ( n = 25 ) , CNS infections ( n = 20 ) , and maternal-fetal infections ( n = 18 ) . These isolates corresponded to 28 STs , distributed into 7 CCs and 13 singletons ( Table S1 ) . There was no association of particular CCs or ST with clinical presentation: the 11 STs with more than one isolate were encountered in at least two clinical sources , and isolates from prevalent CCs or STs were equally isolated from the three clinical forms ( Table S1 ) . Possible trends in the relative prevalence of CCs over time were investigated based on 126 isolates from maternal-fetal cases of listeriosis , collected from 1987 to 2005 ( Table S1 ) . These isolates ( Table S1 ) fell into 43 STs and were grouped into 7 CCs and 14 singletons . Four CCs ( CC1 to CC4 ) and two singletons ( ST5 and ST9 ) comprised more than 10 isolates . Numbers of isolates of each of these clones over the 19 year period showed distinct patterns of temporal dynamics: while CC1 ( 4b ) and ST9 ( 1/2c ) were sampled equally over the entire period , CC3 ( 1/2b-3b-7 ) shows a clear decrease in prevalence ( 16 isolates before 1995 , 2 isolates after; Chi2 p<0 . 001 ) . In contrast , ST5 ( 1/2b ) was isolated only once before 1997 but 12 times in the second period ( p = 0 . 02 ) . Similarly , CC2 ( 4b ) showed an apparent increase in prevalence ( 2 vs . 9 , p = 0 . 034 ) . Divergence among genotypes appeared to be mainly driven by the progressive accumulation of mutations over time , as strains diverge from their common ancestor ( Figure 1 , inset B ) . Congruence among the seven individual gene phylogenies obtained for the distantly related STs [48] was statistically significant ( p<0 . 005 ) , as assessed by the likelihood method [50] . Similarly , the short-term contribution of recombination to genotypic diversity was modest , as L . monocytogenes alleles are five times more likely to change by mutation than by recombination ( r/m = 0 . 197 ) . In addition , the r/m rate for nucleotides was 0 . 59 , indicating that nucleotides are approximately twice more likely to change by mutation than by recombination . As an independent approach , the composite likelihood of r/m [54] on the concatenated sequence of the seven genes was 0 . 62 for lineage I , 0 . 47 for lineage II and 0 for lineage III . r/m values of some of the observed housekeeping genes exceeded 1 , but lacked statistical significance ( Table 3 ) . Consistently , r/m was 0 . 81 as estimated using ClonalFrame [47] . In order to determine which lineages underwent recombination events that left a detectable footprint in extant strains , the nucleotide polymorphisms within the seven gene fragments were analyzed with structure [53] , [58] , a Bayesian method that attempts to identify the ancestral sources of nucleotides . The ancestry of each isolate can be estimated as the summed probabilities of derivation from each ancestral group over all polymorphic nucleotides . structure recognized three clusters of strains within L . monocytogenes , which were largely homogeneous in terms of their ancestral sources of polymorphism ( Figure 2A ) . However , a number of isolates are likely to have a mixed origin ( Figure 2A ) , and this was confirmed statistically using RDP3 on the concatenated sequences ( Table S2 ) . Because recombination events , even if they are rare , can strongly distort phylogenetic reconstruction , we took into account potential recombination events using ClonalFrame ( Figure 3 ) . The majority-rule consensus tree revealed three major branches , which could be equated to the three currently recognized L . monocytogenes lineages I , II and III , as deduced from serotyping data and inclusion of reference strains . In particular , strains with serotypes 4b and 1/2b fell into lineage I , serotypes 1/2a and 1/2c were associated with lineage II , whereas serotypes 4a and 4c belonged to lineage III . The neighbor-joining ( NJ ) method ( Figure 2 ) retained the three major lineages , which were also consistent with the three major groups revealed by Structure . However , the obtained branching pattern was conspicuously distinct for those isolates that underwent recombination events . The most conspicuous example was isolate CLIP85 , which was clearly associated with lineage III ( Figure 3 ) , but not in the NJ tree ( Figure 2B ) . This difference could be attributed to horizontal transfer of lhkA from lineage II into CLIP85 , as detected with high probability by ClonalFrame ( Figure 3E ) . Likewise , strains that were inferred to have mixed ancestries ( Figure 2A ) were placed at the tip of relatively longer branches on the NJ tree ( Figure 2B ) than on the tree derived from ClonalFrame tree ( Figure 3 ) . One exceptional isolate , CLIP98 ( serotype 1/2a ) isolated from a human blood infection in Canada , was placed at the tip of a long branch , thus representing an apparent fourth lineage . Individual gene genealogies based on the neighbor-joining method also placed CLIP98 outside the three lineages , except for genes dat and lhkA , which clearly associated CLIP98 with lineage II ( not shown ) . Close inspection of the sequence alignment showed that a large proportion of nucleotide changes that distinguished CLIP98 from lineage II strains were clustered in a small number of short segments and corresponded to nucleotide bases also observed in L . innocua strains . The phylogenetic relationships within lineage I ( Figure 3B ) suggest that serotype 4b is monophyletic , since all strains of this serotype formed a unique branch . Differently , serotype 1/2b is paraphyletic , as it was encountered in two distinct branches , one of which is branching off early in the history of lineage I . Within lineage II , serotype 1/2a was paraphyletic , whereas 1/2c was monophyletic ( Figure 3C ) . Notably , the sequenced strain EGDe ( 1/2a ) appears to branch off just before the evolutionary change from 1/2a to 1/2c . The 2 , 400-nt coding sequence of virulence factor InlA showed 162 ( 6 . 7% ) polymorphic sites and 33 alleles undergoing a distinctive pattern of evolution ( Figure 4 ) . First , in contrast to housekeeping genes , phylogenetic analysis of inlA sequences revealed a conspicuous pattern of intragenic homologous recombination . Visual inspection of the distribution of polymorphic sites in inlA revealed a mosaic structure ( Figure 4 ) , with several regions having estimated recombination rates one order of magnitude higher ( ρ≥0 . 03 ) than for baseline regions ( ρ≈0 . 003 ) . Thus , each L . monocytogenes inlA sequence represents a composite assembly of short sequences with distinct evolutionary history , likely the result of multiple horizontal gene transfer events . Notably , in no case did we find fully- or nearly identical inlA sequences in unrelated STs , showing that horizontal transfer of entire inlA alleles is infrequent or non-existing , and that the entire inlA coding sequence is clone-specific . The short-term and long-term impacts of localized recombination in the inlA sequence were contrasted . Over the short term , inlA sequences clearly evolved more rapidly than housekeeping genes . For example , inlA sequences within lineage II evolved more than twice as fast ( c = 2 . 13 , r = 18% ) as MLST genes . In contrast , over the long term , inlA sequence divergence was restricted , as housekeeping genes were on average more divergent between the three major lineages than are inlA sequences ( e . g . , 4 . 8% and 1 . 3% , respectively , between lineages I and III strains ) . This constraint on inlA sequence divergence resulted in the lack of phylogenetic demarcation of lineages I and III ( Figure S1 ) , contrasting sharply with housekeeping genes-based phylogeny ( Figures 2 and 3 ) . Thus , import of sequence stretches from other clones accelerates diversification of clones , while homogenizing inlA sequences among distantly related strains . Second , when the entire length of inlA was considered , purifying selection against amino-acid changes appeared more relaxed than for housekeeping genes , with a Ka/Ks ratio for inlA ( 0 . 094 ) higher than for the seven concatenated MLST genes ( 0 . 039 for the same 157 isolates ) . However , the distribution of ω ( the Bayesian estimate of the rate of synonymous vs . non-synonymous substitutions ) along the sequence was heterogeneous , with a highly constrained LRR-region ( ω≈0 . 04 ) and moderately constrained Ig-like and B-repeats regions , with peak values of 0 . 5 . It is worth mentioning that no single stretch of the molecule displayed a significant signature of positive selection , a result that contradicts previous analyses in which recombination was not incorporated [59] . All but one amino-acid changes found in the LRR domain were located in repeats 1 to 6 ( Figure 4 ) , suggesting a stronger constraint on repeats 7 to 15 , which are more extensively involved in interactions with E-cadherin [60] . Truncated forms of InlA have been described and associated with reduced virulence [5] , [16] , [59] , [61]–[64] . We found four distinct inlA alleles that had premature stop codons ( PMSC ) at positions 492 , 539 , 577 and 685 ( Figures 1 and 4; Table 4 ) . Remarkably , three of these four PMSCs occurred in isolates that belonged to CC9 ( Figure 1 ) . In addition , out of eight previously reported inlA sequences leading to truncated forms , five were identical or nearly identical ( <2 SNPs ) to the inlA sequences that are specific of clone CC9 , strongly suggesting that they first occurred in isolates of clone CC9 as well . The remaining PMSC ( at codon 492 ) was observed in ST121 ( 1/2a ) , and three other PMSCs from previous reports were also observed in inlA alleles unrelated to those in CC9 ( Table 4 ) .
A majority of L . monocytogenes isolates belonged to limited number of major clones . Although these were defined based on allelic profiles , the same groupings were obtained by MST analysis based on nucleotide sequences ( not shown ) , with the exception of the three STs ( ST35 , ST15 and ST74 ) inferred to derive by a recombination event that changed more than two SNPs . Hence , no significant loss of information was incurred by collapsing nucleotide sequence information into allelic profiles , as expected given limited amounts of recombination . Interestingly , the major clones were almost always separated from other strains by at least three allelic mismatches , indicating ancient divergence . Hence , a relaxed criterion ( e . g . , two allelic mismatches ) would have little impact on the assignment of L . monocytogenes isolates to particular clones . Given the neat delineation of the major clones and the fact that they account for a large proportion of clinical L . monocytogenes isolates , we propose that these genetic entities represent reference units for future studies on strain virulence , ecology or epidemiology . For global population biology and international surveillance purposes , a definitive strain typing scheme is greatly needed [18] . The present MLST data represent a unifying language on clone characterization in L . monocytogenes and are freely available for comparison at www . pasteur . fr/mlst . Other sequence-based strain characterization methods have been developed [15]–[17] , [34] . Future determination of the relative power of MLST and these methods for discrimination among L . monocytogenes strains , as well as establishment of the correspondence among the sequence types they define , is required for optimal communication and will provide Listeria specialists with a choice of methods that may be suited for distinct purposes ( e . g . , fine-scale epidemiology versus long-term population biology ) . Although our strain collection was not designed to address the important question of ecological or epidemiological differences among L . monocytogenes clones , we found different distributions of serotypes and clones among animal , environmental and clinical isolates that are consistent with previous reports [26] , [27]; for example , serotypes 4b clones mostly included isolates from clinical or food sources , whereas they are rare among isolates from animals ( 4 out of 21 ) or the environment ( none out of 9 ) . However , further MLST studies should be performed with ecologically representative collections of isolates . Likewise , our initial temporal analysis of maternal-fetal isolates suggests the existence of temporal shifts in prevalence of clones over relatively short periods of time ( 19 years ) , but a larger longitudinal survey is needed to provide a clearer picture of the temporal dynamics of these clones . Finally , the disclosed diversity is based largely on isolates from France , and a worldwide collection may therefore reveal additional diversity . However , it is noteworthy that many reference strains from outbreaks in other countries and continents belonged to clones defined by French isolates ( Figure 1 ) . It is also important to remember that L . monocytogenes is an environmental saprophyte , which does not need to infect animals for survival and propagation . The diversity of clinical isolates may thus only represent a particular subset of the entire diversity of this species . The evolutionary relationships of serotypes within the major lineages have not been previously defined , although this knowledge is particularly important for correct interpretation of serotyping data . We found that all serotype 4b strains belonged to a unique branch , consistent with early MLEE data [3] and recent sequence data [16] . This result indicates that serotype 4b appeared only once during the evolution of lineage I . Hence , the apparent increased potential of serotype 4b strains to cause outbreaks may be explained by genetic characteristics that evolved ancestrally , before the diversification of serotype 4b into several clones that appear highly successful among clinical isolates . The fact that the 4b branch is nested within a larger diversity made of 1/2b strains suggests that serotype 1/2b is more ancestral than 4b , and possibly represents the ancestral serotype of lineage I . Likewise , within lineage II , paraphyletic serotype 1/2a clearly stands as the most likely ancestor of lineage II , with serotype 1/2c having evolved more recently ( Figure 3C ) . As a consequence , our results contradict the DNA array-derived hypothesis that serotype 1/2c represents an ancestral state [28] . Notably , strain EGDe ( 1/2a ) appears to branch off from the ancestor of CC9 1/2c strains just before the evolutionary shift from 1/2a to 1/2c . Hence , strain EGDe may represent a genomic state close to the evolutionary link between 1/2a and 1/2c strains . While exhibiting an antigenic structure that retained ancestral characteristics , its full genomic content [57] is likely to be more related to that of 1/2c strains than to most other 1/2a strains , consistent with DNA array data [28] , [41] . Groups of distinct serotypes observed in isolates with the same ST or CC reflect relatively recent evolution of antigenic structures . Under our evolutionary scenario ( Figures 1 and 2 ) , serotypes 7 and 3b derived from serotype 1/2b in the branch corresponding to clone 3 , whereas serotypes 4d and 4e each derived at least twice from distinct 4b ancestors , as also proposed recently [16] . Likewise , serotype 3c may be derived from 1/2c strains ( although the reverse , from 3c to 1/2c , cannot be excluded ) . All these evolutionary changes in serotype involve modification of the somatic antigens [68] . In contrast , H ( flagellar ) antigens appear stable over evolutionary time , as we inferred a single within-lineage change of the H antigen , namely from antigen A to antigen D , corresponding to the evolution of 1/2c [antigenic formula I , II , ( III ) :B , D] from 1/2a [I , II , ( III ) :A , B] [68] . Knowledge and subsequent characterization of the genetic determinants of somatic and flagellar antigenic structures [69] , [70] will provide molecular details on serotype evolution . The distinct phylogenetic methods used herein consistently identified three major lineages of L . monocytogenes strains , in agreement with a wide set of previous studies based on alternative markers [25]–[27] . The only exception was isolate CLIP98 , placed at the tip of a long branch with weak relatedness to any lineage . However , we suggest that CLIP98 does not represent the first disclosed member of a fourth L . monocytogenes lineage . Instead , the fact that two genes of CLIP98 were typical of lineage II , whereas numerous polymorphisms in the other five genes were shared with L . innocua , suggests that CLIP98 could have a ‘hybrid’ genome derived from lineage II , but which has received from L . innocua donors enough recombined fragments to now appear poorly affiliated with its ancestral lineage . Because putative recombined segments are very small and often consist of only two or three SNPs , these imports were overlooked by ClonalFrame . As three nucleotides of CLIP98 ( between positions cat 364–368 ) were also uniquely shared with lineage III strains , CLIP98 may correspond to a recombinant strain with multiple ancestries , possibly due to an increased capacity for incorporation of foreign DNA . Further genomic characterization of this isolate is needed to clarify these aspects . L . monocytogenes lineages differed from L . innocua , their closest relative , by 11% of nucleotide positions on average . This is consistent with the monophyly of L . monocytogenes and does not support the hypothesis of a descent of L . innocua as a whole from L . monocytogenes [28] . Amounts of diversity within the three lineages were 0 . 33% , 0 . 61% and 1 . 25% , respectively , consistent with data obtained by others [22] , [27] . In contrast , the nucleotide divergence was 4 . 99% between lineages I and II , 5 . 3% between lineages I and III , and 7 . 57% between lineages II and III . Notably , there was not a single common allele among the three lineages , as well as between them and L . innocua , whereas strains within a particular lineage generally shared at least one allele . Hence , consistent with DNA hybridization data [71] , the three major lineages correspond to clearly demarcated sequence clusters that fulfill the separateness criteria and divergence levels used in other bacterial groups to distinguish species [72]–[75] . However , as noted earlier [27] , the issue of taxonomic revision of L . monocytogenes needs careful evaluation . In particular , improved sampling ( especially from diversity-rich sources such as the environment ) would be necessary to challenge the neat demarcation among lineages and characterize their ecology . The rate of homologous recombination within bacterial species can differ widely from one species to another [76] , [77] and has a profound impact on the validity of phylogenetic analyses [78] , on the evolutionary stability of genotypes [79] , on biological features such as virulence [35] , [80] and on interpretation of typing data [20] . Previous reports have indicated that L . monocytogenes undergoes low levels of recombination among housekeeping genes [3] , [14] . Here , we quantified the relative impact of recombination and mutation at various levels of phylogenetic depth and found similar estimates with independent approaches . To our knowledge , L . monocytogenes is one of the bacterial species with the lowest rate of recombination [50] , [77] , [81] . Highly restricted levels of recombination were disclosed in all three major lineages , contrasting with a previous proposal that rates of recombination vary among lineages [66] . Full genome sequencing [57] suggested that competence genes are present in L . monocytogenes EDGe ( CC9 ) , but the regulatory genes for their expression have not been identified , and a noncoding RNA ( RliE ) may regulate negatively the L . monocytogenes orthologs required for competence in Bacillus subtilis [82] . Yet , evidence that L . monocytogenes retained the ability for localized recombination is clearly provided by the inlA gene encoding InlA , in agreement with previous reports [22] , [27] , [59] . For this gene , recombination events may contribute to the acquisition by the recipient strain of a selective advantage , for example by escaping the immune response while retaining the ability to interact with its receptor . Such exchange could possibly occur in the intestinal lumen , in which multiple L . monocytogenes strains may coexist . Hence , rapid diversification of inlA sequences contrasts with the inferred high stability of clonal backgrounds defined by housekeeping genes . This illustrates how the phylogenetic structure based on MLST genes provides a scaffold , which sheds light onto the evolution of individual genes exposed to selective pressures , such as virulence genes . Currently , the ecological significance of loss of a full-length InlA is not understood , and the clonal background in which these forms evolve have not been defined precisely . Little is known about the ecology of L . monocytogenes clones , but a realistic scenario is that different clonal families might be adapted to different niches , and their occurrence as mammalian pathogens may be of limited significance for their evolutionary success in the long term . Among the four alleles of gene inlA identified among ST9 isolates , the one corresponding to the non-truncated form ( inlA-8 ) can be inferred to be ancestral , as the three inlA alleles corresponding to truncated forms ( inlA-9 , inlA-10 and inlA-16 ) differed by only one mutation from inlA-8 , whereas they differed by two mutations from each other ( Table 4 ) . In addition , inlA-8 was also found in strain EGDe ( ST35 ) , which was inferred to branch off before diversification of other CC9 members ( Figure 3 ) . It is intriguing that InlA , an important bacterial factor for host colonization , was repeatedly lost by convergent evolution in the genetically homogeneous 1/2c ST9 genotype . Such a pattern can be explained either by a relaxed selective constraint on maintaining InlA function , or by a selective advantage provided by the loss of a functional InlA protein , in the ecological niche occupied by members of ST9 . Determining the natural habitat of ST9 may provide clues as to why the expression of a virulence trait may in fact turn out to be disadvantageous in particular environments . | Listeria monocytogenes is a pathogen transmitted through contaminated food and is responsible for severe infections , including meningitis and abortion in animals and humans . It is known that many distinct strains of this pathogen exist , and that they differ in their virulence and epidemic potential . Unfortunately , there is currently no standard definition of strains and no comprehensive overview of their evolution . To tackle these serious limitations to the control of listeriosis and to improve knowledge of how virulence evolves , we characterized a large collection of isolates with sequence-based genotyping methods . We were thus able to identify precisely the most prevalent clones of L . monocytogenes , i . e . , groups of isolates that descend from a single ancestral bacterium , which can now be characterized further for diagnostic purposes and determination of their precise ecology and virulence potential . We also determined how these clones evolved from their common ancestor and the evolutionary history by which they acquired their phenotypic characteristics , such as antigenic structures . Finally , we show that some particular strains tend to lose a virulence factor that plays a crucial role in infection in humans . This is a rare example of evolution towards reduced virulence of pathogens , and the discovery of the selective forces behind this phenomenon may have important epidemiological and biological implications . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"microbiology/microbial",
"evolution",
"and",
"genomics"
] | 2008 | A New Perspective on Listeria monocytogenes Evolution |
Planarian regeneration requires positional information to specify the identity of tissues to be replaced as well as pluripotent neoblasts capable of differentiating into new cell types . We found that wounding elicits rapid expression of a gene encoding a Forkhead-family transcription factor , FoxD . Wound-induced FoxD expression is specific to the ventral midline , is regulated by Hedgehog signaling , and is neoblast-independent . FoxD is subsequently expressed within a medial subpopulation of neoblasts at wounds involving head regeneration . Ultimately , FoxD is co-expressed with multiple anterior markers at the anterior pole . Inhibition of FoxD with RNA interference ( RNAi ) results in the failure to specify neoblasts expressing anterior markers ( notum and prep ) and in anterior pole formation defects . FoxD ( RNAi ) animals fail to regenerate a new midline and to properly pattern the anterior blastema , consistent with a role for the anterior pole in organizing pattern of the regenerating head . Our results suggest that wound signaling activates a forkhead transcription factor at the midline and , if the head is absent , FoxD promotes specification of neoblasts at the prior midline for anterior pole regeneration .
Planarians can regenerate from nearly any injury , but how missing tissues are recognized and replaced is poorly understood . The adult population of proliferating cells ( neoblasts ) in Schmidtea mediterranea includes pluripotent stem cells [1] and is responsible for new tissue production in regeneration . New cell production at wounds produces an outgrowth called a blastema , which will replace some of the missing tissues [2] . At the molecular level , injuries trigger a rapid wound response program that includes conserved immediate early genes and patterning factors required for normal regeneration [3] . Wnt and Hedgehog ( Hh ) signaling pathways instruct the regeneration of the anterior-posterior ( AP ) axis , whereas the Bmp signaling pathway controls the regeneration of the dorsal-ventral ( DV ) axis [4]–[13] . Multiple genes required for positional identity control during embryonic development of other organisms , such as Wnt and Bmp signaling ligands , display constitutive regionalized expression in adult planarians and also guide pattern maintenance during tissue turnover [14] . Two distinct regions composed of a small cluster of cells located at the anterior and posterior animal extremities are referred to here as the anterior and posterior planarian poles . The poles are found at the midline of the animal and are subjects of current intense study . The anterior pole expresses notum , a Wnt inhibitor required for head regeneration [8] , whereas the posterior pole expresses wnt1 [7] , [14] . A number of genes encoding proteins predicted to be involved in signaling pathways that regulate planarian body plan patterning , and that display regional expression in planarian muscle cells have been described [15] . The genes that display these unique attributes will be referred to as position control genes ( PCGs ) for simplicity of discussion , but it is important to note that patterning phenotypes have not yet been described for many of these genes . PCGs expressed broadly in the planarian head include the candidate Wnt inhibitor , sFRP-1; candidate FGF inhibitors nou darake ( ndk ) and ndl-4; and a homeodomain transcription factor , prep [6] , [7] , [14]–[17] . PCGs expressed broadly in the posterior region of the animal include genes encoding additional Wnt ligands , wnt11-1 , wnt11-2 , and wntP-2/wnt11-5 and the Wnt receptor frizzled-4 [6] , [7] , [14] , [15] , [18]; Hox genes are also expressed in the posterior [14] . Several PCGs are expressed at the planarian poles , but have broader expression domains that extend beyond the cluster of notum+ or wnt1+ cells at the animal head and tail tips . How the poles are formed and the role they have in organizing regeneration of an animal with a properly patterned body plan is poorly understood . Two genes encoding transcription factors of the TALE-class homeodomain family , prep and pbx , are required for regeneration of the expression domains of anterior PCGs and the anterior pole marker notum [17] , [19] , [20] . pbx [20] and a LIM-homeobox gene ( djislet , [21] ) are required for regeneration of expression domains of posterior PCGs and the posterior pole marker wnt1 . follistatin , which encodes a secreted regulator of TGF-β proteins , is also expressed at the anterior pole and is required for normal head regeneration [22] , but strong inhibition of this gene can also result in the absence of blastema formation indicating a broader role of this gene during regeneration [23] . Forkhead-box ( Fox ) genes are an evolutionary ancient family of winged-helix transcription factors involved in a wide variety of biological processes such as regulation of cell proliferation , growth , and differentiation [24] . During embryogenesis , Fox genes are involved in organogenesis and patterning of several tissues from all three germ layers [25] . Mutations in Fox genes have a profound impact in human disease causing a variety of phenotypes , from eye abnormalities to speech impediments [25]–[27] . Some members of the Fox gene family are expressed in restricted regions of embryos . In Drosophila , genes encoding Forkhead-family proteins , fkh , sloppy paired 1 and 2 , and crocodile , are all expressed in the anterior region of the embryo , and are required for midline establishment as well as head patterning [28]–[31] . In amphioxus , FoxQ is expressed at the anterior pole during embryogenesis [32] . In Xenopus , the forkhead family gene , XDF1 is expressed in Spemann's organizer and at later stages in the anterior neural region [33] . In planarians , few Fox genes have been described . In particular , DjFoxD is expressed in few cells at the anterior pole region of the planarian D . japonica [34] and FoxD influences expression of follistatin in planarian heads [22] , [34] . Given the potential importance of the planarian anterior pole in organizing head regeneration , we investigated the role of Schmidtea mediterranea FoxD in regeneration .
A number of genes have been identified that are expressed in different domains of planarian heads . To provide a molecular definition of the anterior-most end of the planarian head , the anterior pole , we investigated the expression of a number of genes expressed near the planarian head tip . FoxD is expressed in a very small number of cells at the head tip ( Fig . 1 and [22] , [34] ) , but the pole and its role ( s ) are poorly defined; we focused our investigation of the anterior pole on the Schmidtea mediterranea ortholog of DjFoxD , Smed-FoxD , or FoxD in short ( Fig . S1 ) . FoxD expression in intact animals was dorsal-biased and most FoxD+ cells also expressed the gene notum . notum is required for the head-versus-tail regeneration decision known as AP regeneration polarity , and encodes a secreted inhibitor of Wnt signaling [8] . Like FoxD , notum expression in uninjured animals is largely restricted to a very small number of cells at the head tip ( Fig . 1 and [8] ) . FoxD+ cells at the head tip also expressed multiple anterior markers ( PCGs ) that are expressed in AP transcriptional domains extending beyond the FoxD+ cells , including sFRP-1 , ndk , ndl-4 , and prep , but not sFRP-2 ( Fig . 1 ) . We also assessed whether FoxD is co-expressed with two planarian genes expressed at the DV boundary ( a lateral domain surrounding the animal at the midpoint of dorsal and ventral surfaces ) and/or at the midline ( the median plane about which bilateral symmetry is organized ) : admp , encoding a BMP-family signaling ligand expressed in the ventral midline and at the DV boundary [10] , [11] and slit , a conserved midline cue with a prominent role in regulation of axon guidance [35] expressed in the ventral and dorsal planarian midline [35] . admp and slit expression did not substantially coincide with pole cells expressing FoxD; midline slit expression did not reach the anterior-most region where FoxD-expressing cells are found ( Fig . 1 ) . We propose a definition for the planarian anterior pole in the mature head as the few cells restricted to the head tip and that co-express the highly restricted FoxD and notum genes together with a set of anterior PCGs , but displaying little expression of DV boundary and midline genes . The cellular basis for formation of these cells and the roles of these cells in regeneration are investigated below . FoxD expression was highly induced by three hours following wounding in subepidermal cells , with expression peaking at approximately six hours after wounding ( Fig . 2A ) . FoxD expression after amputation occurred at both anterior- and posterior-facing wounds , raising the possibility that FoxD is a generically wound-induced gene ( Fig . 2A ) . FoxD expression after amputation was greatly diminished by 18 to 24 hours following injury ( Fig . 2B ) , but increased again between 24 and 48 hours after amputation . At this later time FoxD expression was only observed at anterior-facing wounds that required the formation of a new anterior pole ( Fig . 2B and Fig . S2A ) . Irradiation eliminates neoblasts [36] , which comprise the entire population of dividing adult planarian cells; therefore , amputation experiments in irradiated animals can determine whether new gene expression in regeneration occurs in pre-existing cells at wound sites , or requires new cell production . Early wound-induced expression of FoxD was irradiation-insensitive , indicating that it is a transcriptional response in cells present at the time of wounding ( Fig . 2B ) . The large numbers of planarian wound-induced genes described so far are expressed broadly at the wound site [3] , [8] , [18] , [37] . By contrast , wound-induced FoxD expression following amputation was unique: restricted to cells found in the ventral midline ( Fig . 2A , B ) . To better understand the regulation of FoxD expression by wounding , we examined the impacts of several injury types on FoxD expression ( Fig . 2C ) . First , we observed that expression of FoxD was not exclusive to wounds requiring pole regeneration; both parasagittal and sagittal amputations induced FoxD expression in the midline broadly along the wound site ( first two cuts from the left , Fig . 2C ) . Incision into the planarian side with a scalpel , a wound not requiring blastema formation for repair , was sufficient to induce FoxD expression . Strikingly , FoxD was expressed at anterior and posterior areas of the incised wound site , but only within the midline on the ventral side ( third cut from left , Fig . 2C ) . This wound-induced FoxD expression occurred even in the absence of the anterior pole , indicating that local cues rather than signals from the pole control wound-induced expression of FoxD . An even more minor injury , a dorsal puncture with a needle ( third panel from right , Fig . 2C ) , induced FoxD expression in the vicinity of the dorsal puncture , but only ventrally in the midline . All of these wound types impinged upon the midline of the animal , raising the possibility that any midline injury is sufficient to trigger FoxD expression , regardless of whether blastema formation would be required for repair or not . Supporting this hypothesis , two wound types that do not damage the midline ( lateral puncture and lateral edge removal; first and second panels from right , Fig . 2C ) did not trigger expression of FoxD . These two wounds did , by contrast , elicit normal expression of other wound-induced genes ( Fig . 2D ) . The wound-induced expression of FoxD occurred within ventral midline cells expressing the midline markers admp and slit ( Fig . 2E ) . Furthermore , FoxD was also co-expressed at wounds with other defined wound-induced genes , such as noggin-like1 ( nlg1 ) [3] , [38] , wntless [3] , [12] , [38] , notum [8] , and wnt1 [37] ( Fig . 2E and Fig . S2B ) . Wound-induced genes that peak in expression around six hours following injury and that are mostly expressed subepidermally at wound sites are known as W2 genes [3] . Although most W2 genes are predicted to be secreted proteins , such as signaling and matrix remodeling factors , our results indicate that the transcription factor FoxD itself belongs in this category . W2 gene expression is induced in muscle cells expressing collagen [15] , and most wound-induced FoxD expressing cells ( 92 . 5±4 . 6% ) also co-expressed the muscle gene collagen ( Fig . 2E ) . We conclude that FoxD is a wound-induced gene in planarian muscle , but unique among known planarian wound-induced genes with expression occurring only in ventral midline cells and only following injury that impinges on the midline . FoxD also defines a fourth gene ( together with wnt1 , notum , and follistatin ) that is wound-induced and subsequently expressed in either the anterior or posterior pole . The second phase of FoxD expression during regeneration , initiating around 24 hours post-amputation , occurred in cells that were coalesced at the anterior pole . This expression phase presented the opportunity to define the cellular steps of anterior pole formation . Irradiated animals did not form an anterior pole and did not express FoxD at 24 to 72 hours following wounding ( Fig . 2B ) , indicating the requirement of neoblasts for this process . FoxD-expressing cells at the regenerating anterior pole at this time also co-expressed multiple anterior PCGs as well as the anterior pole-expressing gene notum ( Fig . 3A ) . follistatin , which is required for anterior regeneration [22] , [23] , was also co-expressed with FoxD at the regenerating anterior pole at this regeneration stage ( Fig . 3A ) . FoxD expression gradually became restricted with time to fewer cells at the regenerating anterior pole , adopting the same appearance as in intact animals ( Figs . 1 and 3A ) . The planarian smedwi-1 gene encodes a PIWI family protein and is expressed in all dividing adult planarian cells , marking the neoblast population [1] . We found that some FoxD-expressing cells located at the forming anterior pole co-expressed smedwi-1 in regenerating blastemas 72 hours following amputation ( Fig . 3B ) . In addition to FoxD , expression of the anterior pole gene notum and the anterior-patterning gene prep was also found in smedwi-1-expressing neoblasts at the regenerating anterior pole ( Fig . 3B ) . These data suggest that by three days of regeneration some neoblasts have been specified to produce the new pole cells of the regenerating head , potentially marking the first cellular step in formation of a new anterior pole . Both Wnt and Hh signaling pathways are required for the head-versus-tail regeneration decision made at planarian amputation planes [4]–[8] , [39] . To test whether wound-induced FoxD expression was regulated by these signaling pathways , we inhibited Wnt ( β-catenin and APC ) and Hh ( patched ( ptc ) and hedgehog ( hh ) ) pathway genes with RNAi and examined FoxD expression six hours following amputation . As controls , we analyzed the numbers of notum- and wnt1-expressing cells in the different RNAi conditions and , as expected from prior reports [5] , [8] , notum expression was exclusively affected in β-catenin and APC RNAi animals and wnt1 expression was affected following perturbation of the Hh signaling pathway ( Figs . 4 and S3A ) . Perturbation of Wnt signaling did not affect wound-induced FoxD expression ( Figs . 4 and S3A ) . By contrast , ptc ( RNAi ) animals displayed fewer than normal FoxD-expressing cells at wounds and hh ( RNAi ) animals had increased numbers of FoxD-expressing cells ( Figs . 4 and S3A ) . patched ( ptc ) encodes a receptor for Hh that antagonizes pathway output [40] . Hh signaling therefore negatively regulates FoxD induction at the midline following wounding . Because hh impacts wound-induced wnt1 expression oppositely to FoxD [4] , [5] , this result does not reflect a generic requirement for hh in wound-induced gene activation . FoxD expression at the anterior pole was normal in hh ( RNAi ) anterior blastemas ( Fig . S3B ) , indicating a specific role for hh in regulating the FoxD wound-induced phase of expression . ptc ( RNAi ) animals with a strong phenotype regenerate tails in place of heads , but we found no evidence that FoxD influences the head-versus-tail regeneration choice . However , ptc ( RNAi ) animals with a weak phenotype ( e . g . , cyclopic heads ) do resemble FoxD ( RNAi ) animals [4] , [5] . Cyclopic or headless ptc ( RNAi ) animals showed decreased expression of the anterior PCG sFRP-1 ( [5] and Fig . S3D ) and decreased expression of the anterior pole marker notum ( Fig . S3D ) , indicating a defect in anterior pole regeneration . Therefore , the defect in FoxD expression might contribute to the ptc ( RNAi ) phenotype . In vertebrates , Sonic hedgehog ( a member of the Hh family ) signaling is required for normal forebrain development and midline induction [40] , [41] . In planarians , hh is expressed ventrally and medially [4] , [5]; the impact on wound-induced expression of FoxD at the midline raises the interesting possibility that hh might also have a role in midline biology in planarians . In ptc ( RNAi ) animals , midline expression of slit at six hours following amputation was normal ( Fig . S3C ) . 86 . 3±5 . 5% of FoxD-expressing cells following wounding co-express the midline gene slit in wild-type animals . Therefore , these results indicate that the reduced wound-induced expression of FoxD in ptc ( RNAi ) animals is not a consequence of the absence of the midline cells that normally express FoxD . FoxD ( RNAi ) animals displayed defective regeneration , with variable blastema size and heads that regenerated either one or no eyes ( Fig . 5A ) . FoxD ( RNAi ) head blastemas had abnormal anatomy , with medial collapse of cephalic ganglia ( labeled with an RNA probe to choline acetyltransferase ( chat ) [42] ) , one or no eyes ( detected with an anti-ARRESTIN antibody ( VC1 ) [43] ) , and a slightly abnormal anterior intestine morphology ( labeled with an RNA probe to mat [1] ) ( Fig . 5A ) . Tail fragments regenerated pharynges ( Fig . S4B ) , demonstrating that some missing tissues can regenerate in FoxD ( RNAi ) animals . Moreover , intestinal branches were normally regenerated in FoxD ( RNAi ) tail blastemas ( Fig . S4C ) , indicating that FoxD has a largely specific role in anterior regeneration . Parasagittal thin fragments of FoxD ( RNAi ) animals also regenerated pharynges ( Fig . S6B ) , further demonstrating that neoblasts can replace missing tissues . However , most of these fragments regenerated only one eye , showed slightly reduced expression of the anterior PCG sFRP1 , and regenerated asymmetric cephalic ganglia ( Fig . S6B ) . Blastema size abnormalities in FoxD ( RNAi ) animals did not appear to be an overt consequence of a neoblast maintenance defect , because normal mitotic cell numbers were present in FoxD ( RNAi ) animals at the time of the amputation ( 0 hours following wounding , Fig . S4A ) . Following FoxD RNAi , neoblast ( smedwi-1-expressing cells ) proliferation ( 6 hours post-amputation , Fig . S4A ) and migration ( 18 hours post-amputation , Fig . 5B , upper panel ) in response to wounding were normal . At 48 hours following wounding , neoblasts of regenerating FoxD ( RNAi ) tail , but not trunk , fragments displayed slightly reduced neoblast proliferation ( Fig . 5B and Fig . S4A ) . Reduced proliferation in FoxD ( RNAi ) tail fragments persisted five days following wounding ( Fig . S4A ) . FoxD ( RNAi ) animals have reduced numbers of follistatin-expressing cells at the anterior pole [22] . At 48 hours following wounding notum+ and notum+ follistatin+ cells were reduced from regenerating FoxD ( RNAi ) anterior blastemas ( Fig . 5B and Fig . S5A ) . Furthermore , neoblasts expressing notum and prep were also fewer or absent in FoxD ( RNAi ) animals ( Fig . S7 ) , suggesting that FoxD is required for neoblast specification into anterior pole cell progenitors . These observations suggest a role for FoxD in anterior pole regeneration by specifying pole progenitors . At 72 hours following head amputation , we observed significantly lower or complete absence in expression of the anterior PCGs prep , ndl-4 , and sFRP-1 , and decreased or no notum-coalesced cells in FoxD ( RNAi ) animals ( Fig . 5C ) . After seven days of regeneration , FoxD ( RNAi ) anterior blastemas also showed severe defects in the expression of sFRP-1 and ndl-4 , as well as the pole-restricted gene notum ( Fig . 5D and Fig . S5B ) . Together , these results establish a role for FoxD and the anterior pole in head patterning during regeneration . FoxD ( RNAi ) animals had normal numbers of notum- and wnt1-expressing cells at six hours following wounding ( Fig . 6A ) , indicating that FoxD does not prevent the wound-induced phase of notum expression during the specification of head regeneration . Furthermore , wound-induced expression of follistatin [22] , [23] was normal in FoxD ( RNAi ) animals ( Fig . S5A ) . We also did not observe ectopic expression of posterior markers ( wnt11-2 and wnt1 ) in regenerating anterior blastemas of FoxD ( RNAi ) animals ( Fig . S4D ) , demonstrating that the choice to regenerate a head instead of a tail ( AP regeneration polarity ) was not detectably affected . In addition , we observed normal expression of posterior markers ( wnt11-2 and wnt1 ) in regenerating posterior blastemas , indicating a specific role for FoxD in anterior regeneration ( Fig . 6B ) . The TALE-homeodomain-encoding pbx and prep genes are required for regeneration and maintenance of anterior PCG expression , including for markers of the anterior pole [17] , [19] , [20] . Because prep is expressed broadly at the head tip and pbx in most cells of the regenerating head [17] , [19] , [20] , it was not previously possible to determine whether poles promoted anterior PCG expression or vice versa . However , because FoxD expression is restricted to the regenerating pole , the FoxD RNAi phenotype described above suggests the pole is required for anterior PCG expression . This model predicts that pbx and prep might be required for FoxD expression . Wound-induced FoxD expression at six hours following injury was normal in both pbx and prep RNAi animals ( Fig . 7A ) . Similarly , other wound-induced genes ( notum and wnt1 ) are expressed normally in pbx ( RNAi ) animals [19] , [20] , indicating that pbx and prep act downstream of wound-induced expression of these genes . By contrast , anterior pole-specific expression of FoxD and notum was completely absent in both pbx and prep RNAi animals 72 hours following head amputation ( Fig . 7B ) . Because both FoxD and notum expression is induced in neoblasts at 72 hours following amputation of wild-type animals , the complete absence of expression of these two genes at this time point suggests a requirement for pbx and prep in the specification of neoblasts into anterior pole progenitors and that this defect might underlie the pbx and prep phenotypes . The restricted , wound-induced expression of FoxD in the midline raises the possibility of a regenerative connection between the midline and the anterior pole . To explore this possibility further , we analyzed the expression pattern of several midline genes , such as slit , admp , and ephR1 , in FoxD ( RNAi ) animals . Indeed , these midline genes were not properly expressed in regenerating FoxD ( RNAi ) anterior blastemas ( Fig . 5D ) . In addition , parasagittal thin pieces showed reduced slit expression at the anterior midline ( Fig . S6B ) . By contrast , tail blastemas of transversely amputated FoxD ( RNAi ) animals displayed normal extension of slit-expressing cells to the posterior pole ( Fig . S4C ) , further demonstrating the specific role of FoxD in the biology of the anterior pole . To further investigate the possible connection of the regenerating anterior pole and the midline , we examined the midline-expressed slit and ephR1 genes in pbx and prep RNAi animals , which are unable to regenerate an anterior pole ( Fig . 7B and [19] , [20] ) . pbx ( RNAi ) animals have been reported to have defective slit expression in anterior and posterior blastemas [19] , [20] . As predicted , expression of both slit and ephR1 was defective in anterior blastemas of pbx and prep RNAi animals seven days following head amputation ( Fig . 7B ) . Moreover , the midline expression of ephR1 was defective in all ptc ( RNAi ) animals showing intermediate phenotypes ( cyclopia and headless; and therefore , reduced or absent anterior pole ) at seven days following head amputation ( Fig . S3D ) ; because of the broad role of ptc it is unknown whether this defect is solely explained by a pole defect in ptc ( RNAi ) animals . hh ( RNAi ) animals , which can regenerate a normal anterior pole ( Fig . S3B ) , showed proper expression of the ephR1 gene in anterior blastemas at seven days following amputation ( Fig . S3B ) . Intact FoxD ( RNAi ) animals undergoing long-term RNAi did not show an obvious phenotype ( Fig . S6A ) ; following transverse amputation of these animals , small blastemas with one or no eyes were regenerated , and the anterior pole was rarely formed or very reduced ( Fig . S6C , D ) . In some of these amputated FoxD ( RNAi ) animals a small cluster of anterior pole cells were regenerated but offset from the midline ( n = 5/19 offset , with 14/19 showing medial but severely reduced poles , Fig . S6D ) . Altogether , our results suggest a role for the anterior pole in organizing the regeneration of the new midline and patterning of the head .
In the first phase , the anterior wound response ( 3–18 hours post-amputation ) , signaling mechanisms occurring in pre-existing muscle cells at wounds determine whether a new head is to be regenerated . Wound signaling triggers rapid ( within ∼6 hours ) expression of wnt1 at all wounds [37] . Wnt signaling is selectively inhibited at anterior-facing wounds , involving activation of the notum gene [8] . The mechanism that leads to selectivity in notum activation is unknown . This first phase of head regeneration results in inhibition of Wnt signaling at anterior-facing wounds , whereas Wnt signaling is active at posterior-facing wounds . FoxD is activated at the midline of most wounds during this initial phase of wound-induced wnt1 and notum expression , but our data do not indicate a role for this gene in the decision to regenerate a head-versus-tail . If an anterior-facing wound is not juxtaposed by anterior tissue , a second phase of head regeneration involving anterior pole progenitor specification ensues . This phase ( 24–72 hours post-amputation ) involves formation of a new anterior pole in an emerging blastema . The initial medial FoxD expression presents a candidate mechanism for establishing the location of pole regeneration–at the prior midline . This location of FoxD induction highlights the connection between the midline and pole regeneration as an important area for further investigation . We found that a small cluster of cells at the midline expressing notum , follistatin , and FoxD emerges from neoblasts in this second phase . Neoblasts can be specified to form eyes [48] or protonephridia [49] in regeneration , and here we demonstrate that some neoblasts near the forming anterior pole express FoxD , notum , and prep . We propose that neoblast induction of these genes near the midline at wounds represents an initial cellular step in formation of the cells of the new anterior pole . In a third phase of head regeneration ( ∼48 hours+ post-amputation ) , as the blastema grows substantially , pattern of the head blastema is established . FoxD RNAi severely reduced anterior pole regeneration . Whereas these FoxD ( RNAi ) anterior blastemas still displayed anterior character ( such as the presence of brain cells ) , the blastemas lacked the normal tissue organization of wild-type head blastemas . FoxD ( RNAi ) blastemas also had defects in the expression of a number of anterior PCGs with broad anterior expression domains ( e . g . , prep , sFRP-1 ) . This raises the possibility that the anterior pole is involved in establishing and maintaining more general anterior patterns of gene expression during this third phase of head regeneration . Consistent with this hypothesis , head tissue can also be regenerated in pbx or prep RNAi animals lacking poles , but establishment of anterior patterning gene expression domains is severely affected [17] , [19] , [20] . Finally , expression domains of genes expressed at the midline were aberrant in FoxD ( RNAi ) head blastemas . Together , these results suggest that the regenerating anterior pole promotes midline regeneration for proper bilateral patterning of the head and promotes establishment of gene expression domains for AP patterning of the head .
Asexual Schmidtea mediterranea strain ( CIW4 ) animals starved 7–14 days prior experiments were used . Animals were exposed to a 6 , 000 rads dose of radiation using a dual Gammacell-40 137 cesium source and amputated three days after irradiation . No vertebrate animals were used in this study , and usage of planarians ( invertebrates ) is unregulated . Animals were injected with control ( the C . elegans gene unc-22 ) , FoxD , or pbx dsRNA . Animals were transversely amputated and trunk pieces injected within 1 hour post-amputation with dsRNA . A booster dsRNA injection of trunk pieces was performed the following day . This procedure ( amputation , injection and booster injection ) was performed a total of three times , every two to three days . Following the third cycle of amputation and injections , animals were scored for phenotype at 72 hours and seven days post amputation , fixed and analyzed with in situ hybridizations [50] and for many experiments involved azide quenching as described [50] . For RNAi by feeding experiments , dsRNA-expressing bacteria cultures were mixed with 70% liver solution in a 1∶300 ratio to culture volume . β-catenin , APC , and prep RNAi animals have been fed four times ( days 0 , 4 , 7 and 11 ) , and amputated at d12 . 72 hours or seven days following amputation , animals were fixed and in situ hybridizations performed . hh and ptc RNAi animals were fed six times every three to four days . Long-term FoxD ( RNAi ) homeostasis experiments were performed by feeding the animals every three to four days during a ten week-period . Animals were wounded and fixed at six hours following injury in 4% formaldehyde [50] and nitroblue tetrazolium/5-bromo-4-chloro-3-indolyl phosphate ( NBT/BCIP ) colorimetric whole-mount in situ hybridizations or fluorescence in situ hybridizations ( FISH ) were performed as described [50] . For immunostainings , animals were fixed as for in situ hybridizations and then treated as described [51] . A mouse anti-ARRESTIN antibody was kindly provided by Kiyokazu Agata and used in a 1∶5 , 000 dilution , and an anti-mouse-Alexa conjugated antibody was used in a 1∶500 dilution . For the neoblast wound response assay , RNAi animals were fed during the course of eight weeks every three to four days , then were transversely amputated and trunk or tail fragments were fixed at 0 , 6 , 18 , 48 , and 120 hours following wounding . Animals were immunostained using a rabbit anti-phospho histone 3 antibody and an anti-rabbit HRP in a 1∶100 dilution as previously described [52] . Fluorescent images were taken with a Zeiss LSM700 Confocal Microscope . Light images were taken with a Zeiss Discovery Microscope . | Regeneration is widespread in the animal kingdom . Planarians are able to regenerate entire bodies from almost any fragment type . This ability requires a cell population called neoblasts , which include pluripotent stem cells , for the production of all missing tissues , as well as the information to form and pattern correct new tissue types . Two discrete regions of the body , called poles , are found at the anterior and posterior ends of the animal . Here we investigate the role of a gene encoding a Forkhead-family transcription factor , FoxD , in formation of the anterior pole . FoxD is expressed at the anterior pole and following injury , FoxD expression is induced in a restricted midline region of the animal . Next , FoxD is expressed in a subset of neoblasts at the midline . Inhibition of FoxD with RNA interference results in defective anterior pole regeneration , and subsequent failure to regenerate an organized head pattern around a new midline . FoxD is specifically required for anterior regeneration . These results suggest that there is a regenerative connection between the midline and the anterior pole . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"developmental",
"biology",
"biology",
"model",
"organisms"
] | 2014 | A forkhead Transcription Factor Is Wound-Induced at the Planarian Midline and Required for Anterior Pole Regeneration |
The ability to respond to environmental temperature variation is essential for survival in animals . Flies show robust temperature-preference behaviour ( TPB ) to find optimal temperatures . Recently , we have shown that Drosophila mushroom body ( MB ) functions as a center controlling TPB . However , neuromodulators that control the TPB in MB remain unknown . To identify the functions of dopamine in TPB , we have conducted various genetic studies in Drosophila . Inhibition of dopamine biosynthesis by genetic mutations or treatment with chemical inhibitors caused flies to prefer temperatures colder than normal . We also found that dopaminergic neurons are involved in TPB regulation , as the targeted inactivation of dopaminergic neurons by expression of a potassium channel ( Kir2 . 1 ) induced flies with the loss of cold avoidance . Consistently , the mutant flies for dopamine receptor gene ( DopR ) also showed a cold temperature preference , which was rescued by MB–specific expression of DopR . Based on these results , we concluded that dopamine in MB is a key component in the homeostatic temperature control of Drosophila . The current findings will provide important bases to understand the logic of thermosensation and temperature preference decision in Drosophila .
To counteract the harmful effects of environmental temperature fluctuation , animals use homeostatic thermoregulatory mechanisms and/or behavioral responses to maintain optimal body temperature . Mammals regulate their body temperatures with various physiological responses such as change of metabolic rates , vasomotor control , and sweating . Due to the large surface area-to-volume ratio of its body , Drosophila maintains body temperature by heat exchange with the surrounding environment . In a genetically controlled process , flies track to an environmental temperature as close as possible to their desired body temperature [1]–[3] . The behavioural responses in Drosophila can be recorded easily by examining TPB on a thermal gradient plate [1] , [4] , [5] . Wild type fruit flies show a strong response to external temperature and robust TPB peaking at 24°C on the temperature gradient [4] . To identify the molecular components and neural circuits regulating thermosensation and body temperature regulation , various studies such as calcium imaging , genetic screens , and electrophysiology have been done in fruit flies , C . elegans , mice , and cultured cells [6]–[12] . From these , various thermo-transient receptor potential ( TRP ) channels and cyclic nucleotide-gated channels have been found to be molecular sensors of temperature . The perceived temperature information generated in neurons bearing these sensor molecules is transmitted to a higher order brain center . Here , the sensed temperature is compared to a set point temperature to determine whether it is too low or too high [2] . In vertebrate , the hypothalamic preoptic area is known to be the higher order brain center interpreting the temperature information [13] . In Drosophila , the temperature that is instinctively preferred plays a role equivalent to the set point temperature in mammals . Three thermo-TRP channels have been reported as molecular sensors of hot or warm temperature in Drosophila . Painless , a thermo-TRP channel , is important for avoidance of hazardous heat above 40°C [14] . Pyrexia , another thermo-TRP channel , is activated around 40°C and acts to prevent paralysis during high temperature stress [15] . dTRPA1 is activated above 27°C and essential for avoidance of warm temperature , and the loss-of-function mutants of dTRPA1 prefer warm temperature [16] . In addition to the discovery of these thermo-TRP channels , various genetic studies to understand the brain center , and the responsible neuromodulators and neural circuits of TPB in Drosophila have been actively pursued [1] , [5] , [16] . The adult Drosophila brain includes about 100 , 000 neurons whose projections cluster in neuropil structures to make two symmetric hemispheres [17] . The central complex and the mushroom body ( MB ) are two prominent structures in the central brain . The central complex is located centrally between the two hemispheres and consists of four interconnected neuropils: the ellipsoid body , the fan-shaped body , the protocerebral-bridge , and the nodule [18] , [19] . The central complex is involved in behaviours such as locomotion , visual flight , courtship , and olfactory learning task [20] , [21] . However , MB is involved in behaviours like sleep [22] , decision making [23] , and learinig [24] . Recently we showed that MB acts as a higher order brain center regulating TPB in Drosophila [1] . MB is made up of approximately 2 , 500 Kenyon cells in each hemisphere , which send out dendrites to the calyx [24] . Kenyon cells are grouped into one of three neuron classes , whose axons form morphologically and functionally distinct lobes of the αβ , α′β′ and γ lobes . For TPB , the αβ lobes are necessary while the γ lobe is dispensable [1] . We also showed that the cAMP-PKA signalling pathway is the key regulator working inside of MB to determine the temperature that Drosophila prefers [1] . Fifteen clusters of six-hundred dopaminergic neurons are distributed in the Drosophila brain [25] , [26] . The dopaminergic neurons are broadly and stereotypically localized , and show particularly dense projections into MB [27] . For example , protocerebral posterior lateral 1 cluster which innervates MB is reported as routes for reinforcement and retrieval in memory formation [28] , [29] . Similar to mammals , dopamine receptors are divided into two major subfamilies in Drosophila . Two D1-like receptors , DopR and DpoR2 , and a D2-like receptor , D2R , have been identified [30] . Interestingly , DopR is highly expressed in MB and the central complex [31] , and mediates learning [32] , [33] , caffeine-induced wakefulness [34] , arousal [35] , and ethanol-stimulated locomotion [36] . DopR2 is expressed in MB and is involved in larval aversive olfactory learning [37] , [38] . D2R is expressed in some discrete cells in central nervous system and regulates locomotor activity [30] . However , it has remained unknown that dopamine is involved in TPB regulation in Drosophila . To uncover the relationship between the dopamine system and TPB , we performed various genetic studies in Drosophila . Dopamine synthesis and dopaminergic neurons are critical in TPB regulation . In addition , we showed that DopR is essential to control TPB in the αβ lobes of MB . These results strongly suggested that dopamine is an important neuromodulator for TPB in Drosophila .
Our previous results have shown that cAMP signalling in MB modulates TPB [1] . Since dopamine is an important modulator of cAMP [39] , [40] and dopaminergic neurons have a strong innervation profile in MB [27] , we suspected that the dopamine system can modulate TPB in Drosophila . The enzymatic reaction of tyrosine hydroxylase ( TH ) , which converts tyrosine to L-DOPA ( Figure 1A ) , is the rate limiting step in dopamine biosynthesis [25] . TH is encoded by pale locus in Drosophila . It is also known that loss of one copy of the TH gene can cause severe reduction of dopamine levels in the animal brain [25] . This led us to test a pale mutant for TPB as the first step to explore the involvement of dopamine signalling in the regulation of TPB . In the TPB assay , the experimental procedure was described previously [1] . The Avoidance Index against Low Temperatures ( AILow ) and Avoidance Index against High Temperatures ( AIHigh ) were calculated using the formulas shown in Figure 1B [1] , [6] . The pale4/+ heterozygote showed abnormal TPB and the animals spread over a wide range of temperature colder than 24°C ( Figure 2A ) . Their AILow was less than 34% of the wild type control w1118 , while AIHigh was normal ( Figure 2A and Table S1 ) . To reduce the biosynthesis of dopamine further , we fed pale4/+ flies with a TH inhibitor , α-methyl-p-tyrosine methyl ester ( AMPT ) , for 4 days . In the TPB assay , these flies showed a more severe defect in their ability to avoid low temperature ( Figure 2A ) . The flies spread almost randomly over all temperature ranges below 24°C ( Figure 2A ) . The AILow of the flies was below 0 ( Figure 2A and Table S1 ) . On the other hand , the reduced AILow of pale4/+ flies was ameliorated by introducing a genomic DNA fragment of the pale locus into the pale4/+ flies ( Figure 2B ) . The genomic DNA fragment of the pale locus was reported to rescue pale flies from lethality [41] . The AILow of pale4/+ flies with the pale genomic DNA was 91% of wild type ( Figure 2B and Table S1 ) . This confirmed that the abnormal TPB of the pale4/+ flies was indeed resulted from a defect in the TH gene . These results strongly suggested that dopamine is a key neurotransmitter controlling fly temperature preference . To convince ourselves further that dopamine is involved in TPB regulation , we examined a dopa decarboxylase ( DDC ) mutant . DDC converts L-DOPA to dopamine ( Figure 1A ) . As shown in Figure 2C , flies homozygous for a DDC hypomorphic allele DdcDE1 showed reduced cold avoidance . The AILow of DdcDE1/DdcDE1 flies was 26% of wild type ( Figure 2C and Table S1 ) . To examine whether further reduction of DDC activity worsens the ability of flies to avoid cold temperature , we fed the DdcDE1/ DdcDE1 flies with 3-hydroxy benzyl hydrazine ( HBH ) . HBH is a potent DDC inhibitor [42] . As expected , the DDC mutant flies fed HBH showed almost complete loss of cold avoidance in the TPB assay ( Figure 2C ) . Some flies even moved to the coldest region of the temperature gradient plate ( Figure 2C ) . The AILow of these flies was −0 . 23 ( Figure 2C and Table S1 ) . These data suggested that dopamine biosynthesis is critical for flies to avoid cold temperature . This was supported further by over expression experiments . When TH or DDC was over expressed separately in dopaminergic neurons , the flies showed normal TPB ( Figure 3A , 3B and Table S2 ) . However , when TH and DDC were over expressed together in dopaminergic neurons , the flies became thermophilic or hypersensitive to cold temperature ( Figure 3A and 3C ) . Their AILow was higher than that of wild type while their AIHigh was only 39% of wild type ( Figure 3C and Table S2 ) . To gain more evidence of TPB control by dopamine , we examined whether the normal function of dopaminergic neurons is essential for normal TPB . We over expressed tetanus toxin ( TNT ) and potassium channel Kir2 . 1 in dopaminergic neurons with TH-Gal4 and dopamine transporter ( DAT ) -Gal4 . TNT only blocks the neural activity requiring n-Syb-dependent synaptic vesicle fusion [43] , while Kir2 . 1 hyperpolarizes all neurons and suppresses action potential firing [43] , [44] . Unfortunately , Kir2 . 1 when combined with TH-Gal4 or DAT-Gal4 killed flies before they hatched . In contrast , TH-Gal4>TNT and DAT-Gal4>TNT flies were viable and both spread over temperature ranges broader than wild type , mostly over the areas colder than 25°C ( Figure 4A , 4B and Table S3 ) . To exclude the possibility of developmental defects induced by TNT and Kir2 . 1 , we combined UAS-TNT and UAS-Kir2 . 1 with the Temporal and Regional Gene Expression Targeting system ( TARGET ) [45] , which employs temperature-sensitive Gal80 protein ( Gal80ts ) under the control of tubulin promoter ( tub-Gal80ts ) . In this system , the transcriptional activator of Gal4 is suppressed at 18°C but active at 32°C . As expected , TH-Gal4>TNT flies with tub-Gal80ts showed normal TPB when they were raised continually at 18°C ( Figure 5A and 5C ) . However , they unexpectedly showed normal TPB even after exposure to 32°C for 16 hr to induce TNT before the TPB assay ( Figure 5B and 5D ) . We obtained similar results when we used DAT-Gal4 instead of TH-Gal4 ( data not shown ) . It might be possible that temporal induction of TNT for 16 hr in the adult stage does not accumulate enough TNT to inactivate dopaminergic neurons sufficiently , while life-time induction of TNT with TH-Gal4 or DAT-Gal4 without Gal80ts does accumulate sufficient TNT . It is also known that TNT might not block synaptic vesicle release strongly enough to block all neuronal activities [43] , [46] . On the other hand , it cannot be excluded that the abnormal TPB of TH-Gal4>TNT and DAT-Gal4>TNT flies shown in Figure 4A and 4B was resulted from developmental defects induced by TNT . Because we obtained unexpected data from TH-Gal4>TNT;tub-Gal80ts/+ or DAT-Gal4>TNT;tub-Gal80ts/+ flies , we used UAS-Kir2 . 1 instead of TNT in combination with TH-Gal4;tub-Gal80ts . Kir2 . 1 is a stronger neuronal inactivator than TNT [43] . When TH-Gal4>Kir2 . 1;tub-Gal80ts/+ flies were raised continually at 18°C , they showed expected normal TPB ( Figure 5A and 5E ) . However , when the flies were raised at 18°C until 3-day-old adult and exposed to 32°C for 16 hr just before the TPB assay , they lost cold avoidance severely . Most of the flies moved towards the 15°C area , which is the low temperature limit of the assay apparatus and their AILow was −0 . 2 ( Figure 5B , 5F and Table S4 ) . Similar TPB was also observed when DAT-Gal4 was used instead of TH-Gal4 ( Figure 5G and 5H ) . DAT-Gal4>Kir2 . 1;tub-Gal80ts/+ flies spread over an area of cold temperature when they were exposed to 32°C for 16 hr before the TPB assay ( Figure 5B and 5H ) . From the above results , we concluded that defective dopamine biosynthesis or inactivation of dopaminergic neurons causes the fly has abnormal TPB . These results strongly suggested that dopamine signalling controls TPB in Drosophila . We next examined whether the activities of dopamine receptors are required for TPB . Drosophila has three subtypes of dopamine receptor; DopR , DopR2 , and D2R [30] . dumb3 is a hypomorphic allele of DopR , in which most MB expression of DopR was removed by a P element insertion [33] . dumb3/dumb3 homozygote flies exhibited reduced ability to avoid cold temperature ( Figure 6A ) . Their AILow was lower than 50% of wild type ( Figure 6A and Table S5 ) . Consistently , flies having one copy of a deficiency chromosome Df ( 3R ) red-P52 , in which DopR is deleted , preferred temperatures colder than normal ( Figure 6B ) . The axis of distribution profile of flies over the temperature gradient was shifted from 24°C to 21°C ( Figure 6B ) . More convincingly , when this deficiency chromosome was combined with dumb3 chromosome , resulting in the genotype of dumb3/Df ( 3R ) red-P52 , the flies lost cold avoidance furthermore ( Figure 6C ) . Their AILow was 23% of wild type ( Figure 6C and Table S5 ) . There are no available mutant alleles for DopR2 and D2R . Therefore , we employed dsRNAi technology with and without UAS-Dcr2 , which is known to enhance the effect of dsRNAi in neurons [47] . However , none of the RNAi transgenes reduced the transcript levels of these two receptors by more than 40% and the flies had normal TPB . Accordingly , we could not ascertain whether DopR2 or D2R is required for TPB control . Taken together , our results shown above demonstrated that dopamine signalling is a key factor controlling TPB in Drosophila . This finding raised the question as to which brain parts or neurons are the targets of dopamine signalling in TPB control . A clue was obtained from the results that we reported recently , which showed that MB is the brain center controlling TPB [1] . Importantly , DopR and DopR2 are expressed in MB [31] . However , they are also expressed in other Drosophila nerves , including the central complex and several neuro-secretory cells in the adult brain [31] . We confirmed that DopR is highly enriched in MB and DopR is also expressed in the central complex ellipsoid body , fan-shaped body , and nodules as previously reported ( Figure 7A and [31] ) . In dumb3/dumb3 homozygote flies , DopR gene expression was strongly reduced in overall MB and the central complex ( Figure 7B ) . To examine if MB is the location where DopR regulates TPB , we generated flies in which DopR expression is maintained in MB . This was achieved by ectopic expression of the UAS-DopR transgene with c309 or MB247 in the DopR mutant background of dumb3/dumb3 ( Figure 7C and 7D ) . c309 and MB247 drive gene expression specifically in MB . As shown in Figure 7C , dumb3/dumb3 flies spread widely over the temperature gradient lower than 25°C . In contrast , the artificial restoration of DopR levels with c309>UAS-DopR transgene enabled dumb3/dumb3 to gather sharply around the 25°C area ( Figure 7C ) . In other words , the flies displayed almost completely restored normal TPB . The AILow of the flies was 99% of wild type ( Figure 7C and Table S6 ) . Consistently , c309>DopR;dumb3/dumb3 flies also showed rescued TPB compared with c309/+;dumb3/dumb3 and UAS-DopR/+;dumb3/dumb3 flies ( Figure S1A ) . This means that the abnormal TPB of dumb3/dumb3 homozygote flies was caused by reduced expression of DopR in the region of c309 expression and that MB may be the location where DopR regulate TPB . However , we could not completely exclude the possibility that the central complex participated in the TPB regulation of DopR as the expression of c309 was observed in other regions of the brain including the central complex ( Figure S2A and [48] ) . To confirm the specific role of MB , we next tested MB247 which strongly induces gene expression in the αβ and γ lobes with very low background expression ( Figure S2B and [49] ) . When we used MB247 instead of c309 ( Figure 7D , Figure S1B , and Table S6 ) , MB247> DopR;dumb3/dumb3 flies also regained preference for 25°C , which is the normal preferred body temperature ( Figure 7D and Figure S1B ) . They showed the AILow and AIHigh values comparable to those of wild type flies ( Figure 7D and Table S6 ) . This means that the restoration of DopR levels only in MB enabled dumb3/dumb3 to behave like wild type flies . These data indicated that dopamine signalling in MB is critical for TPB . In the same way , we generated flies to rescue the cryophilic TPB of Df ( 3R ) red-P52/+ by ectopic expression of UAS-DopR with MB-specific Gal4 . Df ( 3R ) red-P52/+ flies preferred temperature colder than normal and AILow was severely lower and AIHigh was higher than wild type ( Figure 8A ) . In contrast , c309>DopR;Df ( 3R ) red-P52/+ flies showed almost normal TPB ( Figure 8A ) . The shifted axis of their distribution profile returned to 24°C , and their AILow and AIHigh showed no difference with the avoidance indices of wild type control w1118 ( Figure 8A and Table S7 ) . Consistently , c309>DopR;Df ( 3R ) red-P52/+ flies also showed rescued TPB compared with c309/+;Df ( 3R ) red-P52/+ and UAS-DopR/+;Df ( 3R ) red-P52/+ flies ( Figure S1C ) . Similarly , MB247>DopR;Df ( 3R ) red-P52/+ flies also displayed normal TPB ( Figure 8B , Figure S1D , and Table S7 ) . These data suggested that the TPB phenotype of Df ( 3R ) red-P52/+ flies is caused by reduced expression of DopR in MB . These results demonstrated again that DopR regulates TPB in MB . As described above , both c309 and MB247could restore the TPB of dumb3/dumb3 with UAS-DopR ( Figure 7C and 7D ) . c309 induces gene expression strongly in the αβ and γ lobes , but weakly in the α′β′ lobes ( Figure S2A and [49] ) . MB247 induces gene expression in the αβ and γ lobes but not in the α′β′ lobes ( Figure S2B and [49] ) . To investigate which lobe of MB is relevant to the DopR-mediated regulation of TPB , we used two more MB-specific Gal4 lines . c739>DopR;dumb3/dumb3 showed restored TPB and avoidance index ( Figure 9A and Table S8 ) . c739 is expressed in the αβ lobes ( Figure S2C and [50] ) . However , 1471> DopR did not rescue the phenotype of dumb3/dumb3 . The flies spread widely over the low and intermediate regions like dumb3/dumb3 and AILow was not restored to the level of wild type control ( Figure 9B and Table S8 ) . This 1471 induces gene expression in the γ lobe ( Figure S2D and [51] ) . Taken together , the results suggested that the αβ lobes are relevant to DopR-mediated regulation of TPB and the γ lobe is dispensable . Finally , we examined whether the dopamine signalling in the central complex is required for normal TPB using two more Gal4 lines . c161 is expressed in the ellipsoid body of the central complex ( Figure S2E and [18] ) . The ectopic expression of UAS-DopR by c161 in dumb3/dumb3 did not rescue the defective TPB . c161>DopR;dumb3/dumb3 flies spread over the areas colder than 25°C like dumb3/dumb3 and the lowered AILow of dumb3/dumb3 was not restored ( Figure 10A and Table S9 ) . OK348 is expressed in the fan-shaped body ( Figure S2F and [52] ) . OK348>DopR;dumb3/dumb3 flies also showed loss of cold avoidance in the TPB assay and AILow was low like dumb3/dumb3 ( Figure 10B and Table S9 ) . These implicated that the ellipsoid body and the fan-shaped body are not relevant to DopR-mediated regulation of TPB .
Monoamine neurotransmitters such as dopamine , norepinephrine , and serotonin have been identified as important factors in thermoregulation in mammals [53]–[56] . However , the exact functions of these monoamines in body temperature control are not clearly understood due to limited experimental methods in mammalian systems . Fortunately , our current study clearly revealed that dopamine plays a critical role in regulating TPB of Drosophila . Dopamine-deficient mutants and dopaminergic neuron-inactivated flies lost cold avoidance significantly ( Figure 2 , Figure 4 , and Figure 5 ) . However , the flies overexpressing TH and DDC together in dopaminergic neurons became thermophilic or hypersensitive to cold temperature ( Figure 3 ) . This suggests that dopamine levels are critical to regulate TPB; high dopamine levels lead to warm temperature preference and low dopamine levels lead to cool temperature preference . In this study , we have shown evidence to support that the dopamine signaling in MB regulates TPB . We also demonstrated previously that cAMP signaling in MB modulates TPB; flies with low levels of cAMP prefer cold and the flies with high levels of cAMP prefer warm temperature [1] . In addition , there have been reports that dopamine regulates cAMP levels [39] and that dopamine stimulation of Drosophila brain leads to activation of PKA specifically in the α lobe [40] . Therefore , we propose that when a distinctive subset of dopaminergic neurons which innervate the TPB center in MB is activated , cAMP levels and PKA activity can be increased in the center . Activated PKA may phosphorylate and regulate various targets including ion channels and also induce gene expression of unidentified TPB regulatory genes with CREB binding sites . These PKA-dependent gene expression and posttranslational regulation would change the activities of the TPB center in MB , and fly can determine specific temperature preference or avoidance behaviours . Consistently , our unpublished data strongly suggest that a specific gene group induced by PKA-CREB signalling in the brain is critical for Drosophila TPB . Previously , it was reported that MB is important for both cold and warm temperature avoidance and preference [1] . Interestingly , all the flies with defective dopamine signalling showed a significant loss in cold avoidance , but some of them also showed loss of warm avoidance ( Figure 2 , Figure 4 , Figure 5 , Figure 6 ) . However , the loss of warm avoidance was not severe as MB-defective flies and dTRPA1-deficient flies [1] , [16] . We do not think that only dopamine regulates TPB in MB sufficiently . It is possible that other neuromodulators and/or multiple innervated neurons may be involved in modulation of the MB process to regulate TPB . The additive or synergistic effect of dopamine and these modulators may regulate cAMP levels in MB and TPB with broader temperature ranges as shown in MB-defective mutants or cAMP signaling-disrupted flies [1] . It is also possible that dopaminergic neurons are involved in transmitting temperature information to MB . It was reported that TRP and TRPL are required for avoidance of cold temperature in larvae [57] , and their cold avoidance phenotypes resemble the TPB of dopamine signalling-deficient flies . Understanding whether the neuronal pathway for the cold avoidance of TRP and TRPL is connected to the dopaminergic pathway of MB will be helpful to understand the regulatory mechanism of TPB . In another report , the flies with surgically removed third antennal segment and aristae showed loss in cold avoidance [16] . Therefore , it is also possible that dopaminergic neurons are involved in transmitting the sensory signals to MB from sensory tissues like the third antennal segment and aristae . Although three subtypes of dopamine receptors are known in Drosophila , we found that DopR in MB is involved in TPB regulation while DopR in the ellipsoid body and the fan-shaped body is not essential for TPB control . This is consistent with the previous study that these substructures of the central complex are not involved in TPB control [1] . According to the previous results from others and our current data , DopR regulates multiple Drosophila behaviours via distinct neural circuits . DopR in the ellipsoid body is required to promote ethanol-stimulated locomotion and to regulate exogenous arousal negatively , while DopR in PDF-expressing circadian pacemaker cells is needed to regulate endogenous arousal positively [35] , [36] . Moreover , DopR in MB is required in learning and memory [32] . In addition , all parts of MB may not work for TPB regulation; DopR in the αβ lobes of MB is relevant but this in the γ lobe is dispensable , which is also consistent with the previous report [1] . In conclusion , our results strongly suggest that dopamine controls TPB and body temperature in Drosophila . We believe that our findings provide important clues to understand the molecular mechanism of Drosophila TPB . Although further studies are required , we cautiously suggest that our fruit fly system can provide a highly useful model to further understand the physiological roles of dopamine in animal body temperature regulation .
Fly stocks were raised on standard cornmeal food at 25°C and 40%–50% relative humidity . Canton-S and w1118 flies all showed a strong temperature preference for 24–25°C regardless of the temperatures at which they were reared in every TPB tests [1] , [4] . w1118 was shown as the wild type Drosophila strain in the figures . Fly lines were provided as follows: pale4 ( Bloomington , 3279 ) , DdcDE1 ( Bloomington , 3168 ) , TH-Gal4 ( Serge Birman ) , UAS-TH , UAS-DDC ( Sean B . Carroll ) , UAS-TNT ( Cahir J . O'Kane ) , UAS-Kir2 . 1 ( Amita Sehgal ) , UAS-Kir2 . 1/CyO; tub-Gal80ts ( Hiromu Tanimoto ) , dumb3 ( Bloomington , 19491 ) , Df ( 3R ) red-P52 ( Bloomington , 3484 ) , ELAV-Gal4 ( Bloomington , 458 ) , MB247 ( Troy Zars ) , UAS-DopR2 RNAi ( VDRC , 3392 ) , UAS-D2R RNAi ( VDRC , 11471 ) , c309 , c739 , OK348 ( Leslie C . Griffith ) , 1471 ( Bloomington , 9465 ) , c161 ( Bloomington , 27893 ) . Genomic rescue transgenic fly of pale locus ( P{ple+8} ) was obtained from Kalpana White [41] . DAT-Gal4 was generated as reported previously [58] . The UAS-DopR construct was generated as follows: a 1 . 8 Kb fragment including DopR coding sequences was obtained from RT-PCR of fly adult head mRNA with the primers tcgctgaaaagagggaagcaa and cagtaggtagagggctggg; the fragment was cloned into pGEM-T Easy vector ( Promega ) and sequenced , and the fragment was transferred into NotI and XbaI sites of pUAST vector . We produced the UAS-DopR transgenic flies with standard microinjection methods . TPB was performed as described previously [1] . Files were incubated under 12 hr∶12 hr dark:light cycle for 4 days before TPB assays . A linear temperature gradient from 15 to 45°C was established along the aluminium block . All TPB assays were done in a room with 35±5% relative humidity . The AILow and AIHigh were calculated as shown in Figure 1B . A mixed population of both sexes was tested for TPB . The number of flies was recorded and processed using Microsoft Excel . The AILow or AIHigh of each tested fly line was compared with the AILow or AIHigh of the control fly lines and then t-tests were performed . * , p<0 . 05; ** , p<0 . 01 . Two to three days old flies were raised on drug-containing food for 4 additional days and tested . Melted pre-made standard dextrose media were blended with drugs or the same amount of distilled water , their solvent . As the final drug concentrations , 20 mM α-methyl-p-tyrosine methyl ester ( AMPT ) ( Sigma ) and 20 mM 3-hydroxy benzyl hydrazine ( HBH ) ( Sigma ) were used . The standard dextrose media contain dextrose 70 g , yeast flake 50 g , cornmeal 35 . 3 g , agar 5 g , tegosept 7 . 3 ml and propionic acid 4 . 7 ml in 1 L total volume . To induce TNT and Kir2 . 1 expression conditionally in dopaminergic neurons , we crossed UAS-TNT; tub-Gal80ts or UAS-Kir2 . 1/CyO; tub-Gal80ts flies with TH-Gal4 and DAT-Gal4 flies . These flies were incubated and transferred to new culture bottles periodically at 18°C . Eclosed F1 progenies of TH-Gal4>TNT;tub-Gal80ts/+ , TH-Gal4>Kir2 . 1;tub-Gal80ts/+ , or DAT-Gal4>Kir2 . 1;tub-Gal80ts/+ were collected and aged for 3 days at 18°C before the TPB assay . For TNT or Kir2 . 1 induction , the flies were transferred from 18°C to 32°C and incubated for 16 hr before the TPB assays . Heterozygous Gal4 flies and UAS-TNT/+;tub-Gal80ts/+ or UAS-Kir2 . 1/+;tub-Gal80ts/+ files were used as controls . To confirm various brain-specific Gal4 fly lines ( c309 , MB247 , c739 , 1471 , c161 and OK348 ) , they were combined with UAS-LacZ . Adult brains were removed from the head capsules and fixed in 4% paraformaldehyde in phosphate buffered saline ( PBS ) for 30 min , and rinsed in PBST ( PBS containing 0 . 5% triton X-100 ) . Brains were incubated with anti-β-galactosidase ( 1∶1000; Promega ) and then incubated with the appropriate red fluorescent labelled secondary antibody ( Jackson Laboratories ) . To verify the DopR expression in w1118 and dumb3/dumb3 fly brains , rabbit anti-DopR antibody ( 1∶1250 , From Wolf FW [36] ) was used . Confocal analysis was performed on a Zeiss LSM5 microscope . | Temperature affects almost all aspects of animal development and physiological processes . The dependence of the body temperature of small insects on ambient temperature and other heat sources makes it plausible that neuronal mechanisms for sensing temperature and behavioral responses for maintaining body temperature in a permissive range must exist . By using the fruit fly model system and previously settled paradigms of temperature-preference test , we find that dopamine regulates temperature-preference behaviours . Wild-type flies show a strong temperature preference for 25°C , but inhibition of dopamine biosynthesis by genetic mutations or treatment with chemical inhibitors causes animals to prefer temperatures colder than normal . We also show that dopaminergic neurons are involved in the regulation of temperature-preference behaviours and that dopamine signalling in mushroom body neurons plays a critical role in regulating the behaviours . These results suggest that dopamine is a key component in the homeostatic temperature control of fruit flies . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"neuroscience/behavioral",
"neuroscience",
"neuroscience/neural",
"homeostasis"
] | 2011 | Dopamine Signalling in Mushroom Bodies Regulates Temperature-Preference Behaviour in Drosophila |
T cell populations are regulated both by signals specific to the T-cell receptor ( TCR ) and by signals and resources , such as cytokines and space , that act independently of TCR specificity . Although it has been demonstrated that disruption of either of these pathways has a profound effect on T-cell development , we do not yet have an understanding of the dynamical interactions of these pathways in their joint shaping of the T cell repertoire . Complete DiGeorge Anomaly is a developmental abnormality that results in the failure of the thymus to develop , absence of T cells , and profound immune deficiency . After receiving thymic tissue grafts , patients suffering from DiGeorge anomaly develop T cells derived from their own precursors but matured in the donor tissue . We followed three DiGeorge patients after thymus transplantation to utilize the remarkable opportunity these subjects provide to elucidate human T-cell developmental regulation . Our goal is the determination of the respective roles of TCR-specific vs . TCR-nonspecific regulatory signals in the growth of these emerging T-cell populations . During the course of the study , we measured peripheral blood T-cell concentrations , TCRβ V gene-segment usage and CDR3-length spectratypes over two years or more for each of the subjects . We find , through statistical analysis based on a novel stochastic population-dynamic T-cell model , that the carrying capacity corresponding to TCR-specific resources is approximately 1000-fold larger than that of TCR-nonspecific resources , implying that the size of the peripheral T-cell pool at steady state is determined almost entirely by TCR-nonspecific mechanisms . Nevertheless , the diversity of the TCR repertoire depends crucially on TCR-specific regulation . The estimated strength of this TCR-specific regulation is sufficient to ensure rapid establishment of TCR repertoire diversity in the early phase of T cell population growth , and to maintain TCR repertoire diversity in the face of substantial clonal expansion-induced perturbation from the steady state .
The steady state T-cell population size arises in the balance among several phenomena , including the rapid and extensive expansion of rare clones through activation-induced peripheral division and their subsequent contraction ( which constitute , in part , the T-cell immune response ) and the relatively slow turnover of a diverse pool of naive cells through continuous thymic emigration and cell death . The specific memory T cells that arise as the result of clonal expansion appear to be regulated largely independently of the naive cells [11] . T cells arising through lymphopenia-induced proliferation acquire markers that ordinarily indicate a memory phenotype and may be regulated as memory cells , though this hypothesis has not been definitively tested . Both the size and diversity of peripheral T cell populations are controlled through competition for limiting resources . ( In the interest of simplicity , we will use the term “resources” familiar from ecological population studies to refer to all of the factors that mediate growth regulation . We do not intend by this to exclude factors that are more accurately referred to as “signals” ) [5] , [13] , and may also be controlled directly by the activities of regulatory T cells [14] . It has been shown , for example , that normal T-cell population growth is dependent on stimulation by self-peptide major histocompatibility complex ( spMHC ) complexes through the TCR [15]–[18] , requiring a TCR that is specific for the spMHC complex . But T-cell population growth also depends on cytokines such as IL7 and IL15 that act independently of TCR specificity [5] , [19]–[21] . Furthermore , growth and survival in all cells require adequate space and nutrients , the utilization of which is independent of TCR specificity [22] , [23] . Our current understanding of lymphopenia-induced proliferation is due to studies in mice demonstrating that T cells divide rapidly after transfer into T cell-deficient ( usually due to RAG or CD3 deficiency ) or irradiated mice , but not after transfer to normal mice [15] , [16] . Moreover , overall T cell numbers in T cell-deficient animals after transfer and clonal expansion are similar to T cell numbers in normal animals , suggesting control mechanisms acting on total T cell numbers , rather than in a clone-specific manner . The importance of TCR specific signals has been studied at length , showing that competition within T cell clones is important in maintaining TCR repertoire diversity . It has been shown , for example , that in a T-cell-deficient host , a T cell must interact with antigen-presenting cells bearing the MHC allele responsible for that cell's thymic selection in order to proliferate [24] . In a T-cell sufficient host , such TCR-spMHC interaction is necessary for T cell survival [24] , [25] . Furthermore , naive polyclonal T cells divide when transferred to TCR-transgenic hosts , as do monoclonal T cells transferred to TCR-transgenic hosts of differing clonotype . T cells do not divide , however , in hosts of identical clonotype [26] . Mice lacking MHC class II expression do not repopulate the periphery with CD4 T cells at all , suggesting that peripheral MHC class II expression is needed for the survival of CD4 T cells [25] . In the present context it is important to note that MHC class II matching in thymic grafts for complete DiGeorge subjects is not necessary for the development of CD4 T cells [27] . TCR-nonspecific signals include cytokines such as the cytokine interleukin-7 ( IL7 ) , which is necessary for the survival of nave T cells [28]–[32] . Lymphopenia-induced proliferation of memory cells requires IL7 or IL15 [13] , [33] . T cells that have lost the ability to respond to IL7 after leaving the thymus are no longer able to proliferate , produce cytokines , or acquire memory cell phenotype [30] . In mice in which IL7 signaling has been completely abrogated , the few mature T cells found in the peripheral blood behave abnormally [30] , [34] , [35] . Experiments in IL7-receptor ( ) -deficient mice ( IL7R−/− ) have shown a reduction in T-cell capacity to proliferate upon stimulation , leading to a six- to seven-fold reduction in the frequency of clonogenic T cells compared with T cells from IL7R-sufficient mice ( IL7R+/+ ) , as well as a 50% reduction in the average clone size of single IL7R−/− T cells compared with the IL7R+/+ T cells [34] . In another study , mice lacking the interleukin 2 ( IL2 ) receptor chain ( ) and/or the Jak family tyrosine kinase ( Jak-3 ) had severe combined immune defects with lack of T lymphocyte maturation and function . This phenomenon is presumably attributable to the fact that is part of the receptor for IL7 and IL15 [36]; its loss leads to the abrogation of both of these cytokine signaling pathways [30] , [35] , and others as well . Moreover , in humans , in the absence of of or of the Jak-3 family , the periphery lacks T cells completely [37] , [38] . To assess the contributions of thymic emigration rate on the steady-state T-cell population size , Berzins et al [39] engrafted variable numbers of thymuses into mice and observed that the size of the naive T-cell population increased in proportion to the number of thymic grafts , while the size of the memory population remained unchanged . It may be of importance to note that the thymus grafts themselves produced IL7 . In humans , transplantation of thymic tissue at varying doses into complete DiGeorge anomaly subjects showed no significant effect on the nave CD4 or CD8 T cell numbers [27] . Complete DiGeorge Anomaly ( cDGA ) is a congenital condition , the hallmark of which is profound immunodeficiency arising from abnormal development of the third and fourth pharyngeal pouches resulting in thymic aplasia ( complete absence of the thymus ) . This developmental irregularity may also cause various other anatomical abnormalities including heart defects , hypoparathyroidism , and craniofacial malformations [40]–[42] . In the absence of a thymus , cDGA subjects lack thymic-derived T cells and are consequently profoundly immunocompromised . Postnatal thymus transplantation can lead to a restoration of T-cell function and the development of a peripheral T-cell population with an apparently normal T-cell receptor repertoire [43]–[45] . Thymus transplantation is emerging as a valuable treatment for athymia generally . There is therefore substantial medical interest in elucidating the immunological recovery of thymus transplant patients quite apart from the basic immunology these patients may reveal . To illustrate the connection between these alternative homeostatic mechanisms and the observations one might make on the DiGeorge subjects , consider the following two scenarios as a thought experiment . First , suppose that TCR-specific resources such as spMHC are not limiting , either because they are produced in great excess or because they are rendered unnecessary . Under these conditions , homeostasis will be due exclusively to competition for TCR-nonspecific resources . The first T cells leaving the thymus will expand rapidly , consuming the TCR-nonspecific resources required for growth of their own growth and for the growth of all other clones . Subsequent T cells leaving the thymus will encounter a more impoverished environment and will grow more slowly , leading to early dominance of one or a small number of early clones and therefore a limited TCR repertoire ( Figure 1 ) . Conversely , if TCR-nonspecific resources such as cytokines were not limiting , and that homeostasis therefore depended solely on competition for TCR-specific signaling , the only cellular competition would be among T cells of the same clonotype . In this case , each clone will grow to roughly the same self-limiting size regardless of when its founder emigrates from the thymus . In this case , TCR repertoire diversity will grow at the greatest possible rate , all other things being equal ( Figure 1 ) . Reality will lie somewhere between these two limiting cases: TCR repertoire diversity will be shaped by both TCR-specific and TCR-nonspecific resources . Our goal is to explore , quantitatively , how these two sets of signals interact dynamically to shape the mature functional T cell population . The quantitative contributions of different signals in the regulation of T-cell number and the diversity of the TCR repertoire are very difficult to determine experimentally in the context of an intact system . To examine the interplay among the mechanisms responsible for the heterogeneity of the T-cell repertoire , we have developed a mathematical model for the effective interactions among T-cell clones . We model the temporal evolution of T-cell clones and their dynamics under the combined effect of TCR-specific and TCR-nonspecific signals . In particular , we consider competition within clones for spMHC complexes presented by antigen-presenting cells as well as competition among cells in all clones for cytokines , space and other TCR-non-specific resources . Such mathematical models have been used extensively to study the dynamical interactions between different branches of the immune system [46] and their effect on T cell [47]–[51] and B cell population development [52] . In this paper , we develop a model to examine competitive interactions among homo- and heterospecific T cells under limiting spMHC and TCR-nonspecific resources . This model then provides the basis for the analysis of clinical data from complete DiGeorge subjects following thymic transplantation to estimate the extent to which regulatory mechanisms working through these distinct pathways contribute to shaping the T cell population . The distinguishing feature of our model is its explicit representation of individual T cell clones , and the direct focus on the interplay between T-cell population size and TCR repertoire diversity this strategy allows . All model parameters are fit to data gathered within the present study; there are no parameters estimated from external sources .
The Kullback-Leibler divergence ( ) is a measure of the difference between two probability mass functions that arises naturally in the context of likelihood ratio tests for multinomial distributions . The diversity of patient TCR repertoires can be quantified via -based comparison of patient spectratypes to mean spectratypes averaged over healthy controls [55] , and -based comparison of TCRBV frequencies to those in healthy controls . Furthermore , can be decomposed hierarchically into components attributable , respectively , to variation in TCRBV family usage , CDR3 length variation within TCRBV family , and protein sequence variation within CDR3 length . For this work , we measure TCRBV family usage by flow cytometry and distributions of CDR3 lengths by spectratyping . The fact that we are not analyzing sequence data implies that we are not making the highest-resolution diversity measurements possible . We will show , however , that this potential limitation does not impede our ability to perform the measurements we set out to make . The data obtained in each of these assays represents the relative frequency of TCR counts in a subclass conditional on the parent class: is the fraction of cells using a gene segment from TCRBV family in a given DiGeorge subject and is the mean usage in our collection of healthy controls . , and are the relative numbers of T cells of CDR3 lengths within those cells using TCRBV family in a given patient , and among controls , respectively . The at the level of TCRBV is ( 6 ) where is the number of TCRBV families considered , which in our case is 23 . The CDR3-length divergence within the family is ( 7 ) where is the number of CDR3 lengths , in our case 18 . The total divergence is ( 8 ) The inverse of the can be regarded as the “completeness” of a TCR repertoire with respect to some standard [55] . Under very reasonable assumptions , the completeness may be regarded as a measure of the diversity , so we will refer to the diversity of the TCR repertoire , and quantify it by the sample completeness , . Note that diversity increases as the the decreases toward its minimum value , zero . The sample , like the sample entropy , is a biased estimator of the population : the expected value of the sample differs from the population by an additive term inversely proportional to the sample size . We account for this fact by including an additive bias as a parameter in the estimation procedure . We can then compare the estimated bias against the sample sizes ( Table 1 ) for each datum . The model is based on absolute T-cell numbers , but the data are blood T cell concentrations . We must use a conversion factor that converts total T cell numbers to T-cell blood concentration . This factor is not easy to estimate with any precision , but our results are quite insensitive to large variation in . Assuming that a 10 kg subject has 600 ml of blood , and that there are 45 times more lymphocytes in the tissues than in the blood , based on adult data [56] , we have . This is the conversion factor we use in the rest of the paper . Furthermore , some subjects have a relatively small number of anomalous T cells at the time of transplantation . We denote the concentration of such cells and treat them as a separate non-functional family . The model quantity ultimately used for comparison to patient T-cell concentration data is . We estimate the remaining parameters , and , as well as the measurement error variances and , and the measurement bias β , by fitting the model specified by Eq . ( 1 ) to the patient T cell concentration and TCR diversity data simultaneously using Markov Chain Monte Carlo ( MCMC; see , eg , [57] ) . We estimate parameters by using the likelihood function given by ( 9 ) where and are the estimated T cell concentrations and diversities , respectively , under the model specified by Eqs . ( 1 , 4 ) ; and are the corresponding patient data observed at sampling times and , respectively . and are the total number of T cell concentration and diversity data points , respectively; and are the error variances . We compute the posterior joint density of all model parameters using a block-sampled Metropolis-Hastings MCMC . The parameters are all manifestly positive , and . Therefore , we transformed , and using natural logs . was treated using a logistic transformation . represents the maximum growth rate of T cell populations . The maximum possible rate is set by the minimum cell-cycle time for lymphocytes of about 6 hours , so we likewise used a logistic transformation on . The prior distributions on the parameters were uniform in the transformed variables .
The maximum-likelihood parameters estimates are shown in Table 2 , along with the 95% credible intervals . The estimate for was much larger than the largest observation time , so we subsequently fixed , thereby eliminating one modeling degree of freedom and refit the model . We found that this simplification entailed no significant decrease in likelihood for any data set . This result implies that our experimental design is not capable of resolving the full time-course of the grafts' becoming functional , not necessarily that the emigration rate continues to increase indefinitely . The estimate of greatest interest for us is , which we find lies between about 10−4 and 10−3 in these three subjects . In subject one , who has the most complete data available and the tightest posterior marginal distribution on , produces an estimate midway between the other two . The parameter governing total thymic emigration , , does not have a simple direct interpretation , but one can estimate the thymic emigration rate at any time post-transplantation . These rates , estimated for the three subjects at one year post-transplantation , are 93 , 44 , and 198 cells per day , respectively . These rates are several orders of magnitude smaller than those estimated for healthy humans infants . We can examine the estimated values of the the bias and use the known expression for this bias to compare to the actual sample sizes . Where the underlying distribution is multinomial , the theoretical bias is given by where is the number of classes in the multinomial , and is the sample size . Using this formula to estimate the sample size leads to the conclusion that the effective sizes of the samples used to estimate diversity are between 103 and 105 cells , consistent with the measured sample sizes ( Table 1 ) . Figure 3 provides a visual assessment of the quality of the model fits to these data . In order to gain a deeper understanding of the role of on the dynamics of both T cell population size and diversity , we performed two additional analyses . We use the data from subject one to illustrate these points . Analyses on the other two subjects yielded comparable results and lead to the same conclusions . A reasonable concern is that thymic emigration rate and may be confounded–that it may be possible to compensate for smaller by making larger . We addressed that concern by performing a numerical experiment to determine the impact on the model fit of fixing outside the inferred credible bounds . We fix to be 10-fold smaller or larger than its maximum-likelihood estimate for subject 1 . The fits are significantly worse ( compare Figures 3 and 4 ) , with log-likelihood ratios of about 8 in both cases ( Table 3 ) . A focused analysis of the time-dependent sensitivity of the model trajectories to parameter variation may provide further insight into the mechanisms of regulation . We examined the sensitivity of our model to changes in [58] , [59] and as follows . Define the sensitivity functions , and . The sensitivity equations are obtained by differentiating both sides of equation ( 1 ) with respect to . The are computed as the solutions of ( 10 ) with for all i . The sensitivity function for D is given by . The relative sensitivities are calculated by multiplying the absolute sensitivity by and dividing by the value of the relevant response variable . The sensitivity to is due largely to the changed timing of discrete events , and so does not lend itself easily to differential sensitivity analysis in the form outlined in Eq ( 10 ) . Sensitivity to was therefore estimated directly by generating sample paths under different values of . We know that mean sensitivity to is transient because the steady-state population size and diversity do not depend on in the deterministic limit ( Eqs . 2 , 3 ) . We find , however , that the diversity remains sensitive to changes in throughout the two-year period of the study ( Figure 5 ) . It should be noted , however , that this assessment is based on the true diversity rather than the sample diversity , whose resolution is limited by sample size . At the sample sizes available , the sensitivity of the measurable diversity to vanishes between 6 and 12 months . The population size is very sensitive to in the first 3–4 months , becoming less so rapidly after that time . As increases , the population size decreases during the sensitive period . Increases in the thymic emigration rate have a positive effect on both the population size and the diversity , as expected . In this case , the sensitivity of the diversity is greatest earlier than in the case of ρ . It should be noted that diversity at the level of amino acid sequence differences is not resolved by our assays , regardless of sample size . If it were , we would expect the diversity to remain sensitive to both of these parameters for a longer period of time . The model does not account for the micro-environmental perturbations invariably encountered by people , including our study subjects . Infection produces a transient change in the steady state of the T cell repertoire , typified by a decrease in the diversity . Once the infection has been resolved , the T-cell population returns to its steady state , and the diversity relaxes back to its original value . The rate at which the return to steady state values occurs depends on ρ . To examine this dependence , we carried out the following numerical experiment . We start with a system at steady state , and suddenly increase the size of a single clone by a factor of 104 , allowing it to consume the non-TCR specific resources its new size requires . The diversity decreases as a result . Over a few days , the other clones adjust to the new steady state . After ten days we remove the artificial support for the enlarged clone and allow the system to return to steady state ( Figure 6 ) . We ran this experiment using models with four values of ρ differing over four orders of magnitude . For high ρ , corresponding to a high competition for specific signals , the diversity decreases less than in the absence of intra-clonal competition . Moreover , after the perturbation is resolved , the diversity increases back to its steady-state , pre-perturbation value at a rate that depends very sensitively on . The return to steady-state diversity is more rapid the greater the competition for TCR-specific signals . The estimated values for obtained from the DiGeorge patients implies a diversity-return time on the order of a few days , rather than weeks or more .
The functional integration of the manifold processes involved in the development and maintenance of the mature T cell repertoire has not yet been fully elucidated . These processes include thymic export , competition for TCR ligands , and competition for non-specific stimulatory factors . Here , we study T cell homeostasis in complete DiGeorge Anomaly patients during the establishment of T cells following thymic transplantation . To quantify the balance between TCR-specific and TCR-nonspecific factors that act to limit T-cell population growth , we developed a mathematical model that accounts for intra- and inter-clonal competition . The key parameter in this model for the present purpose is ρ . This parameter can be given a functional interpretation by considering the case of an individual capable of rearranging just a single TCR . The size of this hypothetical sole clone , at equilibrium , must be larger than the size of the same clone in the presence of a T cell population with a complete repertoire . The ratio of these two sizes is 1∶ρ . We have estimated to be about 10−3; the carrying capacity for a single isolated clone is about 1000 times larger that that for the same clone under normal heterogeneous conditions . This result suggests that TCR-specific resource limitation is relatively unimportant , but this is true only for the total T-cell population size near steady state; away from steady state , TCR-specific regulation may is crucial to the establishment and maintenance of repertoire diversity . At steady-state , TCR diversity is maintained through competition for self peptide MHC [60] , and population numbers are maintained through competition for cytokines and other resources not specific to the TCR [5] , [19] . Our models fit the data adequately only when the thymic emigration is an accelerating function of time through the initial post-transplantation phase , as would be expected from the biology of thymus transplantation [61] , [62] . The slices of cultured thymus tissue used for transplantation are cultured from 2–3 weeks prior to transplantation . There is dramatic depletion of thymocytes from the tissue , thymic epithelium becomes condensed , and the cortico-medullary distinction is lost . In the first months after transplantation , the epithelium differentiates so that cortex and medulla again are distinct and the epithelium develops its characteristic lacy appearance . During this process , the numbers of thymocytes in the transplanted tissue increases dramatically , from small numbers of scattered thymocytes to densely packed thymocytes throughout the allograft . Thus , the biology predicts an increase in emigration with time after transplantation as normal thymic architecture is reestablished . The current study was not able to treat naive and memory T cells separately , but doing so is clearly of great interest given the indications that these pools are regulated independently of each other [11] . Furthermore , we were not able to measure thymic emigration directly , though these measurements would be of significant interest given the surprisingly small rates inferred here , and the impact that the reconciliation of this implied inconsistency would have on our understanding of the treatment of athymia and thymic dysfunction . Finally , our model contains terms for the density-dependent competition among T cells ( Eq . 1 ) whose forms are commonly accepted in population modeling but do not have direct empirical justification in the present context . Although we believe that our conclusions are robust against reasonable variation in these terms , it would be of great utility to explore this issue explicitly . Our aim has been to study T cell homeostasis in a more natural human system than has otherwise been available thus far . It remains to be shown just how natural the post-transplantation environment is in this regard , and therefore how broadly applicable our findings are . We are encouraged , however , by the fact that complete DiGeorge patients receiving thymus transplants recover adaptive immune function . It is important to note that total T-cell counts in these recovered patients remains below normal , typically at about the 10th percentile for children of the same age [45] . Although specific reasons for this condition have not yet been determined , no molecular defects in T-cell function have been reported . We regard it as possible that the sole immunological lesion in DiGeorge anomaly is athymia , and that , once ameliorated by transplantation , the emergence of the T cell population proceeds as it would in a non-affected T-cell lymphopenic subject . We expect , therefore , that our findings will be generally applicable . Our study enabled us to discriminate between two qualitatively different forms of population regulation: TCR-specific regulation and TCR-nonspecific regulation , and to elucidate their roles in jointly maintaining the dynamic homeostasis of the T cell population . The parameters estimates obtained by analyzing data from three DiGeorge Anomaly patients suggest that T-cell population size is maintained by TCR-nonspecific mechanisms , and TCR repertoire diversity is maintained by TCR-specific mechanisms . | Protective adaptive immunity depends crucially on the enormous diversity of the T-cell receptor repertoire , the antigen receptors expressed collectively on T-cell populations . T cells develop from T-cell precursors that originate in the bone marrow and migrate to the thymus , where their T cell receptors are constructed stochastically , and tested for autoreactivity against a host of self antigens . Complete DiGeorge anomaly is a rare congenital disease in which the thymus fails to develop , blocking all T cell development and causing profound immunodeficiency . Thymus transplantation , performed in the first two post-natal years , allows the patient's own T cell precursors to develop in the engrafted thymus tissue into normal , functioning T cells . In addition to saving patients' lives , this procedure provides an extraordinary opportunity to study the de novo development of human T cell populations . We have developed a mathematical model to aid in the statistical analysis of the precious data from these patients . In addition to helping elucidate the means by which the size and diversity of T cell populations are jointly regulated , the insights gained from this study hold promise for the development of therapies to promote immune recovery after transplantation . | [
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
] | [
"mathematics/statistics",
"immunology/leukocyte",
"development"
] | 2009 | The Dynamics of T-Cell Receptor Repertoire Diversity Following Thymus
Transplantation for DiGeorge Anomaly |
Ciliopathies are Mendelian disorders caused by dysfunction of cilia , ubiquitous organelles involved in fluid propulsion ( motile cilia ) or signal transduction ( primary cilia ) . Retinal dystrophy is a common phenotypic characteristic of ciliopathies since photoreceptor outer segments are specialized primary cilia . These ciliary structures heavily rely on intracellular minus-end directed transport of cargo , mediated at least in part by the cytoplasmic dynein 1 motor complex , for their formation , maintenance and function . Ninein-like protein ( NINL ) is known to associate with this motor complex and is an important interaction partner of the ciliopathy-associated proteins lebercilin , USH2A and CC2D2A . Here , we scrutinize the function of NINL with combined proteomic and zebrafish in vivo approaches . We identify Double Zinc Ribbon and Ankyrin Repeat domains 1 ( DZANK1 ) as a novel interaction partner of NINL and show that loss of Ninl , Dzank1 or both synergistically leads to dysmorphic photoreceptor outer segments , accumulation of trans-Golgi-derived vesicles and mislocalization of Rhodopsin and Ush2a in zebrafish . In addition , retrograde melanosome transport is severely impaired in zebrafish lacking Ninl or Dzank1 . We further demonstrate that NINL and DZANK1 are essential for intracellular dynein-based transport by associating with complementary subunits of the cytoplasmic dynein 1 motor complex , thus shedding light on the structure and stoichiometry of this important motor complex . Altogether , our results support a model in which the NINL-DZANK1 protein module is involved in the proper assembly and folding of the cytoplasmic dynein 1 motor complex in photoreceptor cells , a process essential for outer segment formation and function .
Dysfunction of cilia is the underlying defect in a growing group of pleiotropic genetic disorders , the ciliopathies . Cilia are ubiquitous microtubule-based organelles involved in fluid propulsion ( motile cilia ) or signal transduction ( primary cilia ) and ciliopathy-associated proteins localize to various ciliary sub-compartments . Retinal dystrophy is a common clinical feature of ciliopathies where the primary affected retinal cell type is the photoreceptor , which contains a highly specialized primary cilium , consisting of the connecting cilium and axoneme serving as a backbone to the outer segment . For propagation of visual excitation , outer segments are composed of stacks of membranous discs , which are densely packed with the light-sensitive transmembrane receptor rhodopsin and its associated photo-transduction machinery . The membranous disks are organized around the axoneme that is continuous with the connecting cilium . The entire outer segment can thus be regarded as a highly specialized primary cilia compartment . The connecting cilium literally connects the outer segment to the inner segment of the photoreceptor and is the equivalent of a canonical ciliary transition zone . This proximal region of the cilium ensures a tight control of protein access into the ciliary compartment [1–5] through a gate-keeper function , involving several ciliopathy-associated proteins such as NPHP’s [4] and Meckel and Joubert syndrome proteins [6] , importins and Ran GTPases [7 , 8] . Given the daily renewal of about 10% of the total length of the outer segments in humans [9] , photoreceptor cells require intense intracellular trafficking to build their outer segments and to replenish the shed discs . Transmembrane proteins , such as rhodopsin and Usherin are synthesized in the inner segment and subsequently moved from the trans-Golgi network ( TGN ) towards the base of the ciliary compartment via microtubule-based vesicular transport [10] . This transport involves motor proteins such as the ATPases kinesin and dynein [11 , 12] . Specifically , the cytoplasmic dynein 1 motor complex , which consists of two 530 kDa heavy chains , responsible for force production , a group of 74 kDa intermediate chains , 53 to 57 kDa light intermediate chains , and 8 to 21 kDa light chains [13] , has been implicated in minus-end directed transport of post-Golgi-derived rhodopsin-containing vesicles [14] . During its transport , the carboxy-terminal domain of rhodopsin binds to the dynein light chain Tctex-type DYNLT1 [14] . In the absence of rhodopsin , small rudimentary photoreceptor outer segments are formed during the first few postnatal weeks . After this period the outer segments vanish and photoreceptors die rapidly . As a consequence , photo-transduction is impaired leading to defects in visual function [15 , 16] . A similar defect in photoreceptor morphology and function is observed in the zebrafish cannonball mutant , in which the cytoplasmic dynein motor complex 1 is dysfunctional due to a mutation in the dynein cytoplasmic 1 heavy chain 1 ( dync1h1 ) gene [17] . These findings emphasize the importance of the cytoplasmic dynein motor complex 1 for intracellular trafficking which is essential for photoreceptor development and function . However , the structure of this complex has not been fully elucidated to date . Previously , we described a retinal ciliopathy-associated protein module consisting of Usherin , Lebercilin ( Leber’s congenital amaurosis type 5 ) and NINLisoB ( ninein-like protein isoform B ) present at the base of the connecting cilium [18] . In addition , we now identified a physical interaction between NINLisoB and the ciliopathy protein CC2D2A , involved in Joubert and Meckel syndrome , two important ciliopathies ( Bachmann-Gagescu , et al co-submission ) . Furthermore , NINL was found to associate with several members of the cytoplasmic dynein 1-dynactin motor complex and polo-like kinase 1 and was found to function in microtubule nucleation by recruitment of gamma-tubulin ring complexes [19] . However , the function of NINL in photoreceptors is still elusive . In the current study , we investigate the role of NINL and its associated protein complex in the retina using a combination of proteomics and in vivo studies in zebrafish . We identify a central role for NINL and its novel interaction partner DZANK1 in vesicle transport towards the photoreceptor outer segments . Knockdown in zebrafish larvae of either ninl or dzank1 or synergistically at sub-phenotypic doses , leads to abnormal outer segment morphology , mislocalization of rhodopsin , accumulation of vesicular structures and consequently , loss of visual function . We further demonstrate that NINL and DZANK1 associate with complementary subunits of the cytoplasmic dynein 1 motor complex , which is essential for proper transport of vesicle-bound proteins towards the base of the photoreceptor connecting cilium and , as a consequence , photoreceptor development in zebrafish . Together , our findings shed light onto the assembly and structure of the cytoplasmic dynein 1 motor complex and link it to several ciliopathy proteins located at the entrance to the ciliary compartment .
Previously , NINLisoB was identified as a key connector of three large retinal ciliopathies [18] ( Bachmann-Gagescu et al , co-submission ) . Its function within the retina , however , has not yet been deciphered . To get insight into the function of NINL , we screened a random-primed bovine retina cDNA library to identify interaction partners for the previously predicted intermediate filament ( IF ) domain ( 538-825aa ) of NINLisoB [18] . Approximately 70% of the positive clones ( > 1 , 000 clones ) , could be identified as DZANK1 ( double zinc ribbon and ankyrin repeat domains 1 ) , a protein with a yet unknown function . To elaborate on this finding , three overlapping cDNA fragments were cloned: one encoding full length DZANK1 ( 752aa ) and two different deletion constructs , encoding either the zinc finger domains as found in Ran binding proteins ( ZNF_RBZ , SMART accession number SM00547 , 244-317aa ) or the ankyrin repeats known to be involved in protein-protein interactions ( ANK , SMART accession number SM00248 , 595-681aa ) ( SMART database; http://smart . embl-heidelberg . de ) ( Fig 1a ) . To test whether the interactions of DZANK1 with NINL are isoform-specific , both isoforms of NINL were tested . The transcript encoding NINLisoB lacks exon17 of the originally described NINL gene ( encoding NINLisoA ) , resulting in the in-frame skipping of 349 amino acids after residue 734 [18] . Using dedicated binary yeast two-hybrid assays , we were able to pinpoint the interaction to the ZNF_RBZ domains of DZANK1 and both isoforms of NINL ( Fig 1b ) . Since NINLisoB was originally found in the yeast-two hybrid screen , we continued with this isoform for further confirmations . We performed several in vitro and in vivo binding assays to conclusively validate the interaction between DZANK1 and NINLisoB . In a glutathione S-transferase ( GST ) pull-down assay , we identified that full-length Strep/FLAG-tagged DZANK1 was pulled down from HEK293T cell lysates by GST-fused NINLisoB_538-825aa and not by GST alone ( Fig 1c ) . To confirm this interaction in cellulo , HEK293T cells were co-transfected with plasmids encoding full length HA-tagged NINLisoB and Strep/FLAG-tagged DZANK1 for immunoprecipitation ( IP ) assays . With anti-HA antibodies , NINLisoB consistently co-immunoprecipitated with full length DZANK1 , but not with the control protein LRRK2 ( Fig 1d ) . Reciprocal IP experiments with anti-FLAG antibodies for IP confirmed these results ( Fig 1d’ ) . In addition we performed GST pull-down assays from biologically relevant bovine retinal extracts followed by mass spectrometry ( LC-MS/MS ) analysis . In total , 445 different proteins were identified of which 59 passed the stringent filtering criteria . GST-fused human NINLisoB_aa538-825 was able to consistently pull-down endogenous bovine DZANK1 ( in 4 out of 4 assays ) whereas unfused GST ( in 0 out of 3 assays ) was not , which further confirmed the interaction ( S1 Table ) . Further confirmation of the interaction was obtained through co-localization of eGFP- and mRFP-tagged DZANK1 and NINLisoB in hTERT-RPE1 cells . In singly transfected cells , NINLisoB and DZANK1 were both present at the ciliary base , partially co-localizing with the basal body and the ciliary marker polyglutamylated tubulin ( anti-GT335 ) ( Fig 2a and 2b” ) . DZANK1 also localized along the microtubule network of the cells . Co-expression of NINLisoB and DZANK1 resulted in the co-localization of both proteins , thereby fully retaining the latter to the basal body ( Fig 2c” , yellow signal ) . Importantly , co-localization of endogenous NINL and DZANK1 was also observed by immunohistochemistry in rat retina where NINL was demonstrated at the region of the connecting cilium [18] . Using a specific anti-DZANK1 antibody we show that NINL and DZANK1 co-localize in this region ( Fig 2d–2f’ ) . To investigate the biological role of NINL and DZANK1 in the retina , we first addressed the suitability of zebrafish as an animal model . tBLASTn searches of the zebrafish genome , using the amino acid sequence of human NINL and DZANK1 , revealed a single ortholog for both genes . Although extensive RT-PCR analyses were performed , we were not able to detect the transcript encoding zebrafish Ninl isoform A in RNA obtained from zebrafish larvae ( 5 days post fertilization ( dpf ) ) . Therefore we conclude that under the given conditions the ninl transcript that shows the highest degree of homology with the human NINLisoB encoding transcript is the most prominent . The presence of shorter or alternative transcripts encoding additional zebrafish Ninl isoforms cannot be ruled out , as no 5’- and 3’-RACE experiments were performed . Subsequently , the effect of ninl and dzank1 knockdown during zebrafish embryonic development was investigated , using gene-specific translation-blocking ( atgMOs ) and splice-blocking morpholinos ( spMOs ) . Control MO-injected larvae ( 10 ng/nl; n = 300 from 2 biological replicates ) appeared morphologically normal , and could not be distinguished from uninjected larvae ( WT ) during the studied developmental period of 4 to 5 dpf ( Fig 3a and 3d ) . In contrast , injection of ninl atgMO revealed a concentration-dependent spectrum of phenotypes ( n>200/group from 2 biological replicates; Bachmann-Gagescu et al . , companion manuscript ) . Injection of the optimal concentration of 2 ng/nl ninl atgMO resulted in severe morphological defects including ventrally curved body axis , ventriculomegaly , pronephric cysts , expanded melanophores , small eyes and circling swimming behavior at 4 dpf ( n = 300 from 2 biological replicates; Fig 3b and Bachmann-Gagescu et al . , companion manuscript ) . In addition , we observed significantly shorter photoreceptor outer segments ( OS ) on retinal cryosections from 4dpf ninl morphant larvae stained with boron-dipyrromethene ( bodipy ) to mark the outer segment membrane disks ( mean OS length 1 . 6+/-0 . 26 μm in morphants compared to 3 . 9+/-0 . 32 μm in wild-type , p<0 . 0001 , unpaired Student’s t-test , n>10 larvae from each group in each of 2 biological replicates ) ( Bachmann-Gagescu et al , companion manuscript ) . Co-injection of 150 pg/nl capped MO-resistant mRNA encoding human NINLisoB with 2 ng/nl of ninl atgMO rescued the observed body curvature phenotype ( curved body shape in 71% of ninl atgMO injected larvae ( n = 207 ) versus 36% in ninl atgMO + ninl mRNA injected larvae ( n = 203 ) , data pooled from 2 biological replicates , p<0 . 0001 , two-tailed Fisher’s exact test ) and the OS length ( mean OS length in rescued larvae 3 . 8+/-0 . 25 μm , p<0 . 0001 , Student’s t-test , n = 10 larvae ) ( Bachmann-Gagescu et al , co-submission ) . In addition , the specificity of the observed phenotypes is further confirmed by the fact that a second morpholino against ninl targeting the splice site at the intron14/exon15 junction causing aberrant splicing led to similar phenotypes , including ventriculomegaly , expanded melanophores ( 4 ng/nl , n = 200 ) and shortened photoreceptor outer segments as compared to control MO-injected larvae . Finally , using an anti-ninl antibody , we observed substantial decrease of Ninl protein on Western blots and on immuno-histochemistry of retinal cryosections for the ninl atgMO-injected larvae and milder decrease in both assays for the ninl ex15 spMO-injected larvae ( Bachmann-Gagescu et al , companion manuscript ) . Injection of increasing amounts of a splice-blocking morpholino targeting dzank1 exon8 resulted in small eyes and severe pericardial edema at 4 dpf ( S1a Fig , Fig 3c ) . In addition , dzank1 morphants showed impaired ambulatory activity as predicted before [20] . Based on the incidence of the observed phenotypes and non-quantitative RT-PCR analysis on RNA from morphant larvae ( harvested at 2 and 4dpf; S1a and S1g Fig . ) we determined that the optimal dose of the dzank1 ex8 spMO was 6 ng/nl . Co-injection of 150 pg/nl capped MO-resistant mRNA encoding human DZANK1 with 6 ng/nl dzank1 ex8 spMO rescued the observed ambulatory activity phenotype ( n = 105 , 2 biological replicates; S1b and S1e Fig ) . This observed ambulatory activity phenotype could be fully recapitulated by a second splice blocking morpholino targeting dzank1 exon4 ( 6 ng/nl , n = 162 out of 200 injected larvae from 2 biological replicates; S2 Fig ) , further indicating the specificity of the used morpholino . Subsequently , we evaluated the retinal morphology of dzank1 ex8 spMO-treated larvae ( 4 dpf; n = 40 ) . While retinal lamination was unaffected , significantly shortened photoreceptor outer segments ( OS ) were observed as compared to controls , as highlighted by boron-dipyrromethene ( bodipy ) staining of membranes ( Fig 3a’–3c’and 3h ) ( mean OS length 2 . 1+/-0 . 39 μm in morphants compared to 4 . 9+/-0 . 27 μm in wild-type , p<0 . 0001 , unpaired Student’s t-test , n> 10 larvae from each group in each of 2 biological replicates ) . The observed photoreceptor outer segment defects fully coincided with the ambulatory activity defects observed in dzank1 ex8 spMO-treated larvae and could be rescued as well ( mean OS length in rescued larvae 3 . 0+/-0 . 36 μm as compared to 1 . 9+/-0 . 25 μm in dzank1 morphants ( P = 0 . 01 , unpaired Student’s t-test , n = 13 larvae each group ) and 3 . 5+/-0 . 25 μm in uninjected larvae ( P = 0 . 3 ( NS ) , unpaired Student’s t-test , n = 12 larvae ) ( S1f Fig ) . These phenotypes could also be recapitulated by a second splice blocking MO targeting ninl ex15 or dzank1 ex4 ( Bachmann-Gagescu et al . , companion manuscript; S2 Fig ) . These observations are indicative for an essential role of both Ninl and Dzank1 in OS formation and/or maintenance . To investigate whether there is a functional relationship between Ninl and Dzank1 in this process , sub-effective doses of dzank1 ex8 spMO ( 1 ng/nl ) and ninl atgMO ( 0 . 5 ng/nl ) were co-injected . While single injections with these sub-phenotypic amounts of MOs caused no discernible phenotypes ( no body curvature defects or abnormal swimming behavior for ninl atgMO-injected larvae or no defects in ambulatory activity in dzank1 ex8 spMO-injected larvae ) ( n = 200/group , 2 biological replicates; Fig 3e and 3f ) ) , co-injection of both MOs resulted in a severely enhanced phenotype including defects in swimming behavior , small eyes and curved tails ( Fig 3d–3g ) . Furthermore , photoreceptor outer segment length measurements on transverse sections of bodipy-stained retina demonstrated significantly impaired OS formation in double morphants ( ninl atgMO 0 . 5 ng/nl + dzank1 ex8 spMO 1ng/nl; mean OS length 1 . 6+/-0 . 3 μm , n = 6 ) , as compared to low dose single ninl atgMO-injected ( 0 . 5 ng/nl;mean OS length 4 . 3+/-0 . 66 μm , n = 6 ) and dzank1 ex8 spMO-injected larvae ( 1ng/nl; mean OS length 5 . 2+/-0 . 76 μm , n = 5 ) , and as to control MO-injected ( mean OS length 4 . 2+/-0 . 26 μm , n = 10 ) and uninjected ( WT ) larvae ( mean OS length 4 . 8+/-0 . 28 μm , n = 9 ) ( Fig 3a’ , 3d’ , 3e’ , 3f’ , 3g’ and 3h; P<0 . 001 , unpaired Student’s t-test for pairwise comparison between double morphants and each low dose single morphant ) . These retinal defects in double morphants were similar to those quantified in larvae injected with the optimal doses of dzank1 spMO ( 6 ng/nl; 2 . 1+/-0 . 39 μm , n = 10; Fig 3c’ and 3h ) or ninl atgMO ( 2 ng/nl; 1 . 6+/-0 . 26 μm , n = 8; Fig 3b’ and 3h ) alone . The functional impact of these morphological changes in the morphant retinas was confirmed by measurements of the optokinetic response ( OKR ) [21 , 22] , which tests the ability of zebrafish larvae to visually track rotating stripes [23] . At 4 dpf , ninl and dzank1 morphant larvae displayed a grossly impaired OKR response ( 0–2 saccades per minute ) , in contrast to the normal OKR response ( 5–15 saccades per minute ) of control morphants and un-injected larvae from the same clutch ( mean number of saccades per minute = 10 . 3 +/- 0 . 7 in uninjected wild-type , 10 . 1 +/- 0 . 6 in control oligo injected , 0 . 5 +/- 0 . 2 in dzank1 morphant and 0 . 9 +/- 0 . 2 in ninl morphant larvae , n = 9/group; P<0 . 0001 for pairwise comparisons wildtype/control oligo vs each morphant , unpaired Student’s t-test ) ( Fig 3h ) . Since spontaneous eye movements in the absence of a stimulus were occasionally registered in all MO-injected larvae , the loss of OKR response is not due to a defect in ocular muscular contraction , but to impaired visual function . To obtain a more detailed insight into the role of Ninl and Dzank1 in OS morphogenesis , the retinal ultra-structure of ninl , dzank1 morphants and control larvae was studied by transmission electron microscopy ( TEM ) ( for numbers see S5 Table ) . In control MO-injected embryos ( 4 dpf ) , the photoreceptor inner segments ( IS ) displayed compact ellipsoid regions with clustered mitochondria , a narrow myoid with a Golgi apparatus , while OS presented well-organized , nicely stacked disc structures ( Fig 4a ) , as described for wild-type uninjected larvae [17 , 24] . In contrast , in both ninl and dzank1 morphants , IS malformations were found including swollen Golgi complexes with enlarged and distended cisternae , accumulation of vesicle-like structures throughout the IS , large vacuoles and dispersed mitochondria . Occasionally , lysosomal structures were observed ( Fig 4e’ ) . OS were either absent , or disrupted . Ultra-structural characteristics of deviant OS were hampered elongation , accumulation of vesicles , polarization defects and deformed discs ( Fig 4d and 4e’ ) . Statistical analyses of these defects were based on quantification of the proportion of photoreceptor cells presenting with vesiculated inner segments in the different experimental groups ( Fig 4i ) . No significant difference could be demonstrated between vesiculation in IS of larvae injected with sub-phenotypic doses of dzank1 ( 1 ng/nl ) or ninl ( 0 . 5 ng/nl ) MO ( Fig 4f and 4g ) as compared to control MO-injected ( 10 ng/nl ) or wildtype ( WT ) zebrafish larvae . The percentage of IS in photoreceptors with vesicular structures and/or swollen Golgi complexes in dzank1 ( 6 ng/nl MO ) , ninl ( 2 ng/nl MO ) as well as combined ( dzank1 1 ng/nl and ninl 0 . 5 ng/nl ) MO-injected zebrafish groups was significantly increased compared to WT and control MO-injected larvae ( P<0 . 01 , Student’s t-test ) . The most significant vesicular increase was observed in the combined MO-injected ( dzank1 and ninl ) group ( Fig 4h and 4h” ) ( P<0 . 001 , Student’s t-test ) . Mass spectrometry ( MS ) -based quantitative proteomics was employed to gain further insights into the molecular basis of the defects observed in ninl and dzank1 morphants . N-terminally fused Strep/FLAG-tagged NINLisoA/B and DZANK1 together with their associated , native protein complexes were tandem-affinity-purified from HEK293T cells and the complex components identified by liquid chromatography coupled to tandem mass spectrometry ( LC-MS/MS ) . Besides six actin-binding proteins ( ARP1 , ARP1B , ARP10 , CAPZA1 , CAPZA2 and CAPZB ) and three subunits of Ca2+/calmodulin-dependent protein kinase II ( CaMKII ) ( CAMK2A , CAMK2D , and CAMK2G ) , the NINL-associated interactome contained multiple subunits of the cytoplasmic dynein 1-dynactin motor complex ( DYNC1H1 , DYNC1LI1 , DYNC1LI2 , DYNCI2 , DYNLRB1 , DCTN1-4 , and DCTN6 ) , which is involved in minus end–directed , microtubule-associated transport ( Fig 5a , S2 Table ) . These results were confirmed by the GST pull-down from bovine retinal extracts in which DZANK1 , several subunits of the cytoplasmic dynein 1-dynactin motor complex ( DYNC1H1 , DYNC1LI1 , DCTN1 , DCTN2 and DCTN4 ) and five dynactin-associated actin-binding proteins ( ARP1B , ARP10 , CAPZA1 , CAPZA2 and CAPZB ) were found to associate with GST-fused NINLisoB_aa538-825 ( S1 Table ) . We thus confirmed the previously described association of NINL with several subunits of the cytoplasmic dynein 1-dynactin motor complex [19] . Intriguingly , the DZANK1-associated protein complex exclusively contained the two cytoplasmic dynein 1 light chains , DYNLL1 and DYNLL2 , which were absent from the NINL interactome . We confirmed the identified interaction between DZANK1 with DYNLL1 and DYNLL2 by reciprocal co-IP experiments in HEK293T cells . In addition , co-expression of mRFP-tagged DYNLL1 or DYNLL2 with eCFP-tagged DZANK1 in ciliated hTERT-RPE1 cells resulted in recruitment of the latter to the basal body and accessory centriole ( S3 and S4 Figs ) . These results suggest that the cytoplasmic dynein 1 motor complex is composed of at least two sub-complexes: one DZANK1-associated sub-complex containing DYNLL1 and DYNLL2 , and another NINL-associated sub-complex containing at least DYNC1H1 , DYNC1LI1 , DYNC1LI2 , DYNCI2 and DYNLRB1 . In order to better understand the orchestration and dynamics of the NINL-associated motor complex , we performed an elution profile analysis of SDS-induced sub-complexes by quantitative mass spectrometry ( EPASIS ) [25] . SF-TAP-tagged NINLisoA/isoB was over-expressed in HEK293T cells and affinity-purified using the FLAG moiety of the fusion tag . The native protein complex was sequentially treated with increasing SDS-concentrations to destabilize the interactions and thereby induce the elution of sub-complexes . Besides the dynactin submodule , which showed the most stable association with the NINL complex ( S5c Fig; complete elution at ≥ 0 . 005% SDS ) a second sub-module consisting of proteins from the cytoplasmic dynein 1 motor complex [26] was identified ( S5c Fig c; elution between 0 . 001 and 0 . 01% SDS ) . This dynein sub-module may dissociate in two additional sub-modules but this finding lacked statistical significance ( S5–S8 Figs , S3 and S4 Tables ) . In addition , CLIP1 ( = CLIP-170 ) , PAFAH1B1 ( = LIS1 ) , ACAD11 and MRPS27 co-eluted with the dynein module , making them potentially novel candidate components of the dynein 1 motor complex . Dynein 1 heavy chain 1 ( dync1h1 ) , encoding a subunit of the cytoplasmic dynein 1 motor complex which mediates minus-end-directed post-Golgi vesicle trafficking towards the basal body , is mutated in the zebrafish cannonball mutant [17] . Similar to our findings in ninl and dzank1 morphants , cannonball mutants form short rudimentary photoreceptor OS , show organelle positioning defects and display a severe accumulation of vesicles in the photoreceptor IS and OS and this phenotype could be recapitulated by a translation blocking morpholino against dync1h1 [17] . Sub-effective concentrations of this previously published dync1h1 MO ( 1 ng/nl ) [17] in combination with the ninl atgMO ( 0 . 5 ng/nl ) were co-injected and compared to single injected morphants , control MO-injected larvae and uninjected controls ( n = 300/group from 2 biological replicates ) . At 4 dpf , quantification of OS lengths in each group was performed on bodipy-stained cryosections and revealed significantly shortened OS in larvae in the dync1h1 ( 3 ng/nl; mean OS length 1 . 95+/-0 . 4 μm , n = 5; Fig 5d’ , 5d” and 5h ) and dync1h1/ninl morphant groups ( 1ng/nl + 0 . 5 ng/nl; mean OS length 1 . 413+/-0 . 2 μm , n = 13; Fig 5g’ , 5g” and 5h ) as compared to uninjected controls ( mean OS length 4 . 8+/-0 . 27 μm , n = 9; Fig 5b’ , 5b” and 5h ) , control MO-injected ( 10 ng/nl; mean OS length 4 . 2+/-0 . 26 μm , n = 10; Fig 5c’ , 5c” and 5h ) and to both low-dose single dync1h1 MO-injected ( 1 ng/nl; mean OS length 3 . 6+/-0 . 44 μm , n = 13; Fig 5f’ , 5f” and 5h ) and ninl atgMO-injected ( 0 . 5 ng/nl; mean OS length 4 . 3+/-0 . 66 μm , n = 6; Fig 5e’ , 5e” and 5h ) groups ( P<0 . 001; two tailed , unpaired Student’s t-test on pairwise comparisons between double morphants and each of the single low-dose morphants ) , demonstrating a genetic interaction between ninl and dync1h1 and supporting a role for Ninl in cytoplasmic dynein 1-driven intracellular vesicle transport . In addition , localization of rhodopsin , which should be restricted to the OS , was aberrantly observed in the photoreceptor cell body in both ninl and dzank1 morphants ( S9 Fig and Bachmann-Gagescu et al . co-submission . ) . The observed mislocalization of rhodopsin in these morphants could be explained by defects in intracellular transport . However it could also be the result of defects in OS development and a consequence of the absence of this structure . Although the OS of ninl morphants are severely affected , the connecting cilium of photoreceptor cells in ninl , dzank1 and ninl-dzank1 double morphants was found to be intact ( arrow heads in Fig 4b , 4c and 4h ) , providing the opportunity to test the localization of proteins to this ciliary compartment regardless of the presence of an outer segment . USH2A , a previously published interaction partner of NINL , is a transmembrane protein that is synthesized in the IS and subsequently transported in TGN-derived vesicles towards the base of the connecting cilium . USH2A was previously shown to localize in the peri-ciliary region , which should be independent of the presence of the OS [27] . Therefore , a defect in USH2A localization would likely be due to impaired post-Golgi dynein-based trafficking . Using a zebrafish-specific anti-Ush2a antibody , we observed a strongly reduced immunofluorescent signal in ninl and dzank1 morphants ( 4 dpf ) , as compared to control MO-injected larvae ( 4 dpf ) ( Fig 6a’–6d’ ) . The localization of centrin , which was used as a marker for the connecting cilium , was unaffected in both controls and morphants ( Fig 6a”’–6d”’ ) . Together , these data indicate a defect in the transport of vesicle-bound transmembrane proteins in ninl and dzank1 morphants . To determine whether Ninl and/or Dzank1 are required for other dynein-based intracellular transport processes , we monitored dynein 1-mediated melanosome transport in zebrafish skin . Zebrafish alter their skin pigmentation by trafficking melanosomes within melanophores . The melanosome , a lysosome-related organelle , can be shuttled bi-directionally between the cell periphery and the peri-nuclear region by two microtubule-based molecular motors , kinesin ll ( anterograde ) and dynein 1 ( retrograde ) . Pigment aggregation ( retrograde transport ) can be stimulated within minutes upon treatment with epinephrine [28] . In the melanosome transport assay , 5 dpf larvae are dark-adapted to display maximum melanophore dispersion ( Fig 7a ) . After addition of epinephrine , the melanosomes rapidly contract and can be visually evaluated for reduction in pigment dispersion ( Fig 7a’–7a” ) . The endpoint is apparent when pigmentation pattern reflects peri-nuclear accumulation of melanosomes ( Fig 7a’ ) . A rapid melanosome contraction was seen in control MO-injected and un-injected larvae ( Fig 7a–a” and 7d ) ( ΔT = 1 . 55 min and ΔT = 1 . 30 min respectively; n = 20/group ) , whereas ninl and dzank1 morphants demonstrated significantly delayed dynein 1-mediated melanosome retraction ( ΔT = 15 . 42 min and ΔT = 23 . 62 , n = 20; P<0 . 001; Student’s t-test ( two tailed , unpaired ) , Fig 7b–7c”’ and 7d ) , which is indicative for impaired dynein 1-mediated retrograde transport . Taken together , our findings suggest that the Ninl-Dzank1-cytoplasmic Dynein 1 complex is required for the intracellular transport of organelles and vesicles , and is essential for the photoreceptor’s OS formation , maintenance and function .
In this study , a central role for NINL and its novel interaction partner DZANK1 is identified in vesicle transport in photoreceptor cells . We demonstrate that NINL and DZANK1 associate with complementary subunits of the cytoplasmic dynein 1 motor complex and that this complex consists of at least two submodules . In photoreceptor cells , this motor complex has been implicated in post-Golgi vesicle trafficking and organelle positioning [14 , 17] . In line with this function , in vivo studies in the zebrafish demonstrate defects in post-Golgi trafficking as revealed by delayed cytoplasmic dynein 1-regulated melanosome transport , defective photoreceptor outer segment formation , abnormal vesicle accumulation within the photoreceptor inner segments and mislocalization of rhodopsin and Ush2a in both ninl and dzank1 morphants . Cytoplasmic dynein 1 is fundamentally important for embryonic development . Dynein 1 heavy chains are essential for the formation of the motor complex and their absence leads to very early embryonic lethality in mice before E8 . 5 [29] . In contrast , larvae of the zebrafish cannonball ( cnb ) mutant lacking Dync1h1 undergo relatively normal early development and remain indistinguishable from wild-type siblings until approximately 3 . 5 dpf . This remarkable difference between mouse and zebrafish mutants may be explained by the presence of wild-type maternally-derived ( yolk-associated ) mRNA in zebrafish embryos [30] . Cnb larvae eventually show a reduced eye size , present with small rudimentary photoreceptor outer segments and expanded skin melanophores , show severe organelle positioning defects and die between 6 and 8 dpf [17] . The phenotype of zebrafish depleted for Ninl is remarkably similar to that of the cnb mutant , with small eyes , mispositioned organelles , retinal dystrophy , and expanded melanophores . These overlapping phenotypic characteristics in combination with the observed genetic interaction in the retina between ninl and dync1h1 suggest that the ninl morphant phenotype is caused by dysfunctional cytoplasmic dynein 1-mediated transport . Mice lacking Dync1li1 , a light-intermediate chain subunit of dynein 1 , which is structurally less important than heavy chains , survive into adulthood [31] . Their photoreceptors do , however , lack outer segments due to blocked transport of TGN-derived vesicles towards the basal body . In contrast to the ninl morphants , dzank1 morphants show a much milder phenotype and do not present curved bodies or major early developmental defects . DZANK1 associates specifically with two structurally less important dynein 1 light chains , which might explain the less severe phenotype of dzank1 morphant zebrafish larvae . Nonetheless , photoreceptor cells of both ninl and dzank1 morphant zebrafish larvae display severely shortened outer segments , disruption of mitochondria organization and accumulation of vesicles within the inner segments . Given that photoreceptors have extremely high transport requirements due to the significant daily renewal of their outer segments , they are predicted to be more sensitive to defects in intracellular transport , which could explain why loss of dzank1 results predominantly in a photoreceptor phenotype . The physical position and role of NINL and DZANK1 in the cytoplasmic dynein 1 motor complex remains unknown . Up to now , tandem affinity purification assays from HEK293T cells using different subunits of this motor complex as bait never identified peptides of NINL or DZANK1 ( S2 Table ) . The most likely explanation for this is that NINL and DZANK1 are not expressed in these cells or are expressed only at very low levels . To get the first insights into the dynamics and orchestration of the NINL-associated dynein 1 motor complex , we performed an elution profile analysis of SDS-induced sub-complexes by quantitative mass spectrometry ( EPASIS ) . Despite the fact that the dynein module shows a tendency of being built up of two distinct sub-modules , the statistical significance is lacking . CLIP1 ( = CLIP-170 ) , PAFAH1B1 ( = LIS1 ) , ACAD11 and MRPS27 co-eluted with the dynein module , making them potentially novel candidate components of the dynein 1 motor complex . LIS1 was previously found to interact with cytoplasmic dynein 1-dynactin [32] and CLIP1 [33] in order to keep dynein in a persistent microtubule-bound state [34] . The role of ACAD11 and MRPS27 in dynein 1 function or dynamics needs to be determined . The dynactin complex was identified as a distinct sub-complex which was most strongly associated with NINL in the EPASIS essay . The lack of clear OS defects in zebrafish mok/dctn1a and dctn1b mutants [17 , 35 , 36] however , implies that dynein 1 functions independently of dynactin in outer segment morphogenesis . Indeed it was reported that the binding of dynactin1 to dynein 1 is non-essential for the ability of dynein 1 to bind stably to rhodopsin transport vesicles in vitro [14] . Therefore it can be concluded that the ocular phenotypes observed in the ninl morphant are most likely caused by a non-functional dynein 1 complex rather than dysfunction of the dynactin complex . We previously described the association of NINLisoB with USH2A [18] , a protein known to be essential for photoreceptor homeostasis in mice [27 , 37] . The retinal defects observed in ninl morphants are more severe than those observed in the Ush2a knockout mouse model , which displays intact outer segments and late-onset mild photoreceptor degeneration [27] . This comparison suggests a transport function for NINLisoB upstream of USH2A towards the apical inner segment . The absence of Ush2a at the photoreceptor periciliary region of ninl morphants is in line with this hypothesis . DZANK1 protein sequence analysis predicted the presence of two ZNF_RBZ domains and their interaction with RanGDP rather than their involvement in transcription . Ran is an abundant Ras-like GTPase , which plays a role in multiple cellular processes , including modulation of nucleo-cytoplasmic transport of macromolecules larger than ~40 kDa across the nuclear envelope [38] . Further , it has been proposed that a similar complex , consisting of Ran , Ran-binding proteins and importins/exportins plays a role in regulating import of cargo at the base of the cilium [39] and that RanBP2 is involved in processing or transport of opsin [40] . Since the ZNF domains of DZANK1 are highly homologous to the functional domains of RanBP2 , DZANK1 might be involved in transport of opsin as well . The observed mislocalization of rhodopsin in photoreceptor cells of dzank1 morphant zebrafish larvae is in line with this . Moreover , a role for DZANK1 in opsin transport and subsequent ciliary entry creates an attractive functional connection between DZANK1 and the Usher protein network , members of which have been suggested to act in opsin vesicle docking at the periciliary region and subsequent transport in the connecting cilium and calyceal processes [41 , 42] . In summary , our study provides a deeper insight into the tissue-specific dynamics of the cytoplasmic dynein 1 motor complex , and supports an essential role for this complex in close connection to NINL and DZANK1 in post-Golgi vesicle transport of selective cargo in zebrafish photoreceptor cells .
Animal experiments were conducted in accordance with the Dutch guidelines for the care and use of laboratory animals , with the approval of the Animal Experimentation Committee ( Dier Experimenten Commissie [DEC] ) of the Royal Netherlands Academy of Arts and Sciences ( Koninklijke Nederlandse Akademie van Wetenschappen [KNAW] ( Protocol # RU-DEC 2012–301 ) . To identify the interacting regions between DZANK1 and NINLisoA/B , a GAL4-based Y2H screen ( HybriZAP , Stratagene , La Jolla , CA , USA ) was used as previously described [43 , 44] . Accession IDs: NINLisoA ( Q9Y2I6 ) , NINLisoB ( XP_005260736 ) , DZANK1 ( Q9NVP4 ) , DYNLL1 ( NP_001032584 ) , DYNLL2 ( Q96FJ2 ) . The cellular ( co ) -localization of DZANK1 , NINLisoB , DYNLL1 and DYNLL2 was determined by co-transfecting hTERT-RPE1 cells on glass slides , with pcDNA3-mRFP and pcDNA3-eCFP . DZANK1 FL , NINLisoB , DYNLL1 and DYNLL2 were transfected using Effectene Transfection Reagent ( Qiagen , Netherlands ) according to manufacturer’s instructions . After 48 hours transfection , cells were washed with PBS , fixed with 4% paraformaldehyde ( PFA ) and mounted with Vectashield containing DAPI ( Vector Laboratories , Inc . , UK ) . Images were taken with an Axioplan2 Imaging fluorescence microscope ( Zeiss ) and processed using Adobe Photoshop version 8 . 0 ( Adobe Systems , USA ) . The GST-fusion proteins were produced by transforming Escherichia coli BL21-DE3 with plasmid pDEST15-NINL ( 538aa to 825aa ) , as previously described [43] . Strep/FLAG-tagged DZANK1 or Strep/FLAG-tagged NINLisoB were produced by transfecting HEK293T cells with plasmids encoding N-SF-TAP-hsDZANK1 or NINLisoB , respectively , using the transfection reagent ( PEI; Polyethylenimine ) , according to the manufacturer's instructions . The GST pull-down assay was performed as described previously [43] . For GST pull-down experiments from retinal extracts , retinas were dissected from fresh bovine eyes obtained from the local slaughter house . Retinas were homogenated by sonication on ice for two times 30 s in extraction buffer [10 mM HEPES ( pH 7 . 9 ) , 10 mm NaCl , 3 mm MgCl2 , freshly added 1 mm DTT , 1 mm Na3VO4] , supplemented with complete protease inhibitor cocktail ( Roche Diagnostic ) . Retinal extracts were incubated overnight at 4°C with the GST fusion proteins immobilized on glutathione sepharose 4B beads . GST fusion proteins were eluted from the glutathion sepharose 4B beads with 100 mM reduced Glutathione ( GSH ) in 50mM TRIS-HCl ( pH 8 . 0 ) overnight . Proteins were subsequently precipitated and analyzed by mass spectrometry analysis as described below . HA-tagged NINL and DYNLL1 were expressed by using the mammalian expression vector pcDNA3-HA/DEST , the 3xHA-tagged DYNLL2 by using p3xHA_CMV/DEST , Strep/FLAG-tagged DZANK1 by using pSF-NTAP/DEST and LRRK2 by using p3xFLAG/DEST from the Gateway cloning system ( Invitrogen , USA ) . HEK293T cells were co-transfected , using Effectene Transfection Reagent ( Qiagen , USA ) according to the manufacturer’s instructions . Twenty-four hours after transfection , the cells were washed with PBS and subsequently lysed in IP lysis buffer ( 50 mM Tris-HCL pH 7 . 5 , 150 mM NaCl , 1% Triton-X-100 supplemented with complete protease inhibitor cocktail ( Roche , Germany ) ) on ice . HA-tagged molecules were immune-precipitated from cleared lysates at 4°C overnight . Protein-antibody complexes were coupled to ProtA/G beads ( Santa Cruz ) for 2 hours at 4°C . After incubations , the beads were pelleted and washed three times with lysis buffer . Beads were boiled and proteins were resolved on SDS-PAGE . For western blotting , proteins were electrophoretically transferred onto nitrocellulose membranes , blocked with 5% non-fat dry milk ( Biorad ) in PBS-T ( PBS supplemented with 0 . 1% Tween ) and analyzed with the appropriate primary and secondary antibodies in 0 . 5% non-fat dry milk in PBS-T . After 4 washes in lysis buffer , the protein complexes were analyzed on immunoblots using the Odyssey Infrared Imaging System ( LI-COR , USA ) . As secondary antibodies , IRDye800 goat-anti-mouse IgG and Alexa Fluor 680 goat-anti-rabbit IgG were used . The monoclonal antibodies directed against Centrin ( 1:100 ) , Millipore , lot nr: 04–162 and the polyclonal antibodies directed against the cytoplasmic region of USH2AisoB ( 1:100 ) , Novus Biological , lot nr: T00620A02 have been described previously [43 , 45] . For the rhodopsin staining anti-rhodopsin , clone 4D2 Millipore lot nr: 2038649 ( 1:1000 ) was used . For Western blot and immunohistochemical analyses , antibodies directed against human NINL ( aa406-455 ) were purchased from LSBio , cat . No . LS-C201509 ( 1:100 ) . Antibodies against the C-terminal region of zebrafish Ninl , which were raised in guinea pigs against a GST-fusion protein , encoding a peptide consisting of 403aa to 591aa ( Genbank NP_001268727 ) were used for immunohistochemical analyses . The cDNA , encoding this peptide was amplified by using the forward and reverse primers 5'-GACCAAGCCTGTCAAGAGCG-3' and 5'-GCCCTGAGACTTCAACAAC-3' , respectively . The secondary antibodies were goat anti-guinea pig Alexa Fluor 488 and Alexa Fluor 568 , goat anti-rabbit Alexa Fluor 488 , goat anti-mouse Alexa Fluor 488 , Alexa Fluor 568 and Alexa Fluor 647; ( all used at a dilution of 1:500 , all from Molecular Probes-Invitrogen Carlsbad , CA , USA ) . The latter were diluted together with DAPI in block buffer ( 2% BSA and 10% Normal Goat serum in PBS ) . Experimental procedures were conducted in accordance with international and institutional guidelines . Wild type adult Tupfel Long fin ( TLF ) zebrafish were used . The zebrafish eggs were obtained from natural spawning of wild-type breeding fish . Larvae were maintained and raised by standard methods [46] . Translation-blocking ninl ( 5’-CATCCTCGTCCATCCCACCACATAC-3’ ) , exon15 splice-blocking ninl ( 5’- CCCAACACTAAAGAGATACACCAAT-3’ ) , exon4 splice-blocking dzank1 ( 5’- CGGCCATCACTGCATCACATTACAA-3’ ) exon8 splice-blocking dzank1 ( 5’- AGGACATCTTTAGAATGATAGACGT-3’ ) and translation blocking dync1h1 ( 5’- CGCCGCTGTCAGACATTTCCTACAC-3’ ) morpholinos ( MOs ) were designed by Gene Tools Inc . ( USA ) and diluted to the appropriate concentration in deionized , sterile water , supplemented with 0 . 5% phenol red . To determine the most effective dose of the ninl , dzank1 and dync1h1 MOs , 1 nl of diluted MO ( containing 2 , 4 , 6 , 8 and 10 ng ) was injected into the yolk of one- to two-cell-stage embryos using a Pneumatic Picopump pv280 ( World Precision Instruments ) . A minimum sample size of 50 larvae was used for every condition . After injection , embryos were cultured in E3 embryo medium ( 5 mM NaCl , 0 . 17 mM KCl , 0 . 33 mM CaCl2 , 0 . 33 mM MgSO4 , supplemented with 0 . 1% methylene blue ) at 28°C and subsequently phenotyped at 4 dpf ( days post fertilization ) . Injected embryos were classified into two classes of phenotypes based on the relative severity as compared to age-matched , standard control ( 5’-cctcttacctcagttacaatttatac-3’; Gene Tools Inc , USA ) MO-injected ( 10 ng ) embryos of the same clutch . Images were taken with an Axioplan2 Imaging fluorescence microscope ( Zeiss , Germany ) equipped with a DC350FX camera ( Zeiss , Germany ) . To determine the efficiency of splice-blocking , RNA was isolated from 50 control MO injected and 50 dzank1- ( and ninl- ) splice MO-injected embryos ( 2 dpf ) using the RNeasy mini kit ( Qiagen ) according to manufacturer’s instructions . Here , 500 ng of total RNA was used to produce first-strand cDNA . Reverse transcription was performed using the Superscript III cDNA synthesis kit ( Life Technologies ) according to the manufacturer’s instructions . Subsequently , PCR analysis was performed . Primers used for the analysis of ninl exon15 morphants are 5’-AAGTATGATGGCCTGGATGC-3’ and 5’-GAGATGTCCTTCCGCTCAAC-3’; primers used for the analysis of dzank1 exon4 morphants are 5’-GGCAGCACCTCAAATAATCC-3’ and 5’-CTGAAGGTCGATGGCTAAGG-3’; primers used for the analysis of dzank1 exon8 morphants are 5’-CTCGCTTGACAGCACAAAAC-3’ and 5’-AAAACAGGTCTGGCTTGTCG-3’ . Obtained fragments were extracted from a 1% agarose gel using the Nucleospin gel extraction kit ( Machery Nagel , USA ) and Sanger sequenced . For histological analysis of zebrafish , larvae were fixed in 4% PFA in PBS at 4°C overnight . Embryos were rinsed with PBS and infiltrated in 10% sucrose solution in PBS for two hours . Embryos were positioned ( ventral side downwards ) in Tissue Tek ( Sakura ) , rapidly frozen in melting isopropyl alcohol and sections ( seven μm thickness along the lens/optic nerve axis ) were made . Immunohistochemistry was performed using retina sections , derived from four-to-six day old morphants and age-matched control oligo MO-injected zebrafish . The bodipy staining was performed on 5 day old larvae . The sections were washed twice in PBS for 5 minutes , permeabilized with 0 . 5% triton-x-100 in PBS for two times 10 minutes and followed by three washing steps with PBS for 5 minutes . Sections were then incubated for 10 minutes with bodipy ( 1:100 ) , DAPI and phalloidin/actin ( monoclonal Actin , Abcam lotnr: Ab328–500 ( 1:400 ) ) diluted in PBS . Subsequent photoreceptor outer segment length measurements were performed blinded as to their injection status ( using ImageJ ) . Equivalent single confocal sections through each eye were selected and the outer segments from 10 adajcent photoreceptors were measured and averaged for each larvae . P-values were calculated using Student’s t-test ( two tailed , unpaired ) . HEK293T cells were cultured as described previously [47] . For SILAC experiments , HEK293T cells were grown in SILAC DMEM ( PAA ) , supplemented with 3 mM L-Glutamine ( PAA ) , 10% dialyzed fetal bovine serum ( PAA ) , 0 . 55 mM lysine and 0 . 4 mM arginine . Light SILAC medium was supplemented with 12C6 , 14N2 lysine and 12C6 , 14N4 arginine . Heavy SILAC medium was supplemented with either 13C6 lysine and 13C6 , 15N4 arginine or 13C6 , 15N2 lysine and 13C6 , 15N4 arginine . 0 . 5 mM proline was added to all SILAC media to prevent arginine to proline conversion . All amino acids were purchased from Silantes . For DNA transfections , HEK293T cells were seeded , grown overnight , and then transfected using PEI . HEK293T ( human embryonic kidney , ATCC ) cells were transfected for 48 hours with either SF-TAP-NINL , SF-TAP-DZANK1 , using polyethyleneimine ( PEI , Polysciences ) as a transfection reagent . Following transfection , cells were lysed in lysis buffer containing 30 mM Tris–HCl ( pH 7 . 4 ) , 150 mM NaCl , 0 . 5% Nonidet-P40 ( NP40 ) , freshly supplemented with protease inhibitor cocktail ( Roche ) , phosphatase inhibitor cocktail II and III ( Sigma ) , for 20 minutes at 4°C . The Streptavidin- and FLAG-based tandem affinity purification steps were performed as previously described [47 , 48] . 5% of the final eluate was evaluated by SDS-PAGE followed by silver staining , according to standard protocols , while the remaining 95% were subjected to protein precipitation with chloroform and methanol . Protein precipitates were subsequently subjected to mass spectrometry analysis and peptide identification as previously described [25] . For SILAC experiments , one step Strep purifications of SF-TAP-tagged proteins and associated protein complexes was performed as described earlier [49] . HEK293T cells , transiently expressing the SF-TAP-tagged constructs were lysed in lysis buffer , containing 0 . 5% Nonidet-P40 , protease inhibitor cocktail ( Roche ) and phosphatase inhibitor cocktails II and III ( Sigma-Aldrich ) in TBS ( 30 mM Tris-HCl ( pH 7 . 4 ) , 150 mM NaCl ) , for 20 minutes at 4°C . After sedimentation of nuclei at 10 , 000 x g for 10 minutes , the protein concentration was determined by a Bradford assay , before equal amounts of each lysate were transferred to Strep-Tactin-Superflow beads ( IBA ) and were incubated for one hour at 4°C on an end-over-end shaker . Then , the resin was washed three times with wash buffer ( TBS containing 0 . 1% NP-40 , phosphatase inhibitor cocktail II and III ) . The protein complexes were eluted by incubation for 10 minutes in Strep-elution buffer ( IBA ) . The eluted samples were concentrated using 10 kDa cut-off VivaSpin 500 centrifugal devices ( Sartorius Stedim Biotech ) and pre-fractionated using 1D-SDS-Page . Afterwards , the samples were subjected to in-gel tryptic cleavage as described elsewhere [50] . For EPASIS , SF-TAP-tagged NINL was over-expressed in HEK293T cells as described above . After 48 hours , cells were lysed as described for SF-TAP analysis and the cleared lysates were incubated with anti-FLAG-M2 agarose resin for 1h . After three washes with wash buffer ( TBS containing 0 . 1% Tergitol-type NP-40 and phosphatase inhibitor cocktails II and III , Sigma-Aldrich ) , the resin was incubated at 4°C for three minutes with each concentration of SDS ( 0 . 001% , 0 . 005% , 0 . 01% and 0 . 02% ) in SDS-elution buffer ( TBS containing phosphatase inhibitor cocktails II and III ) . Afterwards , a final elution step with FLAG peptide ( 200 μg/ml; Sigma-Aldrich ) in wash buffer was performed . After every elution step a single wash step was performed . The flow-through was collected and precipitated by methanol-chloroform , before being analyzed by LC-MS/MS . LC-MS/MS analysis was performed on an Ultimate3000 nano RSLC system ( Thermo Scientific ) coupled to a LTQ Orbitrap Velos or to an LTQ OrbitrapXL mass spectrometer ( Thermo Scientific ) by a nano spray ion source . Tryptic peptide mixtures were automatically injected and loaded at a flow rate of 6 μl/min in 0 . 1% trifluoroacetic acid in HPLC-grade water onto a nano trap column ( 75 μm internal diameter ( i . d . ) × 2 cm , packed with Acclaim PepMap100 C18 , 3 μm , 100 Å; Thermo Scientific ) . After five minutes , peptides were eluted and separated on the analytical column ( 75 μm i . d . × 25 cm , Acclaim PepMap RSLC C18 , 2μm , 100 Å; Thermo Scientific ) by a linear gradient from 2% to 35% of buffer B ( 80% acteto-nitrile and 0 . 08% formic acid in HPLC-grade water ) in buffer A ( 2% aceto-nitrile and 0 . 1% formic acid in HPLC-grade water ) at a flow rate of 300 nl/min over 33 minutes for EPASIS samples , respectively over 80 minutes for SF-TAP and SILAC samples . Remaining peptides were eluted by a short gradient from 35% to 95% buffer B in 5 minutes . The eluted peptides were analyzed by a LTQ Orbitrap Velos or a LTQ Orbitrap XL mass spectrometer . From the high resolution MS pre-scan with a mass range of 300 to 1500 , the ten most intense peptide ions were selected for fragment analysis in the linear ion trap if they exceeded an intensity of at least 200 counts and if they were at least doubly charged . The normalized collision energy for CID was set to a value of 35% and the resulting fragments were detected with normal resolution in the linear ion trap . The lock mass option was activated; the background signal with a mass of 445 . 12002 as lock mass . Every ion selected for fragmentation , was excluded for 20 seconds by dynamic exclusion . Non-quantitative MS/MS data were analyzed , using Mascot ( version 2 . 4 , Matrix Science , Boston , MA , USA ) . Mascot was set up to search the human subset of the Swiss Prot database ( Release 2013_12 , 20274 entries ) , assuming trypsin as the digestion enzyme . Mascot was searched with a fragment ion mass tolerance of 0 . 5 Da and a parent ion tolerance of 10 . 0 PPM . Oxidation of methionine and was specified as variable modification , iodoacetamide derivative of cysteine as fixed . The Mascot results were loaded in Scaffold ( version Scaffold_4 , Proteome Software Inc . , Portland , OR ) to validate MS/MS based peptide and protein identifications . Peptide identifications were accepted if they could be established at greater than 95 . 0% probability as specified by the Peptide Prophet algorithm [51] . Protein identifications were accepted if they could be established at greater than 99 . 0% probability and contained at least two identified peptides . Protein probabilities were assigned by the Protein Prophet algorithm [52] . Proteins , which contained similar peptides and could not be differentiated based on MS/MS analysis alone , were grouped to satisfy the principles of parsimony . For SILAC experiments , all acquired spectra were processed and analyzed , using the MaxQuant software [53] ( version 1 . 3 . 0 . 5 ) and the human subset of the human proteome reference set , provided by SwissProt ( release 2012_01 534 , 242 entries ) was used for peptide and protein identification . Cysteine carbamidomethylation was selected as fixed modification , methionine oxidation and protein acetylation were allowed as variable modifications . The peptide and protein false discovery rates were set to 1% . Contaminants like keratins were removed . Proteins , identified and quantified by at least two unique peptides were considered for further analysis . The significance values were determined by Perseus tool ( part of MaxQuant ) , using significance A . Label-free quantification and statistical analysis of the EPASIS data were performed as previously described [25] using MaxQuant ( version 1 . 3 . 0 . 5 ) . The human subset of the human proteome reference set , provided by SwissProt ( Release 2012_01 534 , 242 entries ) was used for peptide and protein identification . Seven biological replicates with five fractions each were performed for the NINL experiment and three biological replicates for the control experiment , resulting in a total number of 50 individual samples being measured . Proteins had to be present in at least 4/7 ( 57% ) repeated experiments to be considered for further analysis . The reproducibility of the experiments was analyzed as already described [25] and is shown in S6 and S7 Figs To assign the proteins to the pre-defined sub-complexes ( S4 Table [26 , 54] ) , the EPD threshold was determined by a stepwise parameter search ( n = 1000 ) , which resulted in a value of 0 . 089 ( S8 Fig ) . Zebrafish ( 4 dpf ) were fixed at 4°C overnight in a freshly prepared mixture of 2 , 5% glutaraldehyde and 2% PFA in 0 . 1 M sodium cacodylate buffer ( pH 7 . 4 ) . After rinsing in buffer , specimens were post-fixated in a freshly prepared mixture , containing 1% osmium tetroxide and 1% potassium ferrocyanide in 0 . 1 M sodium cacodylate buffer ( pH 7 . 4 ) , at room temperature during 2 hours . After rinsing , tissues were dehydrated through a graded series of ethanol and embedded in epon . Ultrathin ( rostrocaudally ) sections ( 70 nm ) , comprising zebrafish eyes at the optic nerve level , were collected on Formvar coated grids , subsequently stained with 2% uranyl acetate and Reynold’s lead citrate , and examined with a Jeol1010 electron microscope . Using Adobe Photoshop version 8 . 0 , TEM images were adjusted for brightness and contrast . To compare the degree of vesiculation in the inner segments of the various experimental groups , quantitative TEM analysis was accomplished . To this end , 8000x magnification images of the central retina ( 50% middle arc length ) were acquired . For each zebrafish group , several eyes and fields of view in the retina were evaluated ( S5 Table ) . For each field of view , the total number of photoreceptor cells was counted . Finally , each photoreceptor cell was evaluated for presence of vesicles . The statistical significance of differences between two groups was assessed using the independent samples Student’s t-test ( SPSS 20 . 0 ) . Morphant groups were analyzed and compared versus the wild type group , as well as versus the mock-injected group . The statistical significance was set at p < 0 . 05 . Data are presented as means ± SEM . The OKR was measured by a previously described method [55] . Zebrafish larvae were mounted in an upright position in 3% methylcellulose in a small Petri dish . The Petri dish was placed on a platform surrounded by a rotating drum 8 cm in diameter . A pattern of alternating black and white vertical stripes was displayed on the drum interior ( each stripe subtended an angle of 36°C ) . Larvae ( 4 dpf ) were visualized through a stereomicroscope positioned over the drum and illuminated with fiberoptic lights . Eye movements were recorded while larvae were optically stimulated by the rotating stripes . Larvae were subjected to a protocol of a 30 seconds counterclockwise rotation , a 10 seconds rest , and a 30 seconds clockwise rotation . To induce melanosome retraction P5 larvae were exposed to epinephrine ( Sigma E4375 ) at a final concentration of 500 μg/ml . Melanosome retraction was continuously monitored under the microscope and the endpoint was scored when all melanosomes in the head ( and the trunk ) were perinuclear [28] . P-values were obtained using Student’s t-test ( two tailed , unpaired ) . For all quantifications of zebrafish experiments , the Graphpad Prism6 software ( http://www . graphpad . com/scientific-software/prism/ ) was employed to generate scatter plots , calculate mean values and SEM values , and perform statistical tests . Continuous data was analyzed using two-tailed , unpaired Student’s t-test and categorical data was analyzed using Fisher’s exact test . | The cytoplasmic dynein 1 motor complex is known to be essential for photoreceptor outer segment formation and function . NINL , an important interaction partner of three ciliopathy-associated proteins ( lebercilin , USH2A and CC2D2A ) , was previously shown to associate with this motor complex . In this work , we scrutinize the role of NINL using a combination of affinity proteomics and zebrafish studies , in order to gain insight into the pathogenic mechanisms underlying these three associated hereditary disorders . We identify DZANK1 as an important interaction partner of NINL and show that loss of Ninl , Dzank1 , or a combination of both synergistically results in impaired transport of trans Golgi-derived vesicles and , as a consequence , defective photoreceptor outer segment formation . Using affinity proteomics , we demonstrate that NINL and DZANK1 associate with complementary subunits of the cytoplasmic dynein 1 complex . Our results support a model in which the NINL-DZANK1 protein module is essential for the proper assembly and folding of the cytoplasmic dynein 1 motor complex , shedding light on the structure and stoichiometry of this important motor complex . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | NINL and DZANK1 Co-function in Vesicle Transport and Are Essential for Photoreceptor Development in Zebrafish |
The underlying mechanisms resulting in the profound immune suppression characteristic of human visceral leishmaniasis ( VL ) are not fully understood . Here , we tested the hypothesis that arginase , an enzyme associated with immunosuppression , is higher in patients with VL and contributes to impaired T cell responses . We recruited patients with VL before and after treatment and healthy controls and measured the arginase metabolism in the blood of these individuals . Our results show that arginase activity is significantly higher in the blood of patients with active VL as compared to controls . These high levels of arginase decline considerably once the patients are successfully treated . We identified the phenotype of arginase-expressing cells among PBMCs as neutrophils and show that their frequency was increased in PBMCs of patients before treatment; this coincides with reduced levels of L-arginine in the plasma and decreased expression levels of CD3ζ in T cells .
Visceral leishmaniasis ( VL ) is a neglected tropical disease caused by parasites of the Leishmania ( L . ) donovani complex . The incidence of VL is estimated to be 500 000 cases every year , with approximately 50 000 deaths , predominantly in India , Bangladesh , Brazil , Nepal and Sudan . It inflicts an immense toll on the developing world and impedes economic development; it is the second biggest parasitic killer in the world after malaria , with an estimated loss of 2 . 3 million disability-adjusted life years . There is no efficient vaccine; currently used chemotherapy is toxic and increasing drug resistance is reported [1] . VL can be asymptomatic or can manifest as a progressive disease characterised by hepatosplenomegaly , fever , weight loss , hyperglobulinemia and pancytopenia [1] . In Ethiopia , VL is caused by L . donovani and it is one of the most significant vector-borne diseases; Ethiopia has the second largest number of VL cases in sub-Saharan Africa with an estimated annual burden of 4500 to 5000 new cases [2] . Sodium stibogluconate ( SSG ) is still the main drug used as first line of treatment in Ethiopia [2] and a case fatality rate of 13% has been reported [3] . VL is worsened by malnutrition and HIV co-infection , and treatment access is often difficult because of the remote location of areas endemic for VL . Based on extensive studies in mouse models , healing of disease and host protection against Leishmania infection is dependent on the development of a T helper ( Th ) 1 type response , characterised by the production of IFN-γ and nonhealing is associated with an IL-4-dominated Th2 type response ( reviewed in [4] ) . However , concentrations of these two cytokines in plasma of symptomatic patients with visceral leishmaniasis ( VL patients ) do not always correlate with clinical outcome: studies have reported both an increase [5] , [6] , [7] , or decrease [9] in IFN-γ levels during the active phase of VL . Whereas IL-4 increases in plasma of VL patients in some studies [8] , [9] , [10] , it has also been shown to be below detection limit in other cases [6] , [11] . In contrast , increased levels of IL-10 , a potent immunosuppressive cytokine , have consistently been associated with symptomatic VL ( reviewed in [12] ) , suggesting that rather than a set Th1 or Th2 type response , IL-10 plays a crucial role in chronic nonhealing/or symptomatic VL . Indeed , one of the key immunological characteristics of active VL is a profound immunosuppression , as demonstrated by the failure of PBMCs to produce IFN-γ and proliferate in response to Leishmania antigen; this impaired capacity to respond to antigenic challenge is however restored following successful chemotherapy [6] and reviewed in [12] , [13] . Arginase-induced L-arginine metabolism has been identified as a potent mechanism of immune suppression [14] , [15] , [16] . We have recently shown in an experimental model of cutaneous leishmaniasis that high arginase activity , a hallmark of nonhealing disease , is primarily expressed locally at the site of pathology . The high arginase activity caused local depletion of L-arginine , which impaired the capacity of CD4+ T cells in the lesion to proliferate and to produce interferon-gamma , while T cells in the local draining lymph nodes responded normally . Healing resulted in control of arginase activity and reversal of local immunosuppression . Furthermore , inhibition of arginase as well as supplementation with L-arginine restored T cell effector functions and reduced parasite growth at the site of lesions [17] . In addition , it has been recently shown in a hamster model of visceral leishmaniasis that increased arginase activity resulted in impaired control of infection [18] . In the present study , we tested the hypothesis that during human visceral leishmaniasis , arginase activity increases and results in depletion of L-arginine .
The study was approved by the Ethiopian National Research Ethics Review Committee ( NRERC , reference 310/18/03 ) , by Addis Ababa University Medical Faculty Institutional Review Board ( IRB , reference 023/2009 ) and by the Joint UCL/UCLH Committees on the Ethics of Human Research ( Committee Alpha , reference 09/H0715/93 ) . For this study , 26 patients with visceral leishmaniasis were recruited from the Leishmaniasis Treatment and Research Center of Gondar University Hospital before treatment . The exclusion criteria were age ( <5 ) , tuberculosis , malaria , HIV and pregnancy; no women presented with visceral leishmaniasis during our study , all patients were male migrant workers . The diagnosis of VL was based on positive serology ( rK39 ) and presence of amastigotes in spleen or bone marrow aspirates . Fourteen male healthy controls with no prior history of VL were recruited among the staff ( 10 controls ) in the clinic and from the patients' household contacts ( 4 controls ) . Informed written consent was obtained from each patient or parent/guardian and control . 10–20 ml of blood was collected in EDTA tubes before the treatment started . All patients were treated with 20 mg/kg/day of SSG for 30 days and showed an initial clinical cure rate of 100% after treatment . In addition there was no significant treatment related adverse event . A further 10 ml of blood was collected from 14 patients 21 to 28 days after treatment had started . Of note , in all the tests performed in this study , no significant differences were obtained between patients after 3 weeks of treatment as compared to 4 weeks of treatment . It was not possible to always analyse the samples from all patients before and after treatment for two reasons: poor medical conditions of the patients at arrival in the hospital and/or frequent electricity cuts that did not allow for immediate processing of the blood . Plasma was isolated by centrifuging 1 ml of blood at 1800 rpm for 10 min and was frozen at −20°C until further use . Peripheral blood mononuclear cells ( PBMCs ) were isolated by density gradient centrifugation on Histopaque-1077 ( Sigma ) . Cells were counted by trypan blue exclusion , washed in phosphate buffered saline ( PBS ) and were used immediately for flow cytometry; PBMCs used for arginase and protein determination were immediately resuspended in lysis buffer ( 0 . 1% Triton X-100 , 25 mM Tris-HCl and 10 mM MnCl2 , Sigma ) and frozen at −20°C until further use . The following tests were used to diagnose the presence of HIV: KHB Shanghai Kehua Bio-engineering Co . Ltd and Chembio HIV 1/2 STAT-PAK; Uni-Gold ( Trinity Biotech PLC ) was used to resolve ambiguous results . CD4+ and CD8+ T cell counts were determined using a BD Multi TEST kit ( BD Biosciences ) and acquisition was performed using a FACSCalibur ( BD Biosciences ) . Haematological data ( platelet counts , white blood cell counts , hematocrit and haemoglobin ) were obtained with a COULTER Ac•T diff Hematology Analyzer . The enzymatic activity of arginase in PBMCs and in plasma and protein concentration of PBMC samples was measured as previously described [19] . L-arginine concentrations were determined in plasma obtained from citrated blood either directly or after concentrating cationic amino acids using Oasis MCX ion exchange columns ( Waters , Eschborn , Germany ) as described before [20] . Antibodies used were as follows: anti-CD4 ( clone 13B8 . 2 , Beckman Coulter ) , anti-CD8 ( clone RPA-T8 , BD Biosciences ) , anti-CD3ζ ( Santa Cruz: clone 6B10 . 2 ) , anti-CD14 ( BD Pharmingen: cloneM5E2 ) , anti-CD15 ( Clone H198 , BD Pharmingen ) and anti-CD63 ( Beckman Coulter: CLBGran/12 ) ; anti-arginase I ( HyCult Biotechnology: clone 6G3 ) and the isotype control ( BD Pharmingen: clone MOPC21 ) were coupled with Alexa FluorR 488 ( Molecular Probes ) . Cells were washed with PBS , the fixation step was performed with 2% formaldehyde in PBS and the permeabilisation step with 0 . 5% saponin in PBS . The determination of intracellular arginase was performed as described in [21] . The percentages for the isotype controls were <1% . Acquisition was performed using a FACSCalibur ( BD Biosciences ) and data were analyzed using Summit v4 . 3 software . Data were evaluated for statistical differences using a two-tailed Mann-Whitney test ( GraphPad Prism 5 ) and differences were considered statistically significant at p<0 . 05 . Unless otherwise specified , results are expressed as median± SEM .
All patients recruited in this study were male with a median age of 22±1 . 1 years and presented with severe VL as shown by the clinical data presented in Table 1: all but one patients had enlarged spleen and/or liver and parasites were detected in the spleen or bone marrow aspirates of all patients . As shown in Table 1 , the duration of illness varied from 2 to 24 weeks . In addition , the following parameters were measured: platelet and white blood cell counts , hematocrit and haemoglobin . Platelets and WBC counts were noticeably lower than the normal range in the large majority of the patients ( Table 2 ) . In addition , all but one patient had hematocrit and haemoglobin levels below the lower limit of normal ( Table 2 ) . The nutritional status of VL patients was determined by calculating their body mass index ( BMI ) and their upper arm circumference: the majority of the patients had a BMI below 18 . 5 ( median±SEM: 16 . 5±0 . 3 ) : out of 25 patients , 11 were malnourished [22] ( BMI<18 . 5 ) and 12 were severely malnourished [22] ( BMI<16 ) ; the BMI of all controls were above 18 . 5 ( 21 . 5±0 . 8 ) ( Figure 1A ) . We did not find a significant difference between the BMI of the 4 endemic controls and the 8 controls recruited among the staff of the hospital ( 20 . 7±1 . 2 vs 22 . 4±0 . 7 respectively , p = 0 . 2828 , data not shown ) . The median arm circumference was also significantly lower in VL patients ( controls: 26 . 0±0 . 4 cm , patients: 21 . 0±0 . 8 cm , p<0 . 0001 , Figure 1B ) . CD4+ and CD8+ T cell counts were assessed and results in Figures 1C and D show that they were markedly reduced in the blood of VL patients ( CD4+ T cell counts ( cell/µl blood ) = 159 . 0±22 . 7; CD8+ T cell counts: 164 . 0±24 . 7 ) . CD4+ and CD8+ T cell counts from controls were in the reference range ( 553 . 5±53 . 9 and 724 . 4±70 . 1 cells/µl blood , respectively ) . As shown in Figure 2 , statistically significantly higher levels of arginase activity were measured in PBMCs of VL patients as compared to controls ( patients: 106 . 6±11 . 0 , controls: 45 . 9±6 . 7 mU/mg protein respectively , p = 0 . 0045 ) . Following 3–4 weeks of treatment , these levels were considerably lower ( treated patients: 64 . 8±5 . 3 mU/mg protein , p = 0 . 0108 ) . These results show that the active phase of visceral leishmaniasis coincides with higher levels of arginase activity in PBMCs . To determine the phenotype of arginase-producing cells , a combination of extracellular ( anti-CD14 and anti-CD15 ) and intracellular ( anti-arginase ) markers was used [19] . The large majority of arginase-expressing cells in PBMCs of patients express CD15 , a typical marker of neutrophils ( 14 . 9% of 15 . 1% = 98 . 3% , Figure 3A ) ; the phenotype of arginase-expressing cells was similar ( CD14−CD15+arginase+ ) in the PBMCs of controls and treated patients ( data not illustrated ) . The frequency of CD15+ cells expressing arginase was >93 . 1% in all 17 patients and controls tested ( data not illustrated ) . Following density gradient purification , neutrophils co-purify in the erythrocyte fraction and not with the PBMCs . However , here we identify arginase-expressing CD15+ cells in the PBMC fraction , this population will therefore be referred to as low-density granulocytes ( LDGs ) . The frequency of LDGs was statistically significantly higher in the PBMCs of VL patients as compared to controls ( patients: 13 . 6±3 . 4 , controls: 3 . 5±0 . 7 , p = 0 . 0001 , Figure 3C ) . Following 3–4 weeks of treatment , the frequency of LDGs was considerably lower ( treated patients: 4 . 1±1 . 3 , p = 0 . 0006 ) . Similarly , the number of LDGs per ml of blood was higher in patients as compared to controls and treated patients ( Table 3 ) . CD14+ cells of the large majority of patients ( 14 out of 17 ) did not express arginase ( the percentages of CD14+ arginase+ cells were <1% , Figure 3B ) , with only a small percentage of CD14+arginase+ cells detected in the PBMCs of 3 patients ( 1 . 7% , 3 . 5% and 2 . 8% , data not illustrated ) . The percentages of CD14+ cells were lower during the active phase of the disease ( controls: 15 . 1±1 . 5 and patients: 7 . 8±1 . 2 , p = 0 . 0040 ) , and did not change significantly after 3–4 weeks of treatment ( patients: 7 . 8±1 . 2 and treated patients: 15 . 7±2 . 7 , p = 0 . 1013 ) ( Figure 4A ) . Similar results were obtained with the number of CD14+ cells per ml of blood ( Table 3 ) . The ratio of LDGs versus monocytes was higher before treatment in VL patients ( 1 . 97±0 . 63 ) as compared to patients after 3–4 weeks of treatment ( 0 . 34±0 . 12 , p = 0 . 0004 ) and controls ( 0 . 19±0 . 09 , p<0 . 0001 ) ( Figure 4B ) . Of note , the white blood cell count per ml of blood was lower in patients before the treatment as compared to controls and to treated patients ( Table 3 ) . Activation of neutrophils is accompanied by exocytosis of arginase-containing azurophilic granules [23] , which express CD63 . Release of these granules results in the incorporation of CD63 into the membrane of neutrophils [24] . Therefore , we measured the expression levels of CD63 and arginase in LDGs . As shown in Figure 5A , CD63 MFI was statistically significantly higher on LDGs isolated from the PBMCs of patients before treatment as compared to controls ( patients: 16 . 4±3 . 1 , controls: 7 . 5±1 . 3 , p = 0 . 0066 ) and to treated patients ( 10 . 0±0 . 9 , p = 0 . 0232 ) , suggesting that the levels of degranulation are higher in LDGs from patients before treatment . Since arginase has been shown to be contained in the azurophilic granules found in the cytoplasm of neutrophils , we assessed its MFI in LDGs: the lower median values observed in the LDGs of VL patients did not reach statistical significance ( controls: 24 . 9±1 . 7 vs VL patients: 19 . 4±1 . 4 , p = 0 . 3126; VL patients: 19 . 4±1 . 4 vs treated patients: 23 . 4±1 . 0 , p = 0 . 0855 ) . Next , we measured the levels of arginase activity in the plasma and our results show that arginase activities were statistically significantly higher in the plasma of patients before treatment ( 23 . 9±4 . 8 mU/ml plasma ) as compared to controls ( 12 . 8±1 . 4 mU/ml plasma , p = 0 . 0117 ) and treated patients ( 13 . 9±1 . 1 mU/ml plasma , p = 0 . 0168 ) ( Figure 5C ) . Since the results shown in Figure 5C show higher levels of arginase activity in plasma from VL patients , we measured the levels of L-arginine in the plasma . As shown in Figure 6A , a sharp reduction in L-arginine levels was observed in the plasma of patients before treatment ( 105 . 6±12 . 4 µM ) as compared to healthy controls ( 179 . 0±31 . 6 µM , p = 0 . 0009 ) ; the increased median levels of L-arginine did not reach statistical significance in the plasma of patients after 3–4 weeks of treatment ( 164 . 7±19 . 8 µM , p = 0 . 2934 ) . It has been shown that decreased levels of L-arginine result in T cell suppression , as measured by decreased expression of CD3ζ [17] , [19] . We next assessed the expression levels of CD3ζ in CD4+ and CD8+ T cells in PBMCs: statistically significantly lower CD3ζ MFI in CD4+ T cells from patients before treatment ( 15 . 4±0 . 6 ) was observed as compared to controls ( 20 . 0±1 . 3 , p = 0 . 0008 ) ; higher CD3ζ MFI in CD4+ T cells that shows a trend towards significance was observed in the treated patients ( 17 . 4±0 . 8 , p = 0 . 081 ) ( Figure 6B ) . No statistical significance was observed in CD3ζ MFI in CD8+ T cells ( patients: 16 . 5±1 . 1 , controls: 20 . 4±1 . 5 , treated patients: 17 . 4±0 . 8 , Figure 6C ) .
PBMCs from patients with active VL lose the ability to mount an antigen-specific immune response , as shown by the inability of their T cells to proliferate and produce IFN-γ; this is however reversed after successful chemotherapy ( summarised in [12] , [13] ) . Here we identified a novel mechanism in human visceral leishmaniasis that might account for the poor T cell responses associated with active visceral leishmaniasis . We show that the activity of arginase , an enzyme that has been associated with immunosuppression , is higher before treatment in PBMCs and plasma of VL patients and decreases rapidly following successful treatment . Indeed , our results show that as early as 3 weeks post treatment , the levels of arginase are similar to those of controls . Similarly , the frequency of arginase-expressing cells in the PBMCs and their absolute counts were shown to be considerably greater during active disease , indicating that increased arginase activity and increased frequency of arginase-expressing cells are hallmarks of active visceral leishmaniasis . Further , we show that increased arginase activity and frequency and absolute numbers of LDGs in the blood of patients with active VL coincide with lower levels of L-arginine in the plasma and lower expression levels of CD3ζ in CD4+ T cells . We have previously shown that increased levels of arginase activity correlated with disease severity in HIV seropositive patients [21] . Here , no significant correlation was observed between any of the clinical and haematological parameters or the nutritional status ( data not shown ) and arginase activity or frequency of LDGs . This is likely to be due to the fact that the patients recruited in our study all presented with severe VL . Therefore , our results suggest that arginase activity is significantly increased in VL patients presenting with severe manifestations of the disease . We and others have already described LDGs [19] , [21] , [25] , [26]; following density gradient centrifugation , these cells co-purify with PBMCs and not with the erythrocyte fraction , suggesting that they are activated granulocytes [27] , which have changed their density . The degree of activation of neutrophils and degranulation depends on the strength of the activating signal and increased activation results in the release of granules in the following order: 1 ) secretory granules; 2 ) gelatinous ( tertiary ) granules; 3 ) specific ( secondary ) granules and 4 ) azurophilic ( primary ) granules . CD63 is found in the membrane of azurophilic granules and we show here that the MFI of CD63 , whose upregulation parallels release of azurophilic granule by activated neutrophils [24] , [28] , is significantly higher on LDGs from active VL patients . Whereas we have previously shown that arginase is contained in azurophilic granules [23] , arginase has also been detected in gelatinous granules [29] . Since azurophilic granules are the last granules to be released and since CD63 is selectively upregulated on neutrophils following release of azurophilic granules [24] , [28] , in agreement with our previous results [23] , we can conclude that arginase-containing azurophilic granules are released . And indeed , the levels of arginase activity are higher in the plasma from patients during the active phase of the disease . These results suggest that LDGs have degranulated and released their arginase . We have recently shown that a similar population of LDGs is significantly increased in the blood of HIV seropositive patients with low CD4+ T cell counts: this population also expressed higher CD63 and lower arginase MFIs; in addition , the levels of CD11b , CD15 , CD33 , CD66b , CD63 were significantly higher and that of CD16 significantly lower as compared to NDGs [30] . The majority of LDGs in HIV patients were mature segmented neutrophils [30] . However , the origins of these cells as well as the factors resulting in the degranulation of neutrophils during VL are still unclear . It is possible that during VL , neutrophils are activated in the spleen and in the liver and that a fraction of these activated neutrophils recirculate and are therefore detected in the PBMCs ( Figure 3 ) . An extensive and systemic activation of neutrophils would result in the release of large amount of arginase , widespread depletion of L-arginine and major disruption of many cellular and organ functions and is therefore unlikely . In contrast to our results , a recent study by Mukhopadhyay et al showed that the levels of arginase in the plasma of VL patients in India are not affected by treatment with sodium antimony gluconate [31] . These discrepancies are likely to be due to the different techniques used to measure arginase activity: in the present study , we deducted the levels of urea present in the plasma from the values obtained following activation of arginase . Of note , even though our results suggest that LDGs have degranulated , we cannot conclude from the present study that the increased arginase we detect in the plasma of VL patients is coming from LDGs , as it could also have been released from damaged hepatocytes . The increased arginase activity in PBMCs and plasma coincides with lower levels of L-arginine . We have previously shown that increased arginase activity , a marker of disease severity in HIV+ patients , correlated with decreased levels of L-arginine [21] . Another study has also described a correlation between elevated arginase activities and lower levels of L-arginine in patients with pulmonary tuberculosis , that was reversed following successful treatment [32] . In our study , in addition to the increased arginase activity measured in the plasma of patients with active VL , it is possible that malnutrition also impacted on the lower levels of L-arginine . Out of 25 patients , 11 were malnourished and 12 were severely malnourished . Since malnutrition results in reduced levels of L-arginine in the plasma [33] , [34] , [35] , it is likely that this has also contributed to the lower levels of L-arginine in the plasma . Further , since the patients do not get nutritional supplements during treatment , malnutrition could also explain why the levels of L-arginine do not increase after 3–4 weeks of treatment . We had a limited number of patients in this study and our cohort of controls consisted of 4 endemic controls and 10 members of the staff of the hospital; we did not find significant differences between the nutritional status of these 2 subgroups of control . It will be interesting to compare the levels of L-arginine in the plasma of a large cohort of controls coming from endemic areas and from patients with active VL , to estimate the contribution of increased arginase and/or protein-energy malnutrition to the lower levels of L-arginine . It has been shown previously that arginase-induced L-arginine depletion results in impaired T cell responses ( reviewed in [14] , [15] , [16] , [36] . Our results suggest that during active VL , LDGs release arginase and we speculate that arginase-induced L-arginine depletion contributes to the poor antigen-specific T cell response that is an immunological hallmark of active VL; it is also possible that this mechanism negatively impacts on the capacity of immune cells to respond to other antigens and might therefore account for the frequent opportunistic infections observed in VL patients . The impaired Leishmania-specific T cell responses observed during active VL is reversed in cured patients ( [6] and reviewed in [12] , [13] ) . Our results show that CD3ζ MFI are significantly lower in CD4+ T cells , but not in CD8+ T cells , and 3–4 weeks post treatment , the increased median in CD3ζ in both cell type were not significantly different . It is possible that this lack of significance in CD3ζ in CD8+ T cells might be due to the limited number of patients recruited in this study . We have previously shown that CD4+ and CD8+ T cells both display decreased CD3ζ expression levels and this coincides with increased arginase activity [37] . The differences in L-arginine levels in the plasma were not statistically significant either; it is possible that this is due to the fact that the blood was collected only after 3–4 weeks of treatment . The observation that PBMCs from patients with active VL mount a poor antigen-specific response is in apparent contradiction with recent studies , which show a clear production of IFN-γ using a whole blood assay [38] , [39]: the authors of these studies have suggested that during the density gradient centrifugation used to isolate PBMCs , factors important for the production of IFN-γ might be removed . Our results obtained in a separate study show that heparin has a remarkable effect on the survival of LDGs . Indeed , our results show a dramatic decrease in the frequencies of LDGs when the blood was collected into sodium heparin as compared to EDTA: we compared the frequency of CD15+ arginase+ cells in the blood of 5 patients , collected in sodium heparin or EDTA and observed an average of 75 . 56±17 . 26% less CD15+arginase+ cells in blood collected in heparin compared to EDTA ( % of LDGs in blood collected on EDTA = 2 . 48±0 . 96 vs 0 . 59±0 . 55 for blood collected with heparin ( n = 5 ) , p>0 . 05 ) [40] . Since in the above-mentioned studies [38] , [39] , blood was collected into heparin , it is possible that the resulting low frequency of LDGs was not sufficient to suppress the production of IFN-γ . Notably , neutrophils are extremely difficult to freeze and indeed , we were not able to freeze LDGs: we froze the PBMCs of 8 patients in FCS containing 10% DMSO , and following careful thawing of the cells , we observe an average reduction of 97 . 3±4 . 9% in the frequency of CD15+arginase+ cells . Similar results were recently published [41] , where it was shown that the frequency of CD15+ cells in PBMCs was significantly reduced after cryopreservation . Since we did not have the facilities to perform stimulation assays with PBMCs from VL patients and since it is not possible to cryopreserve LDGs to send them in another laboratory with adequate facilities , we were therefore not able to directly demonstrate that LDGs have the ability to suppress T cell responses . We have shown in an experimental model of cutaneous leishmaniasis that increased arginase activity is highest at the site of pathology , and significantly lower in the draining lymph nodes [17] . Further , we have recently performed a study with patients with localised cutaneous leishmaniasis and have shown that consistent with the mouse model , arginase activity is increased in the lesions , but not in the periphery [37] . Therefore , it is possible that the higher frequencies of LDGs we observed in the PBMCs of VL patients before treatment are only a weak reflection of the events occurring at the local sites of Leishmania replication , such as spleen , liver or lymph nodes; and that considerably stronger T cell suppression is likely to occur in these sites . Indeed , a recent study showed in a mouse model of prostate-specific inflammation , that a population of Gr-1+ CD11b+ arginase+ cells isolated from the site of inflammation are more suppressive than those isolated from the periphery [42] . Further studies will be essential in assessing the strength of LDG-mediated T cell suppression in different compartments and to show whether arginase-mediated L-arginine metabolism is a key element in the outcome of VL . We and others have shown that Leishmania parasites express their own arginase [43] , [44] , [45] . However , it is highly unlikely that parasite arginase accounts for the arginase we detect in the PBMCs and in the plasma as it is very difficult to detect parasite in the blood of VL patients [46] . In summary , here we show that high arginase activity in PBMCs is a marker of active visceral leishmaniasis and we propose that arginase-induced L-arginine depletion contributes to disease severity in these patients . | Leishmaniases , a group of diseases caused by a parasite , Leishmania , belong to the most neglected tropical diseases: they are mainly found in low-income countries and affect the poorest populations . These parasites infect cells of the immune system called macrophages , which can kill the intracellular parasites when they receive the right signals from other cells of the immune system , the lymphocytes . During the active phase of visceral leishmaniasis , it has been shown that lymphocytes lose their capacity to instruct the macrophages to kill the intracellular parasites . Here , we show that the levels of an enzyme , arginase , are significantly increased in the blood of patients with visceral leishmaniasis , but decrease to the same levels as those of healthy controls following successful treatment . Arginase has the capacity to deplete an amino acid , L-arginine , which is crucial for the activation of lymphocytes . Indeed , our results show that the levels of this amino acid are considerably decreased in patients with visceral leishmaniasis . Our results suggest that during the active phase of visceral leishmaniasis , increased arginase results in the depletion of L-arginine , which is responsible for the incapacity of lymphocytes to send the adequate signals to the macrophages . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"immunomodulation",
"medicine",
"biochemistry",
"infectious",
"diseases",
"immune",
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"metabolic",
"pathways",
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"parasitology",
"immunology",
"biology",
"microbiology",
"metabolism",
"immune",
"response"
] | 2013 | Arginase Activity - A Marker of Disease Status in Patients with Visceral Leishmaniasis in Ethiopia |
The majority of investigations of the epidemiology of nontuberculous mycobacteria ( NTM ) have focused on highly developed nations with a low prevalence of tuberculosis . In contrast , the Para state of north Brazil represents an area of high tuberculosis prevalence and increasing NTM incidence . Toward the goal of understanding the dynamics of infection by all Mycobacterium species , we report patient characteristics and the identification of NTM strains isolated from sputum samples from patients that were residents of Para , a state in the Amazon region , Northern of Brazil , over the period January 2010 through December 2011 ( 2 years ) . The 29 NTM patients comprised 13 . 5% of positive mycobacterial cultures over the 2-year period . A major risk factor for NTM pulmonary disease was previous tuberculosis ( 76% ) . Further , the average age of NTM patients ( 52 years ) was significantly higher than that of tuberculosis patients ( 39 years ) and more were female ( 72 . 4% vs . 37 . 4% ) . Unlike other Brazilian states , NTM pulmonary patients in Para were infected with a different spectrum of mycobacteria; primarily the rapidly growing Mycobacterium massiliense and Mycobacterium simiae complex .
Nontuberculous mycobacteria ( NTM ) are environmental opportunistic pathogens that are natural inhabitants of soil [1] and drinking water [2] , [3] . Humans and their agronomic animals are literally surrounded by nontuberculous mycobacteria [4] . Risk factors for NTM pulmonary disease include: prior tuberculosis , chronic obstructive pulmonary disease ( COPD ) , lung damage due to occupational exposures to dusts ( e . g . , mining ) , cystic fibrosis or heterozygosity for a cystic fibrosis mutation , α-1-antitrypsin deficiency [5] . Fisherman and others exposed to fish are at risk for skin infections caused by Mycobacterium marinum infection [5] and children from 18 months to 5 years of age are at risk for cervical lymphadenitis caused more typically by M . avium [6] . Immunodeficiency , due to HIV-infection or immunosuppression due to cancer or chemotherapy are risk factors for Mycobacterium avium bacteremia [2] . Several case reports and studies on the prevalence of pulmonary disease caused by NTM in North America , Europe and Japan have been published during recent years [7] , [8] , [9] , [10] , [11] . Nevertheless , the impact and the exact magnitude of NTM infections in countries where tuberculosis is endemic are not known . Here , we report the identification of NTM strains isolated from pulmonary samples from patients with a presumptive diagnosis of pulmonary TB and residents of the State of Para , in the Amazon region , Northern of Brazil . This study documents the occurrence and diversity of species of NTM that cause pulmonary disease in a region representative of those in the world with high infection rates by M . tuberculosis .
Patients from routine laboratory presenting symptoms suggestive of mycobacterial disease ( e . g . , chronic cough ) and/or who were noted to have radiological alterations at medical examination , and from NTM were isolated at least once between January 2010 and December 2011 at the Evandro Chagas Institute , were included in this study . All the NTM isolates described in this study were obtained from pulmonary samples ( sputum , bronchoalveolar washes , and gastric washes samples ) of 38 individuals residents of the State of Para , North Brazil . Patient records were reviewed to assess the clinical data . Diagnostic criteria for NTM disease published by the American Thoracic Society ( ATS ) were applied to determine the clinical relevance of NTM isolation ( Table 1 ) [12] . The clinical samples were initially decontaminated , using the N-acetyl-L-cysteine-sodium hydroxide procedure [13] . The samples were subsequently inoculated onto Löwenstein–Jensen medium ( Difco , Sparks , USA ) and incubated at 35°–37°C in the absence of light for at least six weeks or until colonies appeared . Isolates of the M . tuberculosis complex were distinguished from NTM by the unique breadcrumb or cauliflower colony morphology of M . tuberculosis , and the production of cord factor and susceptibility to 0 . 5 mg/mL of para-nitrobenzoic acid by M . tuberculosis [14] . All subjects provided written consent by signing the free and informed consent form , and all patients data analyzed were anonymized . This study was approved by the ethics committee of the Evandro Chagas Institute ( protocol n° 017/10 , CAAE: 0017 . 0 . 072 . 000-10 ) . All NTM isolates of this study were identified by sequencing a portion of the 16S rRNA [15] and hsp65 [16] genes . The descriptive analysis were expressed as mean ± standard deviation or percentage , while analytical statistics was conducted using either non-parametric Chi-squared test or G-test , using the software BioEstat version 5 . 01 [17] . Statistical significance was defined as p<0 . 05 .
From January 2010 to December 2011 , a total of 69 NTM isolates were recovered from pulmonary specimens from 38 patients with respiratory symptoms that included chronic cough and alterations on chest X-ray . The patients and their characteristics are listed in Table 1 . The 38 patients represented 13 . 5% of culture-positive mycobacterial cultures obtained in our laboratory over that period of time . Of the 38 patients , 29 met the American Thoracic Society diagnostic criteria for NTM infection [12] . All patients were initially diagnosed as having pulmonary tuberculosis ( M . tuberculosis ) based on sputum smear microscopy for acid-fast bacilli ( AFB ) and had suffered a treatment failure . A summary of the characteristics of the 29 NTM patients meeting the criteria for NTM disease is provided in Table 2 . Among the patients with NTM disease the mean age was 52 . 3 years ( ±17 . 8 SD ) , and the mean time from onset of symptoms to NTM diagnosis was of 7 . 8 months ( ±13 . 5 SD ) . Twenty of the 29 patients ( 68 . 9% ) were above 50 years old , whereas 69 . 4% of tuberculosis cases ( X2 = 26 . 7; p<0 . 0001 ) reported in the state of Para are under 50 years old ( Figure 1 ) . Among the most frequent co-morbidities found were prior tuberculosis ( 22/29 , 75 . 8% ) and bronchiectasis ( 13/29 , 44 . 8% ) ( Table 2 ) . The results of chest X-ray ( CXR ) and high resolution computerized tomography ( CT ) examination are shown in Table 3 and Figure 2 . A number of 17 CXR and 12 CT findings of these 29 NTM-patients were reviewed . Atelectasis ( 12/17 , 70 . 5% ) and cavities ( 7/17 , 41 . 2% ) were the most frequent findings in CXR , while bronchiectasis ( 12/12 , 100% ) , centrilobular nodules/tree-in-bud ( 8/12 , 66 . 6% ) and cavities ( 6/12 , 50% ) were more frequently observed in the CT . Pleural thickening was detected in 8 ( 47 . 0% ) patients . Clinical manifestations of advanced lung disease , such as dyspnea and haemoptysis , occurred in 15 ( 88 . 2% ) patients . A total 26 out of the 29 NTM-infected patients ( 89 . 6% ) were classified as pardo , a Brazilian term for people of mixed white and indigenous heritage , who constitute the majority of the Para state population , with a total of 5 , 270 , 307 ( 69 . 5% ) of the population in the 2010 Brazil Census [18] . The frequency of pardo individuals with NTM was significantly different from the percentage of pardo in the state of Para ( X2 = 5 . 5; p = 0 . 0312 ) ( Table 1 ) . Among the 29 NTM-infected individuals , 21 were females ( 72 . 4% ) , aged between 19–84 years ( 50 . 9±18 . 3 SD ) . There was statistically significant difference in the occurrence of M . tuberculosis and NTM infection between males and females ( 62 . 6% in male with TB versus 72 . 4% in female with NTM; X2 = 15 . 1; p = 0 . 0002 ) . A total of five patients declared themselves as smokers . Roughly 72% ( 21/29 ) of patients were residents from an urban area , with 64 . 2% ( 18/21 ) of them having access to a water supply through piped systems . The difference between NTM urban and rural residents with access to piped water supply systems was found significant ( G-test = 21 . 3; p = 0 . 0001 ) . Eight different NTM species were identified from the 29 patients meeting the ATS criteria and included M . massiliense ( n = 13; 44 . 8% ) , M . avium ( n = 3; 10 . 3% ) , M . intracellulare ( n = 3; 10 . 3% ) , M . abscessus ( n = 2; 6 . 9% ) , M . bolletii ( n = 1; 3 . 4% ) , M . moriokaense ( n = 1; 3 . 4% ) , M . fortuitum ( n = 1; 3 . 4% ) , M . celatum ( n = 1; 3 . 4% ) and M . kansasii ( n = 1; 3 . 4% ) . Eight isolates ( 28% ) from three patients were identified as being related to the M . simiae complex by 16S rRNA sequence . The sequences obtained shared 100% similarity with the corresponding 16S rRNA ( GenBank accession number HM056101 ) and hsp65 gene sequences ( GenBank accession number HM056135 ) of Mycobacterium sp . IEC23 . The pulmonary infection by M . chelonae-M . abscessus complex members ( M . abscessus , M . massiliense and M . bollletii ) occurred in females with an average age of 60 . 7 years . Among the nine patients who did not meet the diagnostic criteria the NTM disease , the NTM species isolated included M . fortuitum ( n = 3; 33 . 3% ) , M . avium ( n = 2; 22 . 2% ) , M . gordonae ( n = 1; 11 . 1% ) , M . colombiense ( n = 1; 11 . 1% ) , M . intracellulare ( n = 1; 11 . 1% ) and M . abscessus ( n = 1; 11 . 1% ) .
About 80% ( 29/38 ) of all the NTM patients met the ATS criteria for NTM pulmonary disease [12] . Among nine cases that did not meet ATS criteria , one was highly suggestive of NTM infection . This patient showed both clinical symptoms of mycobacterial disease and a positive sputum smear . Such cases need to remain under observation and expert consultation sought [12] . This study clearly provides guidance in the diagnosis of NTM pulmonary disease in an area of high tuberculosis prevalence . Specifically , NTM-infected patients were older , more frequently female and had prior tuberculosis . In the Para state of Brazil , being of the pardo race was a risk factor for NTM disease . Roughly 70% of NTM pulmonary infections cases were patients over 50 years old , as other contemporary studies have shown [9] , [19] . These data also agree with the characteristics of a series of NTM-infected patients that had revealed an increased NTM-disease susceptibility among female , slender and older individuals [20] . A variety of factors may contribute to the observation that prior tuberculosis was found to be a risk factor for NTM disease: ( i ) lung damage resulting from prior tuberculosis infection reduces normal clearing of pathogens; ( ii ) a proportion of tuberculosis patients are at increased risk for mycobacterial infection , and this subset of tuberculosis patients would be at risk for nontuberculous mycobacterial infection; and ( iii ) as M . tuberculosis infection is associated with nutritional deficiency , that subset of individuals with prior tuberculosis would be expected to be of increased susceptibility to NTM infection [21] , [22] , [23] , [24] . COPD and cancer , diseases commonly associated with NTM disease , were less frequent in this series of case ( one case of each ) . In the USA , COPD was described as the main co-morbidity , being found in up to 28% of the cases , while neoplastic diseases have been reported in 25% of cases [25] , [26] . The fact that the average age of M . tuberculosis-infected patients was lower than that of the NTM patients is likely due to a number of reasons . First , M . tuberculosis is a highly virulent pathogen , capable of infecting healthy individuals; thus persons of all ages are susceptible . In contrast , the NTM are opportunistic pathogens; every NTM patient has some risk factor for infection . In developed countries , NTM disease is more frequently seen in older ( >60 years ) , slender ( <50 kg ) men and women who lack risk factors for M . tuberculosis infection [20] . All risk factors for NTM disease are unknown , although it has been shown that they are innately susceptible , as they are subject to repeated NTM infection [22] , [24] , [27] , [28] , [29] . In recent publication , Dirac et al [30] reported that prior lung disease and immunosuppression appear to be associated with susceptibility to NTM disease . Furthermore , it is well-known that elderly individuals generally have a worse response to infections than the young ones , possibly as the result of the immunosenescence . This condition has been associated to an increased susceptibility to infections , including mycobacterial infections [31] , [32] . A low proportion of HIV infected patients was observed among the NTM patients , as proven by serology in this study . However , this finding does not rule out the possibility of NTM-HIV co-infection in the study area , but instead it may point to the possibility of death of these patients by other causes , or perhaps even by disseminated NTM-infections , before appearance of respiratory NTM disease . Similar results have been found in Rio de Janeiro State , where 9 . 8% of NTM cases were diagnosed in HIV infected patients [33] . Even smaller proportion was found in the USA and Denmark , with 3 . 4% and 2 . 4% of HIV infected patients , respectively [26] , [34] . According to Sexton et al . [35] , this low frequency suggests that an abnormal airway mucosa is required as initiating factor for NTM disease . Among the HIV-infected patients in this study , all of them had history of previous tuberculosis and additionally smoking , co-morbidities that predispose to NTM pulmonary disease . NTM patients had a lower frequency of cavitary lesions compared to tuberculosis patients ( Table 4 ) . Although the radiographic features of NTM pulmonary infections are similar to those of tuberculosis , the presence of upper lobe cavitary lesions and endobronchial spread , bronchiectasis , as well as of fibroproductive nodules that change slowly , cicatricial atelectasis , and pleural thickening , were common findings in the patients of this study , which also had been shown in other studies [12] , [36] , [37] . The radiologic manifestations of pulmonary NTM has been classified basically as both cavitary ( “classic” ) or nodular-bronchiectatic ( “nonclassic” ) forms [37] , [38] , [39] . However , some NTM-cases could not be securely to fit into these categories , since they have exhibited the two forms combined . Others studies have tried to associate the patterns and forms of pulmonary lesions to NTM-species , suggesting a radiological differentiation between the diseases caused by MAC and M . abscessus . Briefly , has been proposed that nodular-bronquiectatic form is more frequent in patients with M . abscessus infection , while in those with MAC-infection the airspace consolidation and cavities are the most common findings [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] . However , these presentations did not agree with our results in all cases . We found that the majority of the patients were of the pardo race . The percentage of the NTM-patients reported here ( 89 . 6% ) is considerably higher than the percentage of pardo individuals in the state of Pará ( 69 . 5% ) . This could be due to either increased susceptibility of pardo individuals to NTM disease or greater opportunity of exposure to NTM-sources such as agricultural soils or drinking water [3] , [48] . Assignment of increased susceptibility is problematic as pardo individuals represent a heterogeneous , genetically diverse group . In this study we found 64 . 2% of the patients having access to a water supply through piped systems . This information is important because , even in urban area , as in Belém – capital of the State of Pará , the water supply is still precarious , with approximately 75% of homes having access to a water supply through piped systems , being the lowest coverage of them in the peripheral urban areas , according to the 2010 Brazil Census [18] . Most patients reported in this study were residents of peripheral urban areas ( data not shown ) . Based in the 16S rRNA sequencing analysis , a group of five isolates was classified as M . simiae complex ( MSC ) . Among the MSC members , only M . simiae species is recognized as potentially pathogenic to human and it is most commonly associated to cervical lymphadenitis . Nevertheless , the recovery of M . simiae from pulmonary specimens has been reported , especially in Israel , Cuba , and the southwestern United States [12] . Similarly , in previous study we found in our laboratory strains phylogenetically related to MSC as the most frequent NTM in pulmonary infection in Para State , Brazil [49] , [50] . RGM species , including M . abscessus , M . massiliense , M . bolletii ( formally M . abscessus species ) represented almost 45% of all NTM pulmonary cases , whereas in Sao Paulo , M . avium complex ( MAC ) and M . kansasii represent the most common NTM in pulmonary disease [51] , [52] . In addition , when stratifying to the NTM species level , we observed that gender associated infection was even more pronounced in the case of M . chelonae-M . abscessus complex ( ∼45% females ) , especially M . massiliense ( 34 . 5% ) . Griffith et al . [53] found a predominance of females ( 65% ) among 154 cases of pulmonary disease by RGM , while descriptions of particular forms of pulmonary disease caused by MAC in women have been reported [54] , [55] . Further studies are needed to elucidate the reasons for female susceptibility . A number of factors may have contributed to the higher frequency of individuals infected with M . massiliense than reported in other studies . First , M . massiliense may be more common in the soils and waters of the Para State of Brazil . Second , M . massiliense is a newly described species; unknown to investigators until recently [56] . Therefore , as M . massiliense and M . abscessus share a number of common characteristics that are used for identification , earlier published studies may have misidentified M . massiliense isolates as M . abscessus . Mycobacterial taxonomy has been undergoing substantial revision; for example it has recently been shown that standard tests for identification cannot distinguish between M . intracellulare and the newly described M . chimaera [57] . That , in turn , has led to the discovery that all water isolates of M . intracellulare are really M . chimaera , forcing a re-evaluation of M . intracellulare epidemiology and ecology [58] . In the present instance , the recent discovery of M . massiliense and its separation from M . abscessus suggests that earlier reports around the world reporting the frequency and numbers of M . abscessus infections may be incorrect; the isolates could have been M . massiliense . Thus , prior reports that form the basis for judging that the frequency of M . massiliense infections in the Para State is high may be incorrect . The results this study show that the clinical manifestations presented by the NTM-patients are suggestive of advanced disease , which reinforces the importance of the timely diagnosis of the NTM disease , since delayed treatment is associated with severe morbidity . | Nontuberculous mycobacteria ( NTM ) are environmental organisms that are naturally found in soil , water , dust and other sites . Several case reports and studies on the prevalence of pulmonary NTM disease have been published , nevertheless , the impact and the exact magnitude of NTM infections in countries where tuberculosis ( TB ) is endemic are not known . Here , we report the identification of NTM strains isolated from pulmonary samples from patients with a presumptive diagnosis of pulmonary TB and residents of the State of Para , in the Amazon region , Northern of Brazil . This study documents the occurrence and diversity of species of NTM that cause pulmonary disease in a region representative of those in the world with high infection rates by Mycobacterium tuberculosis . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"epidemiology"
] | 2013 | Occurrence of Nontuberculous Mycobacterial Pulmonary Infection in an Endemic Area of Tuberculosis |
Extracellular polysaccharides are key constituents of the biofilm matrix of many microorganisms . One critical carbohydrate component of Candida albicans biofilms , β-1 , 3 glucan , has been linked to biofilm protection from antifungal agents . In this study , we identify three glucan modification enzymes that function to deliver glucan from the cell to the extracellular matrix . These enzymes include two predicted glucan transferases and an exo-glucanase , encoded by BGL2 , PHR1 , and XOG1 , respectively . We show that the enzymes are crucial for both delivery of β-1 , 3 glucan to the biofilm matrix and for accumulation of mature matrix biomass . The enzymes do not appear to impact cell wall glucan content of biofilm cells , nor are they necessary for filamentation or biofilm formation . We demonstrate that mutants lacking these genes exhibit enhanced susceptibility to the commonly used antifungal , fluconazole , during biofilm growth only . Transcriptional analysis and biofilm phenotypes of strains with multiple mutations suggest that these enzymes act in a complementary fashion to distribute matrix downstream of the primary β-1 , 3 glucan synthase encoded by FKS1 . Furthermore , our observations suggest that this matrix delivery pathway works independently from the C . albicans ZAP1 matrix formation regulatory pathway . These glucan modification enzymes appear to play a biofilm-specific role in mediating the delivery and organization of mature biofilm matrix . We propose that the discovery of inhibitors for these enzymes would provide promising anti-biofilm therapeutics .
Candida spp . are an increasingly common cause of bloodstream infections in hospitalized patients [1] , [2] . This rise in incidence is at least in part related to the organism's ability to produce biofilm infections on medical devices [3] . A biofilm is a community of microbes attached to a surface and encased in an extracellular matrix [4]–[6] . The biofilm lifestyle is a common form of growth in nature and the most common cause of infection in humans . The most troublesome characteristic of biofilms is that they are up to 1 , 000-fold more resistant to common antifungals than their planktonic counterparts , even without accumulation of specific drug-resistance genes [7]–[10] . This lack of effective therapy contributes to dismal outcomes for patients with invasive candidiasis , including death in up to 40% of patients . Delineating the mechanisms of biofilm formation and associated treatment resistance is one method of identifying optimal management strategies and therapeutics of this devastating infectious disease . The focus of our investigations is the construction and configuration of the extracellular biofilm matrix , one of the properties that distinguishes biofilm from planktonic growth [11] . The function of matrix remains incompletely understood , but previous investigations have identified roles such as providing infrastructure for biofilm accumulation , controlling disaggregation , and granting protection from antimicrobial drugs and the host immune system [12]–[14] . Although the complete composition of the C . albicans biofilm matrix has yet to be fully elucidated , studies have identified the inclusion of carbohydrates , proteins , and nucleic acids components [11] , [13] , [15] . The goal of the present studies was to identify genes that control matrix delivery . We hypothesized that this process involves a biofilm-specific pathway composed of enzymes capable of modifying matrix carbohydrates . This hypothesis is based on two findings . First , one of the carbohydrates , β-1 , 3 glucan , has been linked to overall matrix production and drug resistance through glucan synthase gene FKS1 ( common nomenclature for the gene GSC1 ) [16] , [17] . Second , microarray analysis of in vivo rat catheter biofilms demonstrated transcript abundance of multiple glucan modification genes [18] . Here we use a candidate gene set to investigate the role of glucan matrix delivery . The gene set was selected to include glucan modification genes which demonstrated transcriptional upregulation in a rat venous catheter biofilm model . In addition , we included gene products which are known or hypothesized to utilize β-1 , 3 glucan as a substrate [19]–[25] . Many of the selected genes had been shown previously to function in planktonic cell wall synthesis and remodeling [23]–[30] . We constructed gene mutants and screened for biofilm formation , matrix delivery and antifungal drug susceptibility . In the current studies we describe the role of three glucan modifying genes for glucan delivery and matrix incorporation . These gene products encode two glucanosyltranferases ( BGL2 , PHR1 ) and a glucanase ( XOG1 ) , respectively [22] , [23] , [25]–[29] . Each appears necessary for modification and delivery of carbohydrate to the mature biofilm matrix . Without delivery and accumulation of matrix glucan , the biofilms exhibit enhanced susceptibility to antifungal drugs . As the biofilm matrix is integral for biofilm maintenance and drug resistance , these delivery enzymes provide promising targets for anti-biofilm drug development .
We have previously described the presence of β-1 , 3 glucan in the biofilm matrix of C . albicans and identified the role of the glucan synthase pathway for production of this material [16] , [17] , [30] . The machinery needed for delivery of this matrix component from the cell to the matrix was , however , not known . We reasoned that proteins that act upon a glucan substrate might contribute to the delivery process . Results of an in vivo microarray analysis of a rat venous catheter biofilm demonstrated differential expression of 11 potential glucan modification genes [18] . A candidate gene set was constructed by combining these 11 genes with 4 additional genes selected from a search of the Candida genome database for putative glucan modifying function ( glucanases , transferases , and glucosidases ) . A combination of homozygous deletion mutants were created for fourteen genes and a heterozygous mutant for one gene presumed to be essential ( Table S1 in Text S1 ) . Our initial experiments consisted of two screens . First , we examined overall biofilm growth in all strains . Each of the mutants produced mature in vitro biofilms similar to reference strains , with the exception the phr1−/− strain which exhibited a modest biofilm defect ( 75% cell burden compared to the reference strain ) . The phr−/− strain also demonstrated a modest defect in adhesion to a polystyrene substrate ( 67% relative to the reference strain ) . The mutant strains exhibited normal planktonic growth in YPD compared to the reference strain . Secondly , we measured the β-1 , 3 glucan concentrations in the matrix from mature in vitro biofilms using both the commercial limulus lysate assay ( Glucatell ) and a glucan ELISA . These assays identified three deletion mutants , bgl2−/− , xog1−/− , and phr1−/− , which produced up to 10-fold less matrix β-1 , 3 glucan than the reference biofilm ( Table 1 and Figure 1A ) . The observations were confirmed in independent transformants for each gene ( Table 1 ) . Furthermore , complementation of the mutants with a single copy of each gene restored glucan matrix concentrations to reference strain levels . The relevance of these glucan modification genes to in vivo biofilm growth is suggested by their transcriptional abundance in a rat venous catheter biofilm [18] . At 12 h of in vivo biofilm growth , microarray studies showed that transcription of BGL2 and PHR1 was upregulated . During mature biofilm growth ( 24 h ) , BGL2 and XOG1 transcripts were abundant . RT-PCR confirmed marked increases in expression during biofilm growth ( Table 1 ) . We asked if these glucan modification enzymes were functioning in conjunction with the previously described Zap1-regulated matrix production [31] . This zinc transcription factor is a negative regulator of biofilm matrix production , including matrix glucan production . Surprisingly , these glucan modification enzymes appear to function independently of Zap1 . First , transcription of BGL2 , XOG1 , or PHR1 was not significantly altered in the zap1−/− mutant biofilm . Second , there were no significant changes in ZAP1 transcription in the glucan modification mutant biofilms ( data not shown ) . These findings suggest that BGL2 , XOG1 , and PHR1 comprise a distinct biofilm matrix delivery pathway . The mutants with reduced matrix glucan ( bgl2−/− , xog1−/− , and phr1−/− ) were evaluated for biofilm architecture , matrix appearance , and total matrix abundance by scanning electron microscopy of in vitro biofilms . These glucan modifying enzyme mutants were capable of biofilm formation , but exhibited diminished extracellular biofilm material ( Figure 1B ) . The association between reduced glucan and total matrix biomass is similar to that described for mutants in the β-1 , 3 glucan synthesis pathway [17] , [30]–[33] . Since β-1 , 3 glucan has been described as a matrix component , we considered the possibility that the glucan in the matrix may also impact biofilm persistence or resistance to disaggregation . To test the functional role of matrix glucan , and the impact of glucan matrix delivery , we examined biofilm cell disaggregation in the reference strain and this subset of glucan modifying enzyme mutants following exposure to low concentrations of β-1 , 3 glucanase . Previous studies in this model have shown that higher concentrations of this enzyme will disperse intact mature biofilms [16] . In the present investigation , exposure to a low concentration of β-1 , 3 glucanase resulted in disaggregation of approximately 25% of the reference biofilm ( Figure 1C ) . However , the same glucanase incubation allowed dispersion of approximately 80–90% of the glucan modifying mutant biofilms . These observations argue that matrix β-1 , 3 glucan provides an adhesive function within the biofilm matrix . The disaggregation findings are also consistent with the matrix biochemical and imaging observations showing less matrix β-1 , 3 glucan and total matrix biomass . A previously demonstrated link between matrix glucan and drug resistance led us to test the impact of these glucan modifying enzymes on this important biofilm phenotype [14] , [16] . Each of the fifteen glucan modifying mutants in the candidate gene set was screened for susceptibility to the triazole , fluconazole , during in vitro biofilm growth ( Table 1 and Table S1 in Text S1 ) . The three glucan modifying mutants that delivered less matrix glucan exhibited enhanced susceptibility to fluconazole . Although the highest concentration of fluconazole resulted in no net change in cell burden in reference biofilms , this same drug exposure reduced the mutant biofilms by 35 to nearly 70% ( Table 1 and Figure 2A ) . A dose dependent anti-biofilm effect was observed over the entire dose range examined ( not shown ) . These findings were confirmed for the independent transformants ( Table 1 ) . Furthermore , the biofilm-associated antifungal resistance was restored in complemented strains ( Figure 2A ) . Deletion of the three glucan modifying genes did not cause a significant change in planktonic antifungal drug susceptibility ( Table 1 ) , so we infer that this is a biofilm-specific phenotype . As drugs from the echinocandin class target β-1 , 3 glucan synthesis , we further examined the impact of these select glucan modification mutants on biofilm susceptibility to a drug from this class . Each of strains ( parent and the three mutants ) demonstrated extensive susceptibility to low echinocandin concentrations ( <0 . 03 µg/ml ) . No difference in drug activity was observed over the range of concentrations examined . In order to determine the clinical relevance of these observations , we examined drug susceptibility using the in vivo rat central venous catheter biofilm model [34] . The impact of the fluconazole treatment was tested by measuring the viable burden of biofilm cells present following a twenty-four hour period of exposure to the drug instilled within the catheter lumen . Drug treatment produced minimal change in biofilm burden in the reference strain . In vivo study of the glucan modifying mutants recapitulated the observations from the in vitro model . The burden of catheter associated cells was reduced by 1 . 5 to more than 2 logs compared to the reference strain ( Figure 2B ) . Earlier studies suggest that the mechanistic basis underlying the glucan matrix associated resistance phenotype is due to sequestration of antifungal by the matrix material away from the drug's cellular target [14] . We tested the biofilm sequestration capacity of the reference strain and the subset of glucan modification mutants , bgl2−/− , xog1−/− , and phr1−/− ( Figure 2C ) . Each of the mutants sequestered less radioactive fluconazole than the reference strain , with the greatest defect observed for the phr1−/− biofilm ( nearly 4-fold ) . The mechanistic reason for differences among the glucan modifying strains is not clear and is clearly an interesting area for further inquiry . This finding further links the glucan modifying enzymes and matrix glucan deposition to biofilm drug resistance . Understanding the function of this subset of glucan modifying enzymes , Bgl2 , Xog1 , and Phr1 , in cell wall construction and maintenance remains incomplete . We hypothesized that the cell wall of mutant strains with reduced matrix β-1 , 3 glucan may exhibit similar glucan reductions in the cell wall . Previous studies in a phr1−/− mutant show altered cell wall glucan and chitin content during planktonic growth [29] . We were surprised to find similar cell wall β-1 , 3 and 1 , 6 glucan content among the biofilm cells of this subset of glucan modifying mutants and the reference strain ( Figure 3A ) . These results support a model in which the individual modification enzymes are dispensable for cell wall glucan production during biofilm growth , but are required for delivery of glucan from the cell to the extracellular matrix . The difference between the PHR1 cell wall results in the planktonic and current biofilm studies further underscore a novel , biofilm specific role for this gene product . Light microscopy and transmission electron microscopy ( TEM ) were used to inspect the mutant cell wall phenotypes . Light microscopy of the cells demonstrated a previously described abnormal hyphal morphology in the phr1−/− strain ( data not shown ) [35] . However , the other mutants appeared similar to the reference strain . By TEM , the yeast cell walls for each of the strains appeared quite similar in thickness and ultrastructural composition , consistent with the carbohydrate composition analyses ( Figure 3B ) . The relative thickness of the cell wall of at least 50 images from each strain was quantified using ImageJ software . The average cell wall thickness for each strain was not significantly different from the reference strain ( data not shown ) . A parallel study of cell wall function was performed to assess the potential impact of the glucan modifying genes on the cell wall integrity pathway that has been shown to contribute to the biofilm formation and drug resistance mechanism [36] , [37] . Susceptibilities to β -1 , 3 glucanase , hydrogen peroxide , SDS , and calcofluor white were similar among the bgl2−/− , xog1−/− , and the reference biofilms ( Figure 3C ) . The phr1−/− strain exhibited hypersensitivity to β -1 , 3 glucanase and calcofluor white , and a relative resistance to SDS . The change in susceptibility to calcofluor white in these biofilm experiments is similar to that described for planktonic conditions [38] . These phenotypic screens suggest potential perturbation of the CWI pathway associated with PHR1 disruption , but we did not detect a similar signal for the other glucan modifying mutants . The β-1 , 3 glucan synthase has been shown as necessary for β-1 , 3 glucan production and development of biofilm matrix [16] , [39] . We theorized that one or more of the glucan modification enzymes acts upon the β-1 , 3 glucan product of the synthase enzyme in a tightly controlled glucan delivery and matrix maturation of pathway . To explore this hypothesis we examined transcript abundance of the glucan synthase , FKS1 , in the glucan modifier mutants . We reasoned that reduced delivery of glucan to the matrix may signal the cell to produce additional β-1 , 3 glucan which would be marked by an increase in the FKS1 transcript . The FKS1 mRNA abundance results were consistent with the theory that matrix glucan levels influence the cell glucan production machinery . Transcript levels were elevated more than 1 . 5-fold in each of the modifier mutants ( Figure 4A ) . Additional testing of these relationships included a functional study of the impact of overexpression of the glucan modification genes , BGL2 , XOG1 , and PHR1 in the FKS1−/+ heterozygote . This strain produces less matrix glucan and exhibits a biofilm antifungal drug susceptible phenotype [32] . We theorized that if the glucan modifier enzymes act upon the glucan product of Fks1p for matrix delivery , then overexpression of the modifiers would not repair the drug susceptibility defect in the FKS1−/+ background . Indeed , the overexpression of the glucan modifiers did not restore the wildtype biofilm resistance phenotype ( Figure 4B ) . In a complementary experiment , we also examined the impact of overexpression of FKS1 in the glucan modifier null−/− background . These manipulations restored the antifungal resistance phenotype to each of the modifier deletion mutants ( Figure 4C ) . One simple explanation for these observations is a model in which the glucan modification enzymes provide complementary activity . Studies in the last several years have taught us that redundancy in the biofilm formation process is a common theme for other important functions , such as adherence [40] , [41] . A second interpretation of the findings is a paradigm in which the glucan synthesis and modification pathways are distinct with regard to the biofilm matrix resistance mechanism . The suggestion of complementary activity for matrix delivery and drug resistance was further investigated by overexpression analysis of the glucan modifier genes in companion deletion mutant backgrounds and double knockout strains . We successfully introduced a PHR1 overexpression allele into the bgl2−/− strain , and introduced a BGL2 overexpression allele into xog1−/− and phr1−/− strains . We were unable to successfully introduce a XOG1 overexpression allele in the bgl2−/− or phr1−/− strains . Similarly , we were unable to introduce the PHR1 overexpression allele in the xog1−/− background . Biofilm susceptibility testing demonstrated restoration of the drug resistance phenotype associated with overexpression of a companion glucan modifier in the glucan modification mutant background for all strains tested ( Figure 4D ) . These results are similar to those observed for in the glucan synthase mutant . We infer that the findings suggest a complementary relationship among the glucan modifiers . Additional examination of the association among the glucan modifiers included testing the impact of mutants in which two modifiers were disrupted . We were unable to construct the double knockout xog1−/− , phr1−/− , suggesting that loss of both of these genes may result in a non-viable mutant . For unclear reasons , the constructed double knockouts ( xog1−/− , bgl2−/− and bgl2−/− , phr1−/− ) demonstrated a growth defect in RPMI-MOPS under both biofilm and planktonic conditions , such that no biofilm could form in RPMI . They were , however , able to adhere to plastic and produce filaments in response to increased temperature when grown in YPD ( Figure 5A ) . These double knockout strains also exhibited normal planktonic growth in YPD ( Figure 5B ) . Therefore , we adapted the XTT biofilm drug susceptibility assay to include YPD media for comparison of double mutant and parent strains . In this assay , both of the double knockouts ( bgl2−/− , phr1−/− and xog1−/− , bgl2−/− ) demonstrate increased susceptibility to fluconazole when compared to their single modifier knockout parent strains ( Figure 5C ) . While these strains produced relatively normal biofilms in the 96 well format , similar study with YPD in the larger format utilized for matrix composition analysis was insufficient in these strains . Thus , we were unable to reliably compare matrix glucan content . Although the observed RPMI growth defects and assay modification are limitations , the experiments suggest that deletion of two modification genes results in a further decline in matrix delivery . These findings support the theory that the modifiers act in parallel and can partially compensate for each other .
The extracellular matrix is critical for mature biofilm formation [42] . This material not only contributes to the adhesive nature of biofilm cells , but has been shown to protect the cells from antimicrobial agents and the host immune system as well [12] , [30] , [33] , [43] , [44] . Understanding the matrix components' production and delivery processes is one path for the development of effective biofilm therapies . A key constituent of the C . albicans matrix is β-1 , 3 glucan [16] , [31] . Previous work identified an increase in cell wall glucan associated with biofilm growth [16] . Subsequent observations demonstrated the importance of the glucan synthase pathway for production of β-1 , 3 glucan in both the cell walls of biofilm cells and the extracellular matrix [32] . The predominant β-1 , 3 glucan synthase in C . albicans is encoded by FKS1 [45] . Both the MAP kinase pathway and the transcription factor ZAP1 have been identified as upstream components of the biofilm matrix production process [30] , [31] , [46] . However , the process of delivering glucan from the cell wall and the resulting mature biofilm matrix accumulation remained unknown . The present findings identify a novel role of several glucan modification enzymes for delivery of matrix glucan and other components to the cohesive extracellular matrix network . The delivery enzymes from the current screen have been shown or suggested to act upon the β-1 , 3 glucan substrate . The function of each includes glucan hydrolysis and in some instances transfer and formation of new branch linkages . Previous studies in two unrelated bacterial pathogens , Pseudomonas aeruginosa and Streptococcus mutans , have demonstrated the importance of similar transferase enzymes for delivery of glucan to their biofilm matrices [43] , [47] . Our glucan matrix and biofilm antifungal susceptibility screens point to a role for three genes , BGL2 , XOG1 , and PHR1 . BGL2 and PHR1 encode glucanosyltransferases and XOG1 encodes a β-1 , 3 exoglucanase [22] , [27] , [29] . Each of these genes has been shown to play a role in cell wall remodeling and specifically glucan chain elongation and cross-linking during planktonic cell growth for both C . albicans and S . cerevisiae [23]–[30] . Interestingly , each of the enzymes shown to impact matrix glucan delivery did not appear to impact the quantity of cell wall ultrastructure or β-1 , 3 glucan concentration . This suggests that these enzymes function specifically for matrix delivery , distinct from the cell wall assembly pathway during biofilm growth . One exception is PHR1 . Disruption of this gene appeared to alter the cell wall integrity pathway during biofilm growth , based upon enhanced susceptibility to cell wall stress by calcofluor white . This observation is similar to that described for planktonic conditions [29]–[30] . Previous investigations found elevated transcript levels of BGL2 , XOG1 , and PHR1 during in vivo biofilm growth compared to planktonic growth [18] . This biofilm associated upregulation is consistent with a role in a biofilm-specific function , such as matrix formation . The current studies identify a biofilm-specific pathway for these enzymes involving matrix delivery . One proposed mechanism is that the enzymes release and modify cell wall glucan for deposition in the extracellular space . An alternative explanation is that the enzymes act in the extracellular space , contributing to steric changes in glucan that are important for mature matrix organization and function . The enzymes Bgl2 , Xog1 , and Phr1 have been localized to the cell wall , supporting the hypothesis of cell wall activity . However , Bgl2 and Xog1 also contain secretion sequences providing feasibility for an extracellular function . Phr1 contains a GPI-linked tail , making it more likely to be tethered to the extracellular portion of biofilm cells . Candida biofilm proteomic analysis ( our data not shown ) identified Bgl2 Xog1 , and Phr1 incorporated in the biofilm extracellular matrix consistent with an extracellular role . Enzyme isolation and further structural analysis of matrix components in parent and mutant strains may be an attractive strategy to differentiate between these matrix delivery functions . The known glucan modification function of these enzymes intuitively supports a model whereby matrix delivery is downstream of the primary β-1 , 3 glucan synthase encoded by FKS1 ( Figure 6 ) . Transcriptional and functional analyses of our target gene overexpression strains support a pathway with partially redundant glucan modification enzymes that link to Fks1 . We propose that the overexpression of glucan modifications enzymes is unable to compensate for disruption of FKS1 due to the lack of available glucan substrate . Overexpression of FKS1 partially restores the glucan mutant phenotype by over production of glucan substrate which is processed through parallel pathways of redundant glucan modification enzymes . Data derived from studying the double modifier mutants and the overexpression of modifiers in companion knockout strains also supports a degree of redundancy among the glucan modifiers . Furthermore , upregulation of the FKS1 transcript in each of the enzyme modifier knockouts suggests feedback signaling for the cell to produce additional β-1 , 3 glucan during biofilm growth in the absence of glucan matrix delivery associated with each glucan modifier mutant . We considered the possibility that the glucan modification pathway was under control of Zap1 , a transcription factor known to function in matrix production . Surprisingly , review of previously reported global expression analysis of ZAP1 did not identify altered expression of XOG1 , PHR1 , or BGL2 . We confirmed the absence of differential mRNA abundance of these select glucan modification enzymes in the ZAP1 mutant ( data not shown ) . Thus , this work has identified a novel matrix glucan delivery pathway that is distinct from the previously described matrix-inhibitory pathway controlled by ZAP1 . Figure 6 shows a proposed model of the relationship of these matrix delivery enzymes to Fks1 and Zap1 . These studies show a novel biofilm matrix delivery pathway linked to the drug resistance phenotype . It is intriguing to consider the potential for drug target development designed specifically to identify enzyme inhibitor molecules . Because homologues are not present in the human genome , the likelihood of a safe pharmacologic anti-biofilm agent is promising . Additional work on the signaling and upstream genetic control of these enzymes promises to shed additional light on this important feature of biofilm formation .
All animal procedures were approved by the Institutional Animal Care and Use Committee at the University of Wisconsin according to the guidelines of the Animal Welfare Act , The Institute of Laboratory Animal Resources Guide for the Care and Use of Laboratory Animals , and Public Health Service Policy . Strains were stored in 15% ( vol/vol ) glycerol stock at −80°C and maintained on yeast extract-peptone-dextrose ( YPD ) medium with uridine ( 1% yeast extract , 2% peptone , 2% dextrose , and 80 µg/ml uridine ) prior to experiments . C . albicans transformants were selected on synthetic medium ( 2% dextrose , 6 . 7% yeast nitrogen base [YNB] with ammonium sulfate , and auxotrophic supplements ) , or on YPD plus clonNat ( 2% Bacto peptone , 2% dextrose , 1% yeast extract , and 400 µg/ml clonNat [Werner Bioagents] ) or on YPD plus 70 µg/ml hygromycin B ( PhytoTechnology Laboratories ) . Prior to biofilm experiments , C . albicans strains were grown at 30°C in YPD and biofilms were grown in RPMI 1640 buffered with morpholinepropanesulfonic acid ( RPMI-MOPS ) . The C . albicans strains used in these studies are listed in Table 1 and the genotypes in Table S2 in Text S1 . Homozygous deletion strains were constructed from one of two parent strains , BWP17 or SN152 . PCR product-directed gene deletion in the BWP17 background was performed as previously reported [48] , [49] . Fusion PCR disruption cassettes were utilized to construct null strains in the SN152 background as previously described [50] . Complementation of mutant strains was performed using selection for arginine prototrophy as previously published [30] , [51] . DNA cassettes of the entire gene as well as 1 kb up and downstream were amplified using PCR . The primers were designed to affix a BamHI site to the 5′ end of the DNA cassette and an AscI site to the 3′ end . Because XOG1 had a BamHI cutting site within the gene , it was complemented using two AscI sites instead . Digested PCR products were ligated into the E . coli plasmid pC23 , which carries ampicillin resistance for selection and encodes the Candida dubliniensis Arg4 . Plasmids were linearized using PmeI and transformed using the lithium acetate protocol . All genotypes were verified by colony PCR using corresponding detection primers . Primers are listed in Table S3 in Text S1 . Overexpression of genes , FKS1 , XOG1 , BGL2 , and PHR1 , was accomplished by replacing the endogenous promoter of one allele with the promoter of TDH3 , using the plasmid pCJN542 containing the NAT1 – TDH3 gene cassette as described previously [52] . Primers were designed with homology to the plasmid as well as to the promoter region of the targeted gene . This homology allowed for the entire cassette produced from the plasmid ( including the NAT1 gene and TDH3 promoter ) to be inserted into the promoter region of the gene of interest using homologous recombination , resulting in the gene now being driven by the highly active TDH3 promoter . Transformants were selectively grown on YPD+clonNAT . All genotypes were verified by colony PCR . Double deletion mutants were created in the SN152 background . The alleles for the first mutant were constructed by sequential replacement with the HygBR and NouR resistance markers , respectively [53] . The second gene was disrupted by replacement of auxotrophic genes as described above [50] . The mutant strains were confirmed by colony PCR . The strain xog1−/− : phr1−/− could not be created . RNA was collected from biofilm cells grown in 6-well plates , as described below . RNA was purified using the RNeasy Minikit ( Qiagen ) and quantified using a NanoDrop spectrophotometer . TaqMan primer and probe sets designed using Primer Express ( Applied Biosystems , Foster City , CA ) for ACT1 , FKS1 , BGL2 , XOG1 , and PHR1 are shown in Table S4 in Text S1 . The QuantiTect probe reverse transcription-PCR ( RT-PCR ) kit ( Qiagen ) was used in an iQ5 PCR detection system ( Bio-Rad ) with the following program: 50°C for 30 min , initial denaturation at 95°C for 15 min , and then 40 cycles of 94°C for 15 s and 60°C for 1 min . Reactions were performed in triplicate . The expression of each gene relative to that of ACT1 is presented . The quantitative data analysis was completed using the delta-delta CT method [54] . The comparative expression method generated data as transcript fold change normalized to a constitutive reference gene transcript ( ACT1 ) and relative to the reference strain . Biofilms were grown in 6-well or 96-well flat-bottom polystyrene plates as previously described [51] , [55] . The C . albicans inoculum ( 106cells/ml ) was prepared by growth in YPD with uridine overnight at 30°C , followed by dilution in RPMI-MOPS based on hemocytometer counts . For 6-well plates , 1 ml of culture was inoculated in each well . After a 60 min adherence period at 30°C , the non-adherent inoculum was removed and 1 ml of fresh medium ( RPMIMOPS ) was applied to each well . Plates were incubated at 37°C for 48 h on an orbital shaker set at 50 rpm . Medium was removed and fresh medium was added midway through the incubation period . A jugular vein rat central venous catheter infection model was used for in vivo biofilm studies [34] . Candida strains were grown to late logarithmic phase in YPD at 30°C with orbital shaking at 200 rpm . Following a 24 h conditioning period after catheter placement , infection was achieved by intraluminal instillation of 500 µl of C . albicans ( 106cells/ml ) . After an adherence period of 6 h , the catheter volume was withdrawn and the catheter was flushed with heparinized saline . For drug treatment experiments , fluconazole ( 250 µg/ml ) was instilled in the catheter after 24 h of biofilm growth . After a 24 h drug treatment period , the post treatment viable burden of Candida biofilm on the catheter surface was measured by viable plate counts on Sabouraud's dextrose agar ( SDA ) following removal of the biofilm by sonication and vortexing . A tetrazolium salt XTT [2 , 3-bis- ( 2-methoxy-4-nitro-5-sulfophenyl ) -2H-tetrazolium-5-carboxanilide inner salt] reduction assay was used to measure in vitro biofilm drug susceptibility [56] , [57] . Biofilms were formed in the wells of 96-well microtiter plates , as described above . After a 6 h biofilm formation period , the biofilms were washed with phosphate-buffered saline ( PBS ) twice to remove non-adherent cells . Fresh RPMI-MOPS and drug dilutions were added , followed by additional periods of incubation ( 48 h ) . The antifungals studied included fluconazole at 4 to 1 , 000 µg/ml . Drug treatments were reapplied after 24 h , and plates were incubated for an additional 24 h . Following treatment with 90 µl XTT ( 0 . 75 mg/ml ) and 10 µl phenazine methosulfate ( 320 µg/ml ) for 30 min , absorbance at 492 nm was measured using an automated plate reader . The percent reduction in biofilm growth was calculated using the reduction in absorbance compared to that of controls with no antifungal treatment . Assays were performed in triplicate , and significant differences were measured by analysis of variance ( ANOVA ) with pairwise comparisons using the Holm-Sidak method . The CLSI M27 A3 broth microdilution susceptibility method was used to examine the activities of fluconazole against planktonic C . albicans [58] . Endpoints were assessed after 24 h by visible turbidity . Agents with various mechanisms of action known to impact cell integrity were tested [37] , [59] . A 96-well XTT assay , as described above , was used for measurement of the biofilm response to stress-inducing agents . The concentration required for a 50% reduction in XTT absorbance ( 50% effective concentration [EC50] ) was recorded as the endpoint . Assays were performed in triplicate . The following concentration ranges were tested: calcofluor white , 0 . 2 to 200 µg/ml; β 1 , 3 Glucanase , 0 . 625 to 5 units/ml; H2O2 , 25–200 µM; and sodium dodecyl sulfate ( SDS ) , 0 . 001 to 2% . In vitro biofilms were grown on sterile coverslips ( Thermanox ) in sterile 12 well plates and coated with 10 µl of human NaEDTA plasma each , which were dried at 30°C . 40 µl of yeast in RPMI , counted and diluted as in the biofilm models described above , was added to each coverslip for 60 min at 30°C . The initial inoculum was then removed and the plates incubated in 1 ml RPMI+MOPS+5% NaEDTA human plasma for 20 h at 37°C and 50 rpm on an orbital shaker . Media was replaced with 1 ml of fixative ( 4% formaldehyde , 1% glutaraldehyde in PBS ) and coverslips were incubated at 4°C for 24 hours . The coverslips were then washed with PBS and treated with 1% osmium tetroxide for 30 min at ambient temperature . After a series of alcohol washes ( 30 to 100% ) , final desiccation was performed by critical-point drying . Coverslips were mounted , palladium – gold coated , and imaged in a scanning electron microscope ( SEM LEO 1530 ) at 3 kV . The images were assembled using Adobe Photoshop 7 . 0 . 1 . C . albicans biofilms were grown on 6-well polystyrene plates for 48 h as described above . Cells were prepared for transmission electron microscopy ( TEM ) as previously described [30] . Following fixation in 4% formaldehyde and 2% glutaraldehyde , cells were postfixed with 1% osmium tetroxide and 1% potassium ferricyanide , stained with 1% uranyl acetate , dehydrated in a graded series of ethanol concentrations , and embedded in Spurr's resin . Sections ( 70 nm ) were cut , placed on copper grids , poststained with 8% uranyl acetate in 50% methanol and Reynolds' lead citrate , and analyzed by TEM ( Philips CM 120 ) . The total cell and cell wall areas of 50 reference and mutant biofilm cells were measured using NIH Image J ( http://rsbweb . nih . gov/ij/ ) . The percentages of the cell wall area , defined as the cell wall area divided by the total cellular area , were calculated . Student's t test was used to determine statistical significance of differences between strains . Biofilms growing in 6-well plates for 48 h were washed with PBS and collected for cell wall carbohydrate analysis as previously described [16] , [60] . Briefly , cells ( 5 mg dry cell weight ) were washed with PBS and broken apart with glass beads . Isolated cell walls were alkali extracted for 60 min with 500 µl of 0 . 7 M NaOH at 75°C three times . The combined alkali-soluble supernatants were neutralized with 250 µl glacial acetic acid . Following neutralization , the alkali-insoluble pellet was digested with 100 U Zymolyase 20T ( MP Biomedicals ) at 37°C for 16 h . One half of the Zymolyase-soluble fraction was dialyzed ( Slide-A-Lyzer dialysis cassette , 7 , 000-molecular-weight-cutoff [MWCO]; Pierce ) to yield a β-1 , 6-glucan fraction . The β-1 , 3-glucan fraction was calculated as the difference between the total Zymolyase-soluble glucan and β-1 , 6-glucan fractions . The carbohydrate contents of each fraction were measured as hexoses by the phenol-sulfuric acid method and normalized for dry cell wall weight . ANOVA with pairwise comparisons ( Holm-Sidak method ) was used to determine statistical significance . The matrix β-1 , 3 glucan content was measured using a Limulus lysate based assay , as previously described [16] . Matrix was collected from C . albicans biofilms growing in the wells of 6-well polystyrene plates for 48 h . Biofilms were dislodged using a sterile spatula , sonicated for 10 min , and centrifuged 3 times at 4 , 500×g for 20 min to separate cells from soluble matrix material . Samples were stored at −20°C , and glucan concentrations were determined using the Glucatell ( 1 , 3 ) -β-D-glucan detection reagent kit ( Associates of Cape Cod , MA ) per the manufacturer's directions . Glucan concentrations were normalized for comparison across strains based upon viable biofilm burden using the XTT assay described above . Matrix β-1 , 3 glucan was also measured using an ELISA assay . Biofilm was grown for 48 hours in 5×850 cm2 roller bottles ( Corning , Thermo-Fisher ) at 37°C . Biofilms were harvested into H2O using a sterile spatula then sonicated at 42 kHz for 20 min to dislodge the matrix . Next , biofilms were centrifuged 3×4 , 000 rpm for 20 min to separate the cells from the soluble matrix . The supernatant was lyophilized , dialyzed in a 3 kDa dialysis membrane ( Spectra , Thermo-Fisher ) , and re-lyophilized to a powder . One mg of powdered matrix , dissolved in 1 ml of PBS was used as the sample in the ELISA assay and laminarin was used as a standard . A range of 1–1000 ng/ml of laminarin was used for the ELISA standard curve . 200 µl of 1 mg/ml matrix for each strain was assayed in triplicate . Plates were incubated overnight at 4°C , followed by blocking with 1% BSA for 45 min at ambient temperature . A 1∶2000 dilution of anti- β-1 , 3-glucan ( BioSupplies Inc , Australia ) was used as the primary antibody and a 1∶10 , 000 dilution of goat anti-mouse IgG-Biotin labeled [Sigma , Saint Louis] was used as a second antibody . Avidin-Peroxidase ( Sigma ) was used for detection . A radiolabeled fluconazole accumulation protocol was adapted for biofilm use as previously described [51] , [61] . Biofilms were grown in 6-well plates , as detailed above . The biofilms were washed with sterile water twice . For stock solution preparation , radioactive [H3] fluconazole ( Moravek Biochemicals; 50 µM , 0 . 001 mCi/ml in ethanol ) was diluted 100-fold in water . The stock solution was then diluted 6-fold in RPMI-MOPS , and each biofilm well received a total of 600 µl of this medium to yield a total of 8 . 48×105 cpm of [H3] fluconazole . After incubation for 30 min at 37°C and orbital shaking at 50 rpm , unlabeled ( cold ) 20 µM fluconazole in RPMI-MOPS was added and biofilms were incubated for an additional 15 min . Biofilms were then washed twice with sterile water , dislodged with a spatula , and collected as intact biofilms for scintillation counting . The biofilms were then disrupted by vortexing and sonication to separate cells and matrix . Following centrifugation , cells were separated from the soluble matrix material . Cells were subsequently disrupted by bead beating , and the intracellular and cell wall portions were collected by centrifugation . The fractions were then suspended in ScintiSafe 30% LSC cocktail ( Fisher Scientific ) and counted in a Tri-Carb 2100TR liquid scintillation analyzer ( Packard ) . ANOVA was used to determine statistical significance of differences among strains . Biofilms were grown using the 96 well microtiter model described above for 24 hours . Then , 90 µl of fresh media and 90 µl of serial 2 fold dilutions of the β-1 , 3 glucanase ( Zymolyase - 20T , MP Biomedicals ) in 0 . 9% NaCl was added to each well , with concentrations ranging from 5 U/ml to 0 . 625 U/ml . Plates were incubated at 37°C for 24 hours , at which point the media was removed and the biofilms were washed gently in 100 µl of PBS to remove any non-adherent cells . The plates were read using the XTT assay as described above . For comparison , a duplicate set of plates was spun at 3 , 000 RPM for 5 minutes before the media was removed on the final day . These biofilms were read via the XTT assay immediately , without washing , thus quantifying all living cells in each well to show whether β-1 , 3 glucanase at the concentrations used causes cell disaggregation or lysis . | Biofilms are a community of microbes that grow attached to each other and adherent to a surface . One distinguishing feature of this form of growth is the presence of a surrounding extracellular matrix which is proposed to provide a structural scaffold and protection for biofilm cells . This later function contributes to the extreme resistance to anti-infective therapies , another innate characteristic of biofilms . One carbohydrate component of the matrix of Candida albicans , β-1 , 3 glucan , has been linked to overall accumulation of matrix material and the antifungal drug resistance phenotype . Although the glucan synthase pathway has been implicated in glucan production , the delivery and incorporation of these carbohydrates into the matrix remains a mystery . The current investigation describes three gene products that serve a matrix delivery role . The functions of these gene products include glucanase and glucanosyltransferase activities . Mutants unable to produce these enzymes demonstrate reduced matrix glucan , decreased total matrix biomass accumulation , and enhanced susceptibility to antifungal drug therapy . The observations here offer insight into a novel pathway that contributes to biofilm maintenance . Enzymes in this biofilm-specific process may provide useful anti-biofilm drug targets . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"medicine",
"infectious",
"diseases",
"drugs",
"and",
"devices",
"biology",
"microbiology"
] | 2012 | A Candida Biofilm-Induced Pathway for Matrix Glucan Delivery: Implications for Drug Resistance |
Collection of surveillance data is essential for monitoring and evaluation of public health programs . Integrated collection of household-based health data , now routinely carried out in many countries through demographic health surveys and multiple indicator surveys , provides critical measures of progress in health delivery . In contrast , biomarker surveys typically focus on single or related measures of malaria infection , HIV status , vaccination coverage , or immunity status for vaccine-preventable diseases ( VPD ) . Here we describe an integrated biomarker survey based on use of a multiplex bead assay ( MBA ) to simultaneously measure antibody responses to multiple parasitic diseases of public health importance as part of a VPD serological survey in Cambodia . A nationally-representative cluster-based survey was used to collect serum samples from women of child-bearing age . Samples were tested by MBA for immunoglobulin G antibodies recognizing recombinant antigens from Plasmodium falciparum and P . vivax , Wuchereria bancrofti , Toxoplasma gondii , Taenia solium , and Strongyloides stercoralis . Serologic IgG antibody results were useful both for generating national prevalence estimates for the parasitic diseases of interest and for confirming the highly focal distributions of some of these infections . Integrated surveys offer an opportunity to systematically assess the status of multiple public health programs and measure progress toward Millennium Development Goals .
In many tropical and sub-tropical countries , the disease burden represented by neglected tropical diseases ( NTDs ) is substantial , yet information on the prevalence and distribution of these diseases is limited because of the significant costs associated with disease-specific surveys . Even with the recent scale-up of preventive chemotherapy programs targeting NTDs [1] , routine assessments to monitor the impact of these programs , when they occur , are often restricted to sentinel sites and may not be representative of all program areas . For some diseases such as strongyloidiasis , prevalence data for many regions of the world are lacking , and no public health strategy has been developed for control of the disease [2 , 3] . Demographic and Health Surveys ( DHS ) and other population-based multiple indicator surveys are conducted to assess the performance of health and development programs . The United States Agency for International Development ( USAID ) has assisted in over 230 DHS surveys in more than eighty countries since 1984 at a cost of approximately $380 million dollars , and additional monies have been contributed by other donors as well as host countries [4] . Collection of biomarker data is often included in these types of population-based surveys to assess morbidity , HIV status , or malaria infection prevalence , but these surveys have not been extended to include NTDs . Multiplexing technologies provide new opportunities to collect data on a large number of diseases using a single serum sample or dried blood spot [5] . Such an approach would provide Ministries of Health with valuable information on the distribution and prevalence of NTDs , opportunities to monitor the impact of NTD interventions , evidence to inform programmatic decisions , and post-elimination surveillance . The Cambodian Ministry of Health conducted a serological survey in 2012 to assess population immunity for poliomyelitis , measles , rubella and tetanus among women aged 15–39 years [6] . This comprehensive national serological survey provided an excellent opportunity to gather information on the distribution and prevalence of other diseases throughout Cambodia by measuring antibody responses to a panel of antigens representing several parasitic infections . We used multiplexing technology to assay sera collected in this national serological survey for immunoglobulin G ( IgG ) antibodies against tetanus , measles , Plasmodium falciparum and P . vivax , Wuchereria bancrofti , Toxoplasma gondii , Taenia solium , Strongyloides stercoralis , and several arthropod-borne viruses . For S . stercoralis the national prevalence exceeded 40% and was indicative of a country-wide public health problem of surprising magnitude . Multiplexed approaches provide an opportunity to gather information of public health importance on a large scale using well-standardized survey platforms and well-characterized infection markers .
Samples were obtained during a serological survey in November and December 2012 as previously described [6] . Briefly , blood samples were collected from women of child-bearing age ( 15–39 years ) throughout Cambodia . Multi-stage cluster sampling was performed with oversampling of areas identified as higher risk for tetanus based on the 2009 Cambodian neonatal tetanus risk assessment . One hundred enumeration areas ( EAs ) were selected by simple random sampling of the 611 EAs defined for Cambodia’s 2010 DHS survey . The number of rural and urban EAs from each region were selected to match the relative proportion of urban and rural populations in the region . From each of the EAs , twenty-two households were selected and all eligible women in those households were invited to participate . The design and sample size were selected to provide estimates of population rubella and tetanus immunity nationwide and by age-group [6 , 7] . Five milliliters of whole blood were collected from each participant , and sera were separated shortly thereafter and stored at -80°C . As previously described , samples were initially tested by enzyme-linked immunosorbent assay ( ELISA ) or standard microneutralization assay for antibodies to measles , rubella , and polio [6] . Residual samples were then tested by multiplex bead assay ( MBA ) at CDC in Atlanta , GA , and by double antigen ELISA for tetanus antibody levels at the Statens Serum Institut , Copenhagen , Denmark . The results of measles , rubella and polio antibody testing have been published [3] and the tetanus assay results will be reported elsewhere [7] . As previously described [7] , a total of 2150 samples had reported tetanus values and were included in the multiplex assay testing . Written informed consent was obtained and documented prior to participation in the survey; specific consent for serologic testing of diseases of public health importance was included as part of this process . Consent was also be obtained separately from the parent or guardian of women under the age of 18 . The protocol was reviewed and approved by the national ethics committee in Cambodia . Staff of the Ministry of Health of Cambodia selected antigens to be included in the multiplex . The following parasite-specific recombinant antigens were used in the MBA ( Table 1 ) : NIE for Strongyloides stercoralis [8]; SAG2A for Toxoplasma gondii [9 , 10]; T24H for cysticercosis [11]; PfMSP-119 ( 3D7 strain ) and PfMSP-142 ( 3D7 strain and FVO strain ) for P . falciparum malaria [12 , 13]; and PvMSP-119 ( Belem strain ) for P . vivax malaria [14 , 15] . For lymphatic filariasis , Brugia malayi Bm14 ( SXP-1 ) [16] , B . malayi Bm33 ( Bm-AP-1 ) [17] , and W . bancrofti Wb123 [18] antigens were used . Wb123 is reported to be largely species specific [18 , 19] , while the Bm14 and Bm33 antigens cross react with sera from W . bancrofti infected patients as well as with sera from some patients infected with other filarial worm species [17 , 20] . Recombinant Bm14 [21] , SAG2A [22] , and NIE [23] proteins tagged with Schistosoma japonicum glutathione-S-transferase ( GST ) and control GST with no fusion partner [24] were expressed and purified as described elsewhere . Bm33 [25] and T24H [26] were expressed with GST fused to the amino terminus and with six histidines ( His6 ) on the carboxy terminus and purified as previously described . Following purification , the His6 tag was removed from T24H by Factor Xa cleavage [26] . Recombinant PfMSP-119-GST ( 3D7 parasite strain ) fusion protein and PfMSP-142 proteins ( 3D7 and FVO parasite strains ) lacking fusion tags were provided by C . Kauth and H . Bujard ( Heidelberg University , Heidelberg , Germany ) [27] . Wb123-GST fusion protein was provided by Dr . T . Nutman ( NIH , Bethesda , MD ) . The P . vivax PvMSP119-GST was cloned , expressed , and purified for the MBA . The coding sequence ( including the carboxy-terminal , hydrophobic anchor sequence ) was amplified from Belem strain DNA ( provided by J . Barnwell , CDC , Atlanta , GA ) using the following forward and reverse deoxyoligonucleotide PCR primers: 5’-CGC GGA TCC ACT ATG AGC TCC GAG CAC ACA TG-3’ and 5’-GCG GAA TTC TTA AAG CTC CAT GCA CAG GAG-3’ , respectively . BamHI and EcoRI restriction endonuclease sites used for directional cloning into pGEX 4T-2 plasmid ( GE Healthcare , USA ) are underlined in the primer sequences . Polymerase chain reaction amplification conditions and protocols for cloning into Escherichia coli BL21 cells ( Stratagene , USA ) have previously been described [28] . The sequence of the resulting PvMSP119 clone was confirmed to match that found in GenBank ( accession number AF435594 . 1 ) [29] . Recombinant PvMSP119-GST fusion protein was expressed and purified on a glutathione Sepharose 4B affinity column as directed by the manufacturer ( GE Healthcare ) . Glutathione-eluted proteins were dialyzed overnight against 300 volumes of 25 mM Tris buffer at pH 7 . 5 using Spectra-Por3 dialysis membrane ( 3 , 500-Da cutoff , Spectrum Laboratories , Rancho Dominguez , CA ) . Proteins were bound to a Mono Q HR5/5 strong anion exchange column ( GE Healthcare ) and eluted with a 20 min linear gradient from 0 to 0 . 25 M NaCl in 25 mM Tris buffer at pH 7 . 5 . Protein fractions collected between 0 . 15 and 0 . 21 M NaCl were mostly free of contaminants by SDS polyacrylamide gel analysis and were combined . The final protein product was dialyzed against 300 volumes of PBS and then concentrated to approximately 1 mg/ml using a Centricon-10 centrifugal filter device ( Millipore Corporation , Bedford , MA ) . The yield from 2 L of E . coli cells was approximately 1 . 5 mg of purified PvMSP119-GST protein . Bm14-GST and Wb123-GST antigens ( 120 μg for 12 . 5 x 106 beads in 0 . 5 ml ) were covalently coupled to SeroMap microsphere beads ( Luminex Corp . , Austin , TX ) using conditions previously described in buffer containing 10 mM Na2HPO4 and 0 . 85% NaCl at pH 7 . 2 ( PBS ) [25] . For the other antigens , coupling buffers for conjugation were empirically chosen to minimize protein usage and maximize the signal/ noise ratio ( shown in Table 1 ) . A small scale coupling reaction ( 50 μl containing 6 . 25 x 105 beads ) conducted at a protein concentration of 120 μg/ ml in PBS at pH 7 . 2 was compared to small scale coupling reactions performed in phosphate buffer at pH 7 . 2 or in buffers containing 2- ( N-morpholino ) -ethanesulfonic acid ( MES ) at pH 5 . 0 or 6 . 0 . Protein concentrations were varied from 120 μg/ ml to as low as 10 μg/ ml . Each small scale coupling was conducted using a different bead classification number so that the beads could be combined in a single assay well for analysis . The efficiencies of the couplings were compared by MBA ( conditions described below ) using a serial dilution of a strong positive human serum , a panel of positive and negative human sera , or a 10-fold serial dilution of a goat anti-GST polyclonal antibody ( GE Healthcare ) with a biotinylated rabbit anti-goat IgG secondary antibody ( 1:500 dilution; Invitrogen , Carlsbad , CA ) . Antigens coupled in 0 . 5 ml of 25 mM MES at pH 5 . 0 with 0 . 85% NaCl used the following amounts of protein for 12 . 5 x 106 beads: SAG2A-GST , 20 μg; T24H-GST , 120 μg; PfMSP-119-GST , 30 μg; PfMSP-142 proteins , 15 μg; PvMSP119-GST , 20 μg; GST control protein , 15 μg . The two antigens purified in the presence of 2 M urea required 2 M urea in the coupling buffer to minimize the MBA response to negative human sera . Bm33-GST-His6 ( 20 μg for 12 . 5 x 106 beads in 0 . 5 ml ) was coupled in buffer containing 25 mM MES , 2 M urea , and 200 mM NaCl at pH 6 . 0 . NIE-GST ( 20 μg for 12 . 5 x 106 beads in 0 . 5 ml ) was coupled in buffer containing 50 mM Na2HPO4 , 2 M urea , and 200 mM NaCl at pH 7 . 2 . Test sera were diluted 1:400 in PBS buffer ( pH 7 . 2 ) containing 0 . 3% Tween-20 , 0 . 02% sodium azide , 0 . 5% BSA , 0 . 5% casein , 0 . 5% polyvinyl alcohol ( PVA ) , 0 . 8% polyvinylpyrrolidone ( PVP ) , and 3 μg/ml E . coli extract , and duplicate samples were assayed for total IgG antibodies as previously described [21 , 25 , 30] . Casein was found to provide additional background noise reductions for the NIE and Bm33 assays compared to PVA and PVP alone . Assay performance was monitored by the inclusion on each plate of two positive control serum dilutions , two negative control serum dilutions , and a buffer-only blank . The average of the median fluorescent intensity values from the duplicate wells minus the background fluorescence from the buffer-only blank was reported as the “median fluorescence intensity minus background” ( MFI-bg ) . Samples having a coefficient of variation of >15% for ≥2 positive responses between the duplicate wells were repeated . Because several of the parasitic diseases represented in our MBA panel are not endemic in the United States ( U . S . ) , we were able to use serum samples from 86 healthy , adult US citizens with no history of foreign travel to define positive IgG response cutoffs [25] . Cutoffs for Bm14 ( 65 MFI-bg ) , Wb123 ( 115 MFI-bg ) , Bm33 ( 966 MFI-bg ) , NIE ( 792 MFI-bg ) , and T24H ( 486 MFI-bg ) were based on the mean plus three standard deviations of the respective antibody response values ( Table 1 ) . Cutoffs for PfMSP119 ( 343 MFI-bg ) and PvMSP119 ( 196 MFI-bg ) were calculated using the mean plus three standard deviations of log transformed antibody response values ( Table 1 ) [31] . Panels of parasitologically confirmed , anonymous sera were available for MBA sensitivity determinations for malaria antigens ( slide microscopy positive patients , P . falciparum n = 33 and P . vivax n = 35 ) , S . stercoralis NIE antigen ( stool positive patients , n = 44 ) , and cysticercosis T24H antigen ( patients with multiple cysts confirmed by CT or MRI scan , n = 52 ) . The significant prevalence of toxoplasmosis in the U . S . population [32] required the use of an alternate means of cutoff determination for the SAG2A antigen . A panel of positive and negative sera ( n = 45 ) identified using the “gold standard” Sabin-Feldman dye binding assay was tested by MBA , and the average of the highest negative value ( 22 MFI-bg ) and the lowest positive value ( 295 MFI-bg ) was chosen as the positive cutoff ( 159 MFI-bg ) ( Table 1 ) [22 , 33] . An alpha of 0 . 05 was set for tests of statistical significance . Statistical analyses were conducted using SAS v9 . 3 ( Cary , NC , USA ) and STATA v 13 . 1 ( College Station , TX , USA ) . Briefly , sampling weights were calculated to take each stage of selection into account , including the probability of selecting the original EAs in the 2010 DHS . A non-response adjustment by strata was included using the weighting class approach . Final weights were scaled to conform to the regional distribution of the population in the 2008 census [34] . Estimates of seroprevalence and coverage with 95% ( logit ) confidence intervals ( CI ) were calculated accounting for survey design ( STATA v13 . 1 ) . Second-order Rao-Scott Chi-square tests were used to assess differences in seroprevalence across age groups , regions , and rural/urban residence .
The cutoff values assigned to the various parasite MBAs in Table 1 performed well when the assays were used to test serum panels from parasite infection-confirmed patients . All three P . falciparum MBAs detected IgG antibodies in 75 . 8% of the slide positive serum panel , and each positive serum reacted with all three antigens . Unfortunately , demographic information and details on the timing of sample collection relative to malaria infection were not available for this anonymous sample set . Specificities calculated from the U . S . citizen negative control panel ranged from 100% ( PfMSP119 ) to 96 . 5% ( PfMSP142 ) . The PvMSP119 , S . stercoralis NIE , and cysticercosis T24H MBAs had sensitivities of 85 . 7% , 84 . 1% , and 98 . 1% , respectively . Specificities calculated from the U . S . citizen negative control panel were 98 . 8% for each assay . As previously reported , the T . gondii SAG2A MBA was 100% sensitive and specific compared to a “gold standard” assay defined panel [22] . Sensitivities were not determined for the LF antigen MBAs; they were >97 . 7% specific with our U . S . negative samples . Although the Cambodia population survey was not powered to detect differences in antibody prevalence across EAs , a plot of median values of the data sorted first by EA and then by region revealed distinct high and low median prevalence values for some of the parasite-specific antibody responses . A single EA in the Steung Treng province of the North geographic region of the country was found to have coincident peaks of high antibody responses to all three LF antigens ( Fig 1A ) . Multiple North region EAs located in the provinces of Kratie , Preah Vihear , Ratanakiti , and Steung Treng had coincident peaks of antibody reactivity to the P . falciparum and the P . vivax MSP119 antigens ( Fig 1B ) . A weak median response peak in the West region province of Pursat was also detected . Median antibody response plots for the FVO and 3D7 PfMSP142 antigens largely mirrored those observed with the PfMSP119 antigen ( S1 Fig ) . Because the MSP142 antigens included the MSP119 sequence , these responses were not further analyzed . Multiple peaks of antibody to S . stercoralis NIE were observed throughout the country , and only the largely urban Phnom Penh region had EAs with relatively low median responses ( Fig 2A ) . For toxoplasmosis and cysticercosis there was no discernible geographic clustering of the antibody responses detected ( Fig 2B ) . Weighted national estimates for toxoplasmosis and cysticercosis were 5 . 8% ( CI , 4 . 7–7 . 0 ) and 2 . 6% ( CI , 1 . 8–3 . 7 ) , respectively , with no statistically significant urban/ rural , regional , or age-related differences noted ( S1 Table ) . Weighted national estimates of seroprevalence for P . falciparum ( 4 . 6%; CI , 3 . 1–6 . 8 ) and P . vivax ( 4 . 6%; CI , 3 . 3–6 . 4 ) malaria are shown in Table 2 . Antibody prevalence was significantly higher in rural areas than urban areas for P . falciparum ( P = 0 . 005 ) and P . vivax ( P = 0 . 014 ) . Regional differences in seroprevalence were statistically significant for P . falciparum and P . vivax ( Table 2 ) , with the North region having higher seroprevalence than the other regions combined for P . falciparum ( 13 . 7% vs . 1 . 9%; P < 0 . 001 ) and P . vivax ( 9 . 2% vs . 3 . 2%; P = 0 . 003 ) . No age related differences were noted for either malaria species . For the LF estimate ( Table 3 ) we required that antibodies to two or more of the LF antigens be present for a sample to be considered positive . The national LF estimate was low at only 2 . 4% ( CI , 1 . 6–3 . 6 ) , but statistically significant rural/ urban ( P = 0 . 039 ) and regional ( P < 0 . 001 ) differences were observed with the latter driven by a higher prevalence of 5 . 6% ( CI , 3 . 0–10 . 2 ) in the North region . In contrast to the low seroprevalence estimates for the parasitic diseases mentioned above , just under half of women of child-bearing age in our countrywide sample of Cambodia were positive for antibodies to S . stercoralis ( 45 . 9% , CI , 41 . 7–50 . 1 ) ( Table 3 ) . Differences between regions ( P < 0 . 001 ) and between urban and rural populations were highly significant ( P = 0 . 003 ) , but no age differences were detected ( P = 0 . 195 ) .
In a previous report , we used the multiplex bead assay to determine anti-tetanus toxoid antibody levels in Cambodian women of child-bearing age and demonstrated that the estimates of population immunity derived from the multiplex testing were very similar to those derived from the “gold standard” assay methodologies [7] . Here we demonstrate that multiplexed antibody assays , when integrated into the robust , population-based Cambodian serologic survey framework , can be used to provide nationally-representative estimates of the presence and distribution of a number of parasitic diseases of public health importance . Although others have used multiplex assays to measure multiple anti-parasite antibody responses [35] , this report is , to our knowledge , the first to generate national parasitic disease estimates from multiplexed serologic antibody assays . Cambodia recently completed five years of mass drug administration ( MDA ) to eliminate lymphatic filariasis in a small number of northern and northeastern provinces where the presence of infection had been documented by antigen surveys ( http://www . who . int/neglected_diseases/preventive_chemotherapy/lf/en/ ) [36] . Our results demonstrate the presence of significant residual antibody reactivity in the geographic North area where the MDA occurred and , perhaps of greater importance , its relative absence in areas where MDA was not carried out . These results are an important confirmation of the baseline mapping data that was used as the basis for determining where to implement MDA . The presence of residual antibody following MDA , as high as 60% in one EA , is not surprising as antifilarial antibody responses in adults are known to be long-lived [37 , 38] . Although sampling of children may be of greater value in the post-MDA setting as a measure of incident seroconversions , these results suggest the potential use of LF antibody testing as a tool for LF surveillance . Additional information on the longevity of responses in adults is needed to guide recommendations on the use of antibody surveys for post-MDA surveillance . The two other vector borne parasitic infections in our panel , P . falciparum and P . vivax malaria , were also focally distributed with seroprevalence for PfMSP119 antibody approaching 100% in some EAs ( S1 Data ) . Both of our national malaria seroprevalence estimates ( 4 . 6% for P . falciparum and 4 . 6% for P . vivax ) were considerably higher than the 0 . 9% blood film parasite prevalence estimate for all species of malaria generated by the Cambodia Ministry of Health in 2010 [39] . From samples collected in Cambodia in 2005 , Cook et al . [40] reported a P . falciparum peak seasonal seroprevalence of 49 . 2% compared to a parasite prevalence by slide microscopy of only 3 . 4% ( November , western provinces ) and a P . vivax seroprevalence of 20 . 2% compared to a 10 . 7% parasite prevalence ( August , eastern province ) . The discrepancies between the parasite prevalence by blood film microscopy and the parasite-specific IgG antibody prevalence may reflect low malaria parasite loads that remain below the limit of microscopic detection [41 , 42] , or , as in the case of LF described above , may result from a long IgG titer half-life following successful treatment [43] . Although the public health value of malaria serosurveys in adults may be limited to confirming the known distributions of those infections , serosurveys in young children , as with LF , may provide useful surveillance data for mapping transmission foci in the context of malaria elimination efforts and may offer an opportunity to monitor the impact of interventions by documenting reductions in seroprevalence over time [44 , 45] . From our MBA results , prevalence of IgG antibody to toxoplasmosis ( 3 . 5–7 . 3% ) and to cysticercosis ( 1 . 3–3 . 3% ) was relatively low across all geographic regions . Few surveys have been conducted for either of these infections in Cambodia [46 , 47] , but our values are consistent with the limited information available . Seroprevalence of IgG antibodies to T . gondii among women <40 years of age in Phnom Penh was 8 . 4% in one small study [48] . A low seroprevalence suggests that the majority of women of child-bearing age in Cambodia are at risk of primary T . gondii infection and could , if infected during pregnancy , transmit toxoplasmosis to their babies in utero with serious health consequences [49 , 50] . Recent stool-based detection surveys by Khieu et al . have found low levels of Taenia solium tapeworm infection in Cambodia: 0 . 4% in Preah Vihear province , 0 . 4% in children in Kandal province , and only 0 . 1% in Takeo province [51–53] . National estimates of infection prevalence among school children in neighboring Lao PDR were similarly low: 0–1 . 8% at the provincial level by stool assay [54] . The relatively low prevalence of intestinal tapeworm infection and the low prevalence of antibodies to the cysticercosis antigen in our study of adult women suggest that the risk of eliciting neurocysticercosis through mass drug administration with either praziquantel ( for schistosomiasis ) or albendazole ( for soil transmitted helminthiasis ) is likely to be low in this setting- a useful observation for the Ministry of Health in planning NTD interventions . A somewhat surprising result from our study was the high seroprevalence of S . stercoralis infection throughout Cambodia . S . stercoralis is thought to establish life-long infection because of its propensity for autoinfection [55] , and , in immunocompromised patients , a hyperinfection syndrome with a high case mortality may result [56] . The sensitivity of the S . stercoralis assay determined using samples from stool-confirmed cases suggested that the assay that was only 84% sensitive . Thus , it is possible that our results are , in fact , an underestimate of true infection prevalence . Previous surveys have documented a high prevalence ( 21–44 . 7% ) of strongyloidiasis using stool assays [51–53]; the present results establish that this problem is national in scope . Strongyloidiasis does respond to ivermectin therapy and MDA with ivermectin is a cornerstone of efforts to eliminate onchocerciasis and lymphatic filariasis in sub-Saharan Africa; however , there is currently no WHO guidance on either the dosage or treatment schedule that would be required to carry out MDA with ivermectin to control Strongyloides in other settings . In addition , donation programs for this drug are currently restricted to the two filarial infections . A key factor in the successful completion of this integrated survey was the forward-looking decision of survey planners to include specific language in the consent form that permitted testing for multiple infections . Such permissive language is not currently a standard feature of most surveys , and obtaining ethical approvals for retrospective testing of stored specimens can be problematic . When serum or blood spot collection is included in population-based surveys , survey planners should include permissive language in consent forms to allow a broader approach to integrated serosurveys . Although multiplexing technology has tremendous potential for integrated serosurveys , some limitations in our study must be acknowledged . First , defining robust cutoffs to determine seropositivity can be challenging for some antigens , especially when banks of true negative sera and of positive sera from parasitologically confirmed cases are not readily available . For most antigens in our MBA panel , we used a non-endemic negative control sample set in order to establish a cutoff . This approach may not have been ideal , and additional efforts will be needed to standardize procedures and cutoff values across multiple labs . Second , while we included in our MBA panel only highly purified recombinant antigens that had been successfully used in other serologic assay formats , sensitivity and specificity are a potential concern , especially when only one parasite antigen is used in the multiplex . Based on the earlier work of Bousema et al . [57] , the antibody responses to the two Plasmodium spp . MSP119 proteins are not expected to be cross-reactive , but we are currently examining this in more detail . We have , however , observed that the distribution of responses to helminth antigens , in particular , may be influenced by the background exposures to other helminth parasites and cross-reactivities may result in false-positives in the Bm14 response [20] . Such specificity concerns can be mitigated by including multiple parasite antigens in the MBA , as we did here with three unrelated LF antigens . Third , the current survey only included women of child-bearing age and was specifically designed to provide seroprevalence estimates for tetanus , and rubella at the regional level [6 , 7] . No epidemiologic information relative to parasitic diseases ( i . e . , bed net use ) was collected . While the survey was well-suited for the concurrent analysis of congenital toxoplasmosis risk , the possibility of gender- or age-specific differences in either the prevalence or distribution of some of the other infections of interest must be acknowledged . For example , several studies have shown that men have a higher prevalence of strongyloidiasis than women [51 , 52] , and gender differences in malaria prevalence are often noted in Cambodia because men are more frequently exposed to vector mosquitoes while working in sylvan environments [58 , 59] . Similarly , the study design did not take into account potential seasonal differences ( important for malaria ) as samples were collected only in November and December of 2012 at the beginning of the dry season [40] . Fourth , because of their small populations and remoteness , the provinces with the highest expected levels of malaria and LF were represented in the national survey by few EAs . This reflects the fact that the study was powered to compare regional prevalence estimates rather than estimates at the province , district , or EA level . Nevertheless , hot spots of parasite-specific antibody responses were observed in the nationwide survey , and , once identified , these areas could certainly be targeted for more focal screening in future surveys . Despite these limitations , the use of the antibody multiplex assay in the context of a nationally representative survey provides a proof of principle of the potential utility of integrated programmatic monitoring and evaluation for many diseases . The multiplex assay is a flexible platform that can integrate monitoring and evaluation opportunities for various conditions and that can easily be adapted to meet country needs . It is our hope that this work will help further the idea of combining efforts for integrated monitoring and surveillance activities among global public health organizations . | In 2012 a comprehensive national serosurvey to assess immunity to vaccine preventable diseases such as polio , rubella , measles , and tetanus was conducted among women of child bearing age in Cambodia . We were able to test this sample set using a multiplex bead assay in order to measure specific antibody responses to the parasites that cause malaria , toxoplasmosis , lymphatic filariasis , cysticercosis , and strongyloidiasis . National prevalence estimates generated from the serologic data show widespread exposure ( >45% positive ) to the soil transmitted nematode worm , Strongyloides stercoralis . In contrast , <5% of women were positive for antibodies to P . falciparum malaria , P . vivax malaria , and lymphatic filariasis , and antibody-positive women were mainly found in the North region of the country . Women who were positive for antibodies to Toxoplasma gondii and Taenia solium ( 5 . 8% and 2 . 6% of the population , respectively ) were not clustered in any particular geographic region . With this study we have shown how the integration of a multiplex assay into a national serosurvey can provide useful information on the prevalence and distributions of medically important parasitic infections . | [
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] | 2016 | Integration of Multiplex Bead Assays for Parasitic Diseases into a National, Population-Based Serosurvey of Women 15-39 Years of Age in Cambodia |
Many species of arthropod are infected by deleterious inherited micro-organisms . Typically these micro-organisms are inherited maternally . Consequently , some , particularly bacteria of the genus Wolbachia , employ a variety of strategies that favour female over male hosts . These strategies include feminisation , induction of parthenogenesis and male-killing . These strategies result in female biased sex ratios in host populations , which lead to selection for host factors that promote male production . In addition , the intra-genomic conflict produced by the difference in transmission of these cytoplasmic endosymbionts and nuclear factors will impose a pressure favouring nuclear factors that suppress the effects of the symbiont . During investigations of the diversity of male-killing bacteria in ladybirds ( Coccinellidae ) , unexpected patterns of vertical transmission of a newly discovered male-killing taxon were observed in the ladybird Cheilomenes sexmaculata . Initial analysis suggested that the expression of the bacterial male-killing trait varies according to the male ( s ) a female has mated with . By swapping males between females , a male influence on the expression of the male-killing trait was confirmed . Experiments were then performed to determine the nature of the interaction . These studies showed that a single dominant allele , which rescues male progeny of infected females from the pathological effect of the male-killer , exists in this species . The gene shows typical Mendelian autosomal inheritance and is expressed irrespective of the parent from which it is inherited . Presence of the rescue gene in either parent does not significantly affect the inheritance of the symbiont . We conclude that C . sexmaculata is host to a male-killing γ-proteobacterium . Further , this beetle is polymorphic for a nuclear gene , the dominant allele of which rescues infected males from the pathogenic effects of the male-killing agent . These findings represent the first reported case of a nuclear suppressor of male-killing in a ladybird . They are considered in regard to sex ratio and intra-genomic conflict theories , and models of the evolutionary dynamics and distribution of inherited symbionts .
Cytoplasmic sex ratio distorters have been reported from many invertebrates [1] . One group of distorters comprises a diverse array of bacteria which distort the secondary sex ratio of their hosts towards females by killing male hosts early in embryogenesis [2] , [3] . Infected females gain an advantage over uninfected females via inbreeding avoidance , resource reallocation or reduction in sibling competition . Theory predicts that , within populations biased towards females as a result of the action of maternally inherited cytoplasmic sex ratio distorters with incomplete vertical transmission , selection will favour autosomal sex ratio compensation [4] , [5] . This could occur by distortion of the primary sex ratio or by distortion of the secondary sex ratio towards males , if loss of female offspring is compensated for by increased fitness of male progeny . No such case has been demonstrated in a diploid harbouring a male-killer ( but see data of male-biased families in [6] , [7] . In addition , selection may favour the evolution of autosomal genes that reduce the vertical transmission or the phenotypic effects of sex ratio distorting bacteria [8] . Autosomal genes that suppress the sex ratio phenotype are known for both cytoplasmic male sterility in plants [9] and sex chromosome meiotic drive in dipterans [10] . They are also suspected in the woodlouse Porcellinoides pruinosus which hosts a feminising Wolbachia [11] . A nuclear suppressor of male-killing could kill the male-killer , or reduce its vertical transmission , or prevent the symbiont from killing males , either by blocking male host recognition , or by blocking the killing act . Such a suppressor has been reported in Drosophila prosaltans where it is suggested there is a recessive allele that prevents transmission of the male-killer [12] . A suppressor conferring resistance has been demonstrated the butterfly Hypolimnas bolina infected with the male-killing Wolbachia strain wBol1 , where infected Southeast Asian H . bolina produce a 1∶1 sex ratio [13] . It has been suggested that the widespread occurrence of males testing positive for known male-killers found via PCR screening of samples of 21 ladybird species , could be indicative of nuclear suppression [14] . Suppressors at fixation might also explain the findings in Drosophila recens and Ephestia cautella , where the Wolbachia strains they harbour cause male-killing when transferred to con-generic host species , although not in the original species [15] , [16] . Cheilomenes sexmaculata harbours a male-killer [17] . This male-killer is transovarially transmitted , is horizontally transferable in haemolymph by microinjection and curable by both high temperature and tetracycline treatment [17] , [18] . The causative agent associated with male-killing has previously been reported to be similar to that causing male-killing in Harmonia axyridis [18] , a Spiroplasma [19] .
Phenotypic assessments , based on egg hatch rates and progeny sex ratio , of a small sample from Tokyo showed that two of 15 matrilines had traits consistent with male-killing , with less than 50% of eggs hatching and female-biased progeny sex ratios ( family Mk1 - 7 males , 73 females; family Mk2 - 2 males , 14 females ) prior to antibiotic ( tetracycline ) treatment . Antibiotic treatment effected a cure of the trait , egg hatch rates and the proportion of males both increasing after treatment ( Mk1 - 71 males , 76 females; Mk2 - 87 males , 93 females ) . Identity of the male-killer was established by PCR amplification of the 16S rDNA gene using general eubacterial primers 27f and 1495r [20] , cloning [21] and sequencing the gene . Briefly , genomic DNA was extracted [19] from females producing female biased sex ratios from both male-killer ( m-k ) lines ( parental females , F1 and F2 progeny ) , from F1 eggs of both m-k lines , from F1 and F2 females from a normal ( N ) sex ratio line ( N12 ) that never produced a biased sex ratio ( five generations , 37 families ) , and from F1 and F2 progeny from antibiotic treated females from both m-k lines . The m-k line females from all three generations , the m-k eggs , but not the N line or progeny from antibiotic treated females , generated a 16S rDNA PCR product , which was then cloned and sequenced ( 1517 bp ) . A majority rule consensus sequence was generated for the bacterium to account for PCR errors ( total 22 sequences , of a total of 26 sequences generated at this stage , 3 contaminant Streptococcus lactis and one pGEM non-transformant also sequenced ) . The sequence produced was submitted to EMBL ( accession number AJ272038 ) . This was compared with sequences in the EMBL , Genbank , DDBJ and PDB databases using BLASTN [22] . The sequence was identified as a γ-proteobacterium with closest similarity to a variety of secondary symbionts , of aphids ( 97-98% identity ) , in particular Hamiltonella defensa ( 98% identity ) , of whiteflies , particularly of Bemisia tabaci ( 97% identity ) and to a number of bacteria of the genus Yersinia . To investigate these relationships further , a phylogeny was produced using 16S rDNA sequences from 25 of the closest matches from the BLASTN search ( Figure 1 ) . It is interesting to note that the male-killer clearly falls within the clade of aphid secondary symbionts and is distinct from both the whitefly symbiont clade and the Yersinia clade . The tree is not particularly informative regarding relationships within the aphid symbiont clade , with almost all of the bootstrap values being below 500 . However , it is certainly suggestive of horizontal transfer of symbionts both between aphid species , as has been reported [23] , [24] , [25] and between prey and predator species as has been suggested for other coccinellid male-killers [26] , [27] . The 16S rDNA phylogeny highlights one further interesting feature of this male-killer , as it supports the suggestion of horizontal transfer of symbionts with a change in phenotype between the alternative hosts . Secondary symbionts are known to confer protection to their hosts against natural enemies [25 for review] , but natural enemy protection co-occurring with reproductive parasitism in a clade has to date only been established for Wolbachia [28] . This finding represents the second instance of a γ-proteobacterium causing male-killing [29] , and the first recorded instance in a coccinellid . To confirm the absence of other known male-killers , in particular Spiroplasma , but also Rickettsia , Wolbachia and Flavobacteria , specific PCR assays were carried out on individuals from both male-killer lines , from the parental and F1 generations . In all cases these tests proved negative [27] . The male-killer in this sample of C . sexmaculata is thus not the same as that identified from H . axyridis [19] , as has previously been assumed [18] . Crosses designed to test the inheritance of the trait , involving F1 females from the two putative male-killer lines ( Mk1 and Mk2 ) , with males from normal sex ratio matrilines produced unexpected progeny sex ratios ( Table 1 ) . All three crosses using males from one line ( N5 ) produced significantly female-biased sex ratios , one ( Mk1 . 1 ) being almost all female , the others ( Mk1 . 4 and Mk2 . 3 ) giving approximately 2∶1 ratios of females to males . The remaining seven crosses , involving males from two other lines ( N1 and N8 ) produced normal ( approximately 1∶1 ) sex ratios . These data suggest either an exceptionally low and variable vertical transmission of the sex biasing trait , or paternal influence on progeny sex ratios . To test the latter possibility , male parents were transferred between some of the crosses , the male ( N5 ) from Mk1 . 1 being mated multiply to the female from Mk1 . 2 and once to the female from Mk1 . 5 , and the male ( N1 ) from Mk1 . 3 being mated to the Mk1 . 1 female . In all cases the progeny sex ratios changed following these additional matings ( Figure 2 ) , that of Mk1 . 1 rising from 0 . 013 to 0 . 355 , with those of Mk1 . 2 and Mk1 . 5 becoming significantly female-biased . The Mk1 . 2 female , mated multiply to the second male produced a strong female bias from three days after introduction of the new male and for the remainder of her life . However , the Mk1 . 5 female , mated just once to a second male , again after a lag ( four days ) produced a strong and significant female bias ( 14 male∶42 female ) over four days , before her progeny sex ratio reverted towards normality . This reversion suggests that sperm from the singly mated second male was used for a block of time before utilisation reverted to sperm from the multiply mated first male . Extended replication of this mate swapping procedure using both male-killing matrilines showed that changes in the sex ratio following change of male were reversible if males were changed back ( data available on request ) . These data suggest the presence of a factor , acting through sperm or some other element in the ejaculate , that inhibits the male-killing action of the bacterium . The initial data could be explained by a unifactorial dominant nuclear gene . Four questions were addressed to test this hypothesis and detail the nature of the suppressor system . To address i ) and ii ) on the basis of the initial hypothesis , expected progeny sex ratios from monogamous crosses involving individuals of alternative genotypes with respect to both the male-killer and the suppressor locus ( alleles: suppressor = res+ , non suppressor = res− ) were calculated ( Table 2 ) . Crosses involving individuals of inferred suppressor genotype , the females being F1 , F2 or F3 from male-killer matrilines , produced progeny sex ratios consistent with expectation on the basis of Mendelian inheritance ( Table 2 ) . The results indicate that the expression of the suppressor is not affected by the sex of the parent from which the suppressor is inherited . For example , cross Mk1 . 1 . 4 . 9 , in which both parents were heterozygous for the suppressor gave a progeny sex ratio of 0 . 419 ( n = 234 ) , close to expectation and significantly different from both the maximum expected sex ratio if only paternally derived suppressors are expressed ( χ21 = 4 . 869 , p< . 05 ) , and from a 1∶1 sex ratio ( χ21 = 6 . 171 , p<0 . 05 ) . Mk1 and Mk2 were maintained for five generations , 118 families ( minimum number of progeny = 10; mean number of progeny = 62 . 7 ) being reared . In 51 of these the genotype with respect to the rescue gene locus was inferred for both parents prior to progeny being obtained . In 46 families , results were consistent with the theorised autosomal ( or pseudoautosomal ) nature of the locus . The locus is not sex-linked . In the remaining five , 16S rDNA sequence analysis , performed post hoc , showed that the female parent lacked the male-killer . This proportion of revertants is roughly consistent with the estimated vertical transmission efficiency , a , of the male-killer seen in Mk1 ( a = 0 . 89 ) and Mk2 ( a = 0 . 83 ) . To address question iii ) , two female F2 progeny from predominantly female families of both the Mk1 and Mk2 matrilines were crossed to homozygous res+ males . The progeny sex ratio in each of these families was close to 1∶1 . res+res− female offspring from these crosses were mated to res−res− males . Of eight crosses , one produced a normal sex ratio , the remainder producing ratios approximating to 1 male: 2 female ( data not shown ) . Given the vertical transmission efficiency of this male-killer , these results show that the male-killer is inherited through females , even when its pathological effect on males has been suppressed . Verification was obtained by sequencing the 16S rDNA PCR product from parents and six progeny ( 3 male , 3 female ) of two of the families that produced a 1 male: 2 female sex ratio . Female parents , all male progeny and five of the six female progeny were shown to bear the male-killer . Verification that the suppressor acts as a rescue gene was obtained by demonstrating that adult males from suppressed male-killer females produced 16S rDNA PCR product with the same sequence as the γ-proteobacterium identified as the male-killer ( question iv ) ) . Two male progeny from two m-k families , one of which produced a 1∶2 sex ratio and one which produced a normal sex ratio ( Mk1 . 4 and Mk2 . 5 , respectively ) were submitted to the same PCR cloning and sequencing protocol as used for the initial identification of the male-killer . In each case the γ-proteobacterium was shown to be present . The male-killer is thus present but not expressed in ‘rescued’ males . Crosses of such males , inferred to be res+res− and to carry the male-killer , to res−res− females lacking the male-killer produced normal progeny sex ratios . The male-killer is thus not inherited from males . Taken together , these results suggest that the endosymbiont is not killed by the suppressor . Rather the suppressor acts as a rescue gene for males .
The discovery of a nuclear gene that rescues males from the pathological effects of a maternally inherited bacterium that otherwise kills males , to the benefit of the males' female siblings that carry and vertically transmit the bacterium , is in accord with theories of sex ratio [30] , [31] and intra-genomic conflict [4] , [5] . Selection favouring a suppressor gene will be a direct consequence of sex ratio distortion . Autosomal genes that act against sex ratio distorters have been recorded in isopods infected with feminising Wolbachia and in a butterfly infected with male-killing Wolbachia . In Armadillium vulgare , the main effect of such genes is to reduce bacterial transmission to progeny [32] . In contrast , in P . pruinosus , autosomal genes are conjectured to prevent the feminising effect of Wolbachia [11] . This is analogous to the situation in C . sexmaculata and H . bolina [13] where observations are compatible with a single , dominant autosomal locus suppressing the male-killing effect of the bacteria . Most models considering the evolutionary interactions between sex ratio distorting symbionts and suppressors are based on the assumption that the suppressor will kill the symbiont or reduce its vertical transmission [33] , [34] . The dynamics of a male rescue gene may be quite different , and will depend on its cost , if any , in the absence of the male-killer . A male-killer in the presence of a male rescue gene should be selected against , due to the cost on hosts of carrying the male-killer and the lack of fitness advantages to infected females resulting from male death [3] . Such a male-killer then has several alternative fates . It may be selected to extinction . It may become polymorphic , male killer prevalence being determined by its transmission dynamics and fitness compensation and the costs to hosts of both it and the rescue gene . It may circumvent the rescue gene by evolving a different mechanism to kill males ( cf . the double feminising effect of Wolbachia in A . vulgare [35] ) . Finally , it may reduce its cost on hosts , becoming costless ( persistence would require vertical transmission close to 1 ) or even beneficial - cytoplasmic male-killers are an exquisite testing ground for theories of virulence [3] . It is interesting to compare the situation in C . sexmaculata with that in H . bolina . In the latter , the suppressor has recently and rapidly spread to fixation in Southeast Asian populations , but is absent from Polynesian populations [13] . A recent model [36] examines the dynamics of such systems with reference to host suppressors of male-killing and Wolbachia that are also able to induce cytoplasmic incompatibility ( CI ) . Here the model predicts that ( in the absence of CI , and so pertinent to this study ) the maximum cost of a dominant suppressor of male-killing that allows invasion of a host population will increase as the male-killer prevalence increases . The model also predicts ( in the absence of CI ) that a costly suppressor that does invade will become polymorphic and the frequency of the male-killer will be reduced . Further , the authors suggest that polymorphic suppressors of the male-killing action of non-Wolbachia male-killers ( not known to induce CI ) should be more common than of Wolbachia male-killers . Our findings fit well with this model . In contrast to the male-killer in C . sexmaculata , the male-killing Wolbachia in H . bolina may also cause CI and where this occurs the model demonstrates that the dynamics of the system are altered , with corresponding changes in the rate of spread , fixation and frequency of infection that depend on the level of CI , the cost of the suppressor , the transmission efficiency and the initial male-killer frequency . Suppressor spread may be inhibited , where there is CI , but where a suppressor does spread it is predicted to lead to fixation of both itself and the infection , giving an appearance of a population exhibiting only CI [36] . The Fuchu population of C . sexmaculata studied here is polymorphic for the rescue gene . Further investigation of whether this is a balanced or transient polymorphism , and determination of whether the rescue gene imposes a cost on bearers , would provide valuable insight into the dynamics of the spread of suppressors , as well as the generality of the findings in H . bolina . Establishment of the frequencies of the rescue gene and male-killer in different populations of C . sexmaculata would be valuable . Further , molecular investigation of coccinellids with ecological traits making them liable to male-killer invasion , but in which searches for male-killers using phenotypic assays have proved negative , may reveal presence of beneficial symbionts that are a peaceful resolution of an evolutionary arms race between a male-killer and a suppressor system .
Lines were designated either Male-killer – Mk , or Normal – N , to reflect the phenotypic status of the P1 females in the original sample . The lines were then numbered sequentially within the two categories , hence Mk1 and Mk2 were the two male-killing lines and N1-N13 the 13 normal lines . Subsequent generations show the parental name followed by a ‘ . ’ to indicate a new generation , and then a number e . g . Mk1 . 3 is the third cross generated from F1 female progeny of Mk1 and Mk1 . 1 . 3 is the third cross generated from an F2 female , progeny of Mk1 . 1 . These numbers simply reflect P1 phenotype and indicate the matriline . They do not reflect rescue gene status and hence do not necessarily indicate F1 ( or subsequent generation ) phenotype . Rescue gene allelic status is indicated by res+ and res- for suppressor and non-suppressor , respectively . For some tests , males known to lack the male-killer suppressor were necessary . These were obtained by tetracycline treatment of singly mated , male-killer bearing females ( for both Mk1 and Mk2 ) . Females were mated once and male-killer status confirmed . Females showing characteristic half hatch rates were treated with antibiotic . Where these initial clutches produced only female progeny , and assuming , as was subsequently shown , that res+ was expressed when inherited from the female parent , males produced in the later clutches of these crosses would be homozygous res− . Different categories of ladybird were assessed for the presence of a bacterial male-killer by performing PCR using general eubacterial primers that amplify the 16S rRNA gene ( primer pair 27f , 1495r ) [20] . The PCR was carried out using Expand High Fidelity PCR System ( Boehringer Mannheim ) . The product was purified using Microcon Microconcentrators ( Amicon Ltd . ) and ligated into pGEM T-vector ( Promega ) . The resulting plasmids were transformed into E . coli DH5α as described by Hurst et al . [21] . Plasmids containing insert DNA were purified using Wizard Minipreps DNA purification system ( Promega ) . Inserts were sequenced using the ABI PRISM BigDye Terminator cycle-sequencing ready-reaction kit ( Perkin Elmer ) and visualised on an ABI 377 automated sequencer . Primers pUC/M13 forward and reverse , 27f and 1495r and internal primers [37] were used to sequence both strands of the whole unit . The 16SrDNA sequence generated above was aligned with 16S rDNA sequences from 25 different BLASTN matches with high alignment scores , which were downloaded from the nr database . The accession numbers of the 25 sequences used were: AY296733 , CP001277 , AF293622 , EU348313 , AY264676 , AF293626 , AF293616 , AY692361 , AY264675 , AY136161 , AY136136 , AY136164 , AY136162 , AY136163 , AY136145 , AY136156 , AY136148 , EU178101 , AB273745 , AM403659 , FM955884 , AL590842 , CP001048 , NR028786 , U90757 . Sequences were aligned with ClustalW2 [38] , minor manual adjustments were made using Seaview [39] , and a neighbour-joining tree was generated excluding sites with a gap in any sequence , using Kimura's 2 parameter correction , with 1000 bootstrapped replicates , implemented in ClustalW2 [38] . The tree was displayed using NJPlot [40] . Under a model where the suppressor is a single gene , with alleles res+ and res− , where res+ is a dominant rescue allele and where the vertical transmission efficiency of the male-killer is between 0 . 8–1 . 0 , the expected progeny ratios from different crosses can be calculated as follows: In all cases female progeny will survive , whether infected or not; what varies is the proportion of males that inherit the infection and further the proportion of those inheriting the infection which carry a res+ allele and so ( under this model ) survive . If the proportion of progeny inheriting the male-killer varies between 0 . 8 and 1 . 0 a maximum of 20% of the progeny ( 10% male and 10% female ) will lack the male-killer . In a cross where both parents are free from the suppressor ( res− res− x res− res− ) and the parental female is infected with the male-killer , none of the progeny will inherit a suppressor and hence all the male progeny that receive the male-killer will die . This will be between 80 and 100% of the males , i . e . if vertical transmission is 1 , 0 males will survive , if the vertical transmission is 0 . 8 , 20% of the progeny will not inherit the male-killer , 10% of these are male , and will now survive , increasing the sex ratio to 0 . 1/0 . 6 = 0 . 167 . Similarly if the parents are res− res+ x res− res− half the progeny will inherit a suppressor allele . If vertical transmission is 100% one quarter of the progeny will die ( males with no suppressor ) , and one third , 0 . 333 , of the remaining progeny will be male . If vertical transmission is 80% this would mean of the 20% of the progeny that fail to inherit the male-killer , 10% will be male ( as above ) , of which half will be res− and so will now survive , increasing the proportion male to 0 . 3/0 . 8 = 0 . 375 . If the parents are res+ res− x res+ res− three-quarters of the progeny will inherit a suppressor so 3/7 or 0 . 429 of the surviving progeny will be male assuming all inherit the male-killer . If vertical transmission is 80% , again 10% of the progeny that fail to inherit the male killer will be male . Now only a quarter of males are res− , hence another 2 . 5% will survive , making the proportion male 0 . 4/0 . 9 = 0 . 444 . Finally , if one parent is homozygous res+ then all progeny inherit a copy of the suppressor , all will survive and the proportion male will be 0 . 5 , regardless of the vertical transmission efficiency . These calculations assume that in the absence of the male-killer the sex ratio would be 1∶1 , that the male-killer is inherited equally by males and females and that it always kills males unless there is a suppressor present . Simulations were carried out to estimate the 95% confidence limits of the expected sex ratios . This can be illustrated using Mk 1 . 7 . Here the range of possible sex ratios is from 0–0 . 167 , and the sample size is 12 . First , a random number is chosen , and on the basis of this , a sex ratio is randomly chosen from an even distribution from 0 to 0 . 167 . Then , using this sex ratio , a binomial sample of 12 individuals is created , of which between 0 and ( theoretically ) 12 will be male . This process was repeated 100 , 000 times , to produce a distribution of the numbers of males seen in samples of 12 . In this case the distribution is: 0 males: 41 . 8% 1 male: 30 . 7% 2 males: 17 . 2% 3 males: 7 . 3% 4 males: 2 . 3% 5 males: 0 . 5% 6 males: 0 . 1% From these values it is concluded that any number of males in the data above 3 would give significant evidence against the hypothesis , since this has a chance of happening that is below 5% , and thus the 95% confidence limits run from zero to three males from 12 , or from 0–0 . 25 as the sex ratio . Corresponding calculations were carried out for each cross listed in Table 2 . EMBL: AJ272038 | Normally , in sexually reproducing organisms , the sex ratio ( ratio of males to females ) is 1∶1 . However , examples are known where this is not the case and there are more females than males in a population . Extreme bias in sex ratio can lead to females failing to find a mate . We studied Cheilomenes sexmaculata , a ladybird species that has females that produce more female than male offspring . In aphid-eating ladybirds , this phenomenon has been widely reported and is known to be due to the presence of bacteria that live inside the mother and are passed via her eggs to her offspring . In eggs destined to become male , the bacteria kill the embryo by some unknown mechanism . This is known as male-killing . Female offspring develop normally . Evolutionary theory predicts that in such systems , the genome of the host can fight back if a variant arises that stops the bacteria killing male offspring . In C . sexmaculata we found females that carried the male-killer but the sex ratio of their offspring depended on the male that they mated with . We carried out breeding tests to show that some ladybirds had a version of a gene that rescued the male offspring from the pathological effects of the male-killer . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [
"evolutionary",
"biology/animal",
"genetics",
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"biology/microbial",
"evolution",
"and",
"genomics",
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"biology/evolutionary",
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] | 2010 | Intergenomic Arms Races: Detection of a Nuclear Rescue Gene of Male-Killing in a Ladybird |
The RNA world hypothesis , that RNA genomes and catalysts preceded DNA genomes and genetically-encoded protein catalysts , has been central to models for the early evolution of life on Earth . A key part of such models is continuity between the earliest stages in the evolution of life and the RNA repertoires of extant lineages . Some assessments seem consistent with a diverse RNA world , yet direct continuity between modern RNAs and an RNA world has not been demonstrated for the majority of RNA families , and , anecdotally , many RNA functions appear restricted in their distribution . Despite much discussion of the possible antiquity of RNA families , no systematic analyses of RNA family distribution have been performed . To chart the broad evolutionary history of known RNA families , we performed comparative genomic analysis of over 3 million RNA annotations spanning 1446 families from the Rfam 10 database . We report that 99% of known RNA families are restricted to a single domain of life , revealing discrete repertoires for each domain . For the 1% of RNA families/clans present in more than one domain , over half show evidence of horizontal gene transfer ( HGT ) , and the rest show a vertical trace , indicating the presence of a complex protein synthesis machinery in the Last Universal Common Ancestor ( LUCA ) and consistent with the evolutionary history of the most ancient protein-coding genes . However , with limited interdomain transfer and few RNA families exhibiting demonstrable antiquity as predicted under RNA world continuity , our results indicate that the majority of modern cellular RNA repertoires have primarily evolved in a domain-specific manner .
Following demonstration that RNA can act as genetic material [1]–[3] and biological catalyst [4] , [5] , the study of the origin and early evolution of life on Earth has been heavily focused on the potential for an RNA world . The RNA world hypothesis is that RNA was both genetic material and main biological catalyst , prior to the advent of DNA and templated protein synthesis [6]–[8] . The chemical plausibility of an RNA world has been intensively investigated through the application of in vitro methodologies that enable selection and subsequent characterization of novel RNA functionalities [9] , [10] . Equally , the discovery of naturally-occurring functional RNAs in biological systems has expanded our understanding of the ways in which extant organisms utilize this macromolecule in a wide range of contexts , including catalysis , regulation , and as sequence-based guides [11]–[15] . A central tenet of RNA world theory as an account of the early evolution of life on Earth is the Principle of Continuity [6] , namely , that modern systems are the product of gradual evolution from earlier states . Consequently , it is possible that some RNA families could be direct descendants of molecules that first evolved in the RNA world [16] , [17] . The broad functionality of RNA both in terms of catalysis and biological function hints at a possibly complex RNA world [12] , [17] , [18] , but assessing the antiquity of individual RNA families has been hampered by limited comparative data , and difficulties in annotating RNAs in genomes [19] . At the same time , it seems likely that many RNA families significantly postdate the RNA world , having evolved de novo much later in the evolution of life [13] , [20] . Indeed , for protein-coding genes , both very deep evolutionary histories [21]–[23] and more recent origins [24] , [25] have been established . Assigning relic status to individual RNAs is not without significant complication . First , placing RNAs with non-universal distributions into the common ancestor of archaea , bacteria and eukaryotes requires lineage or domain-specific losses to be invoked [26] . While loss is plausible , it is difficult to verify at the level of cellular domains , since recent origin versus lineage-specific loss following a more ancient origin cannot be readily distinguished , and other data must be considered [27] , [28] . Another process that may obfuscate the history of early RNA-based life is the propensity for genes to undergo horizontal transmission , from a donor to a recipient . For protein-coding genes , there is now overwhelming evidence that horizontal gene transfer is a significant evolutionary force , particularly for microbes [29] , [30] . Consequently , gene-based phylogenies do not always provide an accurate means of gauging the evolutionary history of species , and , extrapolating across the tree of life and several billion years of evolutionary history , it is plausible that no gene will have remained untouched by horizontal gene transfer [31] . Consequently , historical signal consistent with RNA world continuity may have been erased through subsequent gene transfer events . Conversely , effective spread by horizontal transmission could lead to RNAs appearing artificially ancient . Finally , many RNAs may be more recent evolutionary innovations , and may not be RNA world relics [13] . These concerns notwithstanding , it remains commonplace for novel RNAs or RNA families to be discussed in regard to their potential relevance to the RNA world . Indeed , there are countless qualitative surveys derived from review of the experimental literature ( see for example [11] , [12] , [14] , [17] , [18] , [32] ) , which often extrapolate deep evolutionary origins from limited comparative data . Problematically , this approach has led to the RNA world model being populated with RNAs whose distributions are patchy , and antiquity has often been inferred on speculative grounds , following detailed experimental characterisation of RNAs from a handful of model organisms . Against this backdrop , it is perhaps of little surprise that more vociferous critics have dubbed this endeavour the ‘RNA dreamtime’ [33] . While detailed studies have been performed for single RNA families ( Table S1 in Text S1 ) , no published data present a systematic analysis covering all RNA families , despite this now being routine for protein-coding genes . For RNA genes , an equivalent analysis is long overdue but has not been possible because , until recently , comparative data were not of sufficiently high quality . We therefore sought to systematically address whether the phylogenetic distribution of extant RNAs fits with direct descent from an RNA world , as predicted under the Continuity hypothesis , or whether the distribution of extant RNAs better reflects more recent ( post-LUCA ) origins . In addition , we sought to examine whether horizontal transfer between cellular domains ( and viruses ) is detectable for RNA families . We report an analysis of over 3 million RNAs spanning 1446 families in the Rfam database [34] , revealing that the overwhelming majority of families ( 99% ) are restricted to a single domain of life . By contrast , fewer than 1% show evidence of either a deeper evolutionary origin , or of interdomain transfers . We conclude that , while , on these proportions , the RNA world ‘palimpsest’ is only a fraction of the RNA repertoires of modern genomes , the most ancient RNA families nevertheless belie evidence of an advanced protein synthesis apparatus . Strikingly , we report that interdomain horizontal gene transfers are also minimal for RNA genes , in marked contrast to the significant levels detected for protein-coding genes . Our analyses thus serve to move the current state-of-the-art from erudite literature review to systematic analysis of the distribution and antiquity of large numbers of RNA families .
We first asked whether a systematic analysis of RNA families expands our knowledge of ancient RNAs beyond those identified by traditional experimental work . To examine the degree to which extant RNAs can be traced to earlier evolutionary periods , we performed comparative analyses of annotated RNAs based on data from all three domains of life as well as viruses . To this end , we used the Rfam ( RNA families ) database [34] , which groups RNAs into families , and families into clans , based on manually-curated alignments , consensus secondary structures , covariance models [35] and functional annotations . RNAs within families and clans can therefore be claimed to share a common ancestry [34] . All analyses presented here are based on Rfam 10 . 0 , which consists of over 3 million annotations grouped into 1446 families and 99 clans [34] . To generate a high-quality dataset , we first established the distribution of all individual RNA sequence entries in Rfam by reference to the NCBI taxonomy database , and manually vetted and removed probable false positive annotations . From the resulting dataset , we generated an initial survey of families and clans across bacterial , archaeal , eukaryotic and viral genomes ( Figure 1 ) . Two patterns are immediately clear . First , each domain carries a large number of entries absent from the other domains , with limited overlap observed between domains , or with viruses . Second , only seven Rfam families are present across all three domains . That we observe distinct domain-level RNA repertoires appears consistent with the view that the three domains of life are genetically distinct [36] . However , families present in more than one domain ( or shared with viruses ) may be the result of either vertical evolution from a common ancestor or horizontal transfer of genes between domains [30] , [36] . We next sought to establish whether the distribution the 12 interdomain Rfam families/clans ( Figure 1 ) could be attributed either to vertical inheritance or horizontal gene transfer . Previous studies and data on distribution allow a predominantly vertical pattern of inheritance to be attributed to only five families ( small subunit ( SSU ) and 5S rRNAs , tRNA , RNase P RNA , signal recognition particle RNA ( SRP RNA ) with four showing evidence of HGT ( group I & II introns , organellar large subunit ( LSU ) rRNA , IsrR RNA ) ( Table S1 in Text S1 ) . Ribosomal RNAs are not fully represented in Rfam , being amply covered by other databases ( e . g . [37] , [38] ) , but their deep evolutionary history has been readily traced ( Table S1 in Text S1 ) . Combined , these data confirm a minimal reconstruction of the RNA repertoire of LUCA consistent with that observed for protein-coding genes [21] , with the demonstrably oldest RNAs and the majority of such proteins being involved in translation and protein export ( Figure 2 ) . Consequently , while the number of RNA families traceable to LUCA is an order of magnitude lower than for proteins , the spread of functionalities is nevertheless very similar in extent . A vertical trace is suspected but not demonstrated for the universally distributed TPP riboswitch ( Table S1 in Text S1 , Figure 3 ) , which modulates gene expression in response to thiamine pyrophosphate ( TPP ) . The analysis of patterns of inheritance for RNAs is complicated by their short lengths and generally low levels of sequence conservation . As riboswitches regulate cognate mRNA in cis , vertical transmission may be tested by generating phylogenies from the protein products , on the assumption that the riboswitch and ORF have coevolved . We therefore generated a phylogeny for THIC , the only TPP-regulated gene product present in all three domains . The phylogeny shows eukaryote sequences grouping with proteobacteria ( Figure S1 ) , consistent with horizontal transmission of TPP-riboswitch regulated ThiC to the eukaryote lineage from a bacterial donor . Several independent observations are consistent with horizontal transmission: Arabidopsis THIC is nuclear-encoded , but targets to the chloroplast [39] , plant ThiC can complement an E . coli ThiC mutant [40] , and eukaryotic TPP riboswitches show limited distribution [41] ( Rfam 10 . 0 ) . Moreover , THI1 , which also carries a TPP riboswitch in its mRNA leader , is also targeted to chloroplasts and mitochondria [42] . While an early origin for TPP riboswitches [11] remains plausible , this is difficult to reconcile with our THIC phylogeny , since bacterial and archaeal sequences are not monophyletic under any rooting ( Figure S1 ) . Also noteworthy is the CRISPR/Cas system , which combats viral and plasmid infection in both bacteria and archaea . Horizontal transmission has been suggested for this system , but interdomain transfer is thought to be limited [43] . Examination of CRISPR crRNA family distribution reveals that 54 of 65 Rfam crRNA families are restricted to a single domain ( Table S2 in Text S1 ) . The remaining 11 families fall into two clans ( CRISPR-1 , CRISPR-2 ) , which include crRNAs in both bacterial and archaeal genomes . However , only one Rfam family from each of these two clans contains annotations deriving from both domains . While short sequence length of crRNAs precludes phylogenetic analyses , the distribution we report ( Table S2 in Text S1 ) is compatible with sporadic interdomain transfer , consistent with a phylogenomic analysis of Cas genes/clusters which reported low levels of horizontal transmission [44] . The low number of observed interdomain RNA families suggests that , in contrast to protein-coding gene repertoires , RNA repertoires are surprisingly refractory to interdomain transfers . While we do see evidence of organellar contributions , these are few in number , in marked contrast to the high numbers observed for protein-coding genes [45] , [46] . We next sought to establish the distribution of RNA families within each domain , since our initial analysis ( Figure 1 ) does not consider within-domain taxonomic distribution of Rfam families . A broad distribution may indicate an early origin of a given family , but information on distribution alone cannot distinguish between horizontal and vertical modes of transmission . As short length and limited sequence conservation preclude robust phylogenies for the vast majority of RNA families , distribution cannot be used to directly infer the RNA repertoire of the last common ancestor ( LCA ) of each domain . Nevertheless , such information may indicate whether the RNA repertoires of the three domains are functionally distinct . We therefore collated families present in at least 50% of major within-domain taxonomic divisions ( Figure 3 , Dataset S2 ) . Surprisingly , the number of broadly distributed families/clans within each domain is small ( Archaea 13/69 = 18 . 8% , Bacteria 15/223 = 6 . 7% , Eukaryotes 20/826 = 2 . 4% ) , though among eukaryotes there are a high number of clans , which may encompass multiple RNA families with a shared evolutionary history . Two patterns emerge from this analysis ( Figure 3 ) . First , eukaryote and archaeal repertoires are dominated by small nucleolar RNAs ( snoRNAs ) . Second , the most broadly distributed bacterial RNAs are regulatory . Closer investigation of the snoRNA repertoires across archaea and eukaryotes reveals that C/D family RNAs are broadly distributed; H/ACA family RNAs , while widespread among eukaryotes , are only known from Euryarchaeota [47] , [48] , and Archaeal H/ACA RNAs are not currently included in Rfam [34] . Strikingly , of the >500 snoRNA families included in this study , none are shared across archaea and eukaryotes . While a deep origin of snoRNPs is supported by surveys of protein and RNA components [49] , this is not reflected by existence of conserved RNA families [28] , for which only scant evidence exists [50] , [51] . In eukaryotes , a strong domain-specific evolutionary trace is attributable to snRNAs ( Figure 3 , Table S3 in Text S1 ) , consistent with other studies indicating both the major and minor spliceosome were features of the Last Eukaryotic Common Ancestor ( LECA ) [52]–[54] . A different picture emerges for miRNAs however . The broad distribution of miRNAs is consistent with the suggestion that RNAi pathways trace to the LECA [55] , with 26/452 miRNA families present in more than one eukaryotic supergroup ( Dataset S3 ) . However , closer inspection reveals most are singleton false positives or artefactual family groupings . Our dataset therefore does not allow the placement of any individual miRNA families in LECA . A broad qualitative difference between bacteria compared to archaea and eukaryotes is the preponderance of conserved regulatory elements , primarily riboswitches ( Figure 3 ) . However , this observation is based on only that small fraction of Rfam families present in ≥50% of taxonomic divisions . To further assess whether there are qualitative differences between the functional RNA repertoires across the three domains and viruses , we took advantage of the organization of Rfam into different functionalities . As is evident from Figure 4 , common functionalities across all three domains are sparse . Riboswitches and ribozymes indicate the ubiquity of small metabolite-based regulation and catalytic function , but of the numerous families included in this analysis , only RNase P RNA is directly traceable to the LUCA ( Figures 2 & 3 ) . Functionalities shared between archaea and eukaryotes to the exclusion of bacteria are restricted to snoRNA-dependent RNA modification , and CRISPRs are the only prokaryote-specific functionality . Interestingly , a number of RNA functionalities present in bacteria lack archaeal or eukaryotic representatives ( cis-regulatory leaders , thermoregulators , sRNAs ) , and Rfam contains no archaeal-specific functionalities ( Figure 4 , Dataset S4 ) , possibly attributable to the smaller number of experimental screens for novel RNAs across members of this domain . In comparing the RNA repertoires of the three domains , a key question is whether the underlying Rfam data cover a reasonable spread of species within each domain , or whether data from a few species or phyla dominate . This is important in that the low number of broadly distributed families/clans we observe within each domain could be the result of an underlying sampling bias . A priori we may expect a significant bias , given current genomic coverage of microbial biodiversity . For instance , a recent survey of snoRNAs indicates there is broad , though nevertheless patchy coverage across major eukaryotic and archaeal groups [49] . We therefore examined the underlying taxonomic distribution of all domain-specific Rfams . For all three domains , entries are heavily skewed , with a majority of Rfam annotations deriving from a narrow phylogenetic diversity ( Figure S2 ) . For protein-coding genes , discovery of novel proteins has been significantly enhanced by sequencing of genomes chosen for maximal phylogenetic diversity [56] . While de novo computational discovery of novel ncRNAs is non-trivial by comparison , we were nevertheless interested in establishing whether the additional phylogenetic coverage provided by the Genomic Encyclopedia of Bacteria and Archaea ( GEBA ) [56] impacted the number of broadly distributed Rfam families . Under the assumption of vertical inheritance , we therefore treated RNAs as characters on the GEBA phylogeny . Our analysis yielded four additional bacterial candidates ( marked with asterisks in Figure 3 ) , though again we caution that broad distribution may be generated through HGT , so these candidates cannot be placed in the bacterial ancestor . Nevertheless , this modest improvement suggests GEBA [56] , and targeted experimental screens informed by phylogeny [49] will provide a valuable framework , both for improving knowledge of RNA family distribution and in focusing experimental screens for novel RNA families . How should we interpret these data ? The limited distribution of domain-specific RNAs is likely to be biased by sampling , a problem that affects all genomic data , and is even more acute for detailed experimental data . On available data , we find that only a minority of domain-specific RNAs exhibit a broad distribution . A broad distribution could result from vertical inheritance , but it could also be the result of horizontal gene transfer . Taxonomic biases might underestimate the number of RNAs vertically traceable to the ancestor of a domain , whereas horizontal gene transfer might be expected to expand the distribution of some RNAs . Assuming that current sampling has gaps , but is not completely uninformative [49] , available data suggest that a high proportion of RNAs are likely to be evolutionarily young , and will not trace to the LCA of the domain in which they reside . We have examined the evolution and diversity of RNAs across the entire tree of life , an important complement to previous comparative studies on RNA metabolism [11] , [17] and RNA-associated protein families [57] . Large-scale analyses of the RNA repertoire are only now becoming possible through improved methodologies for RNA identification and greater integration between RNA discovery and online databases . It is commonplace for novel RNAs or RNA families to be discussed in regard to their potential relevance to the RNA world , yet RNAs with limited distribution are difficult to reconcile with a very ancient evolutionary origin unless massive losses are invoked . Excepting the possibility of losses ( which cannot be readily tested since the evidence for antiquity has been erased ) , our study shows that direct evidence for the RNA continuity hypothesis remains scant; there is undoubtedly an RNA ‘palimpsest’ [16] , but it is not possible to expand this through systematic comparative analyses . Conversely , we find clear evidence of distinct domain-level repertoires , but limited evidence of inter-domain transfers , consistent with a recent analysis indicating a detectable vertical trace amidst ongoing HGT [30] . The paucity of shared eukaryotic and archaeal RNA regulatory processes ( Figure 4 ) and the marginal bacterial contribution to the eukaryote RNA repertoire , support the view that eukaryotic mechanisms of RNA regulation are a domain-specific invention [15] , and extend this view to the other two domains . While we see qualitative similarities between archaea and eukaryotes ( Figures 3 & 4 ) , in agreement with studies indicating a phylogenetic affinity between these two domains [58] , these are currently restricted to snoRNAs . The clear differences in RNA functional repertoires between eukaryotes , archaea and bacteria ( Figure 4 ) strengthen the case for recognizing the biological distinctness of the three domains [36] , independent of uncertainty surrounding their specific phylogenetic relationships [59] .
Annotated noncoding RNA data used in this study was derived from data curated in Release 10 . 0 of the Rfam database [34] ( http://rfam . sanger . ac . uk/ ) . The distribution of Rfam families ( Dataset S1 ) was established in two steps . First , for a given family , all annotations across the EMBL database [60] ( http://www . ebi . ac . uk/embl/ ) were binned into domains using the taxonomic information attached to each sequence . We then inspected annotations from families whose distribution spanned more than one domain to identify possible false annotations . For all Rfam families with annotations spanning two or more domains ( including viruses ) we first confirmed the taxonomic affiliation of each sequence through reciprocal blasts against the GenBank database and removed any cases where sequences were clearly misannotated ( e . g . bacterial sequencing vectors in eukaryote genome projects ) . Next , we inspected the quality of each annotation with reference to Rfam seed alignments . Any sequences with a bitscore within +10 bits of the individual bitscore cutoffs for curated seed alignments , and where sequence similarity was deemed insufficient to reliably establish homology , were discarded . In assigning Rfam entries to specific taxonomic groups of bacteria and archaea ( Figure 3 , Dataset S1 ) , we used the top-level classifications within each domain in the NCBI Taxonomy Database . At the time the analyses were performed , the proposed archaeal phylum Thaumarchaeota [61] was not recognised in the database , and available sequences were classified as Crenarchaeota . While members of the Thaumarchaeota are present in our data , none carry annotated snoRNAs , so not explicitly recognizing putative Thaumarchaeotes as a phylum does not impact the results summarized in figure 2 . For Eukaryote RNA sequences , data was grouped according to the classification scheme proposed by Adl and colleagues [62] . All sequences annotated as THIC in Genbank were retrieved ( 8 Feb 2011 ) . The resulting list of 4508 sequences were examined for sequence similarity by generating a blast network using the blastall program from the BLAST package ( version 2 . 2 . 18 ) , with an E-value cutoff of 0 . 1 . The network of blast results was visualized with CLANS [63] , using default settings . The output was then clustered using MCL [64] , with granularity set at 4 . Representative sequences spanning all domains were retrieved from all MCL clusters with >10 members . Sequences were aligned using MSA-Probs [65] . Partial sequences and extremely divergent sequences where annotation appeared questionable were removed . Conserved regions were selected for use in phylogenetic analysis via the G-blocks server [66] ( http://molevol . cmima . csic . es/castresana/Gblocks_server . html ) , with the settings ‘Allow smaller final blocks’ and ‘Allow gap positions within the final blocks’ selected . ProtTest [67] was used to identify the best-fit model of protein evolution for our alignment . Phylogenetic analysis was performed using PhyML 3 . 0 [68] with parameters and model ( WAG+I+G ) as selected using ProtTest . Bootstrapping was performed on two Mac Pro machines with Intel Xeon Quad core processors , running 12 parallel threads . Parallelization yielded a total of 108 bootstrap replicates ( a consequence of running 12 threads in parallel , resulting in bootstrap replicates that were a multiple of 12 ) ; all bootstrap values in figure S1 are therefore out of a total of 108 not 100 . Additional trees were generated using RAxML [69] and BioNJ [70] to assess robustness of the topology . Tree figures were generated in Dendroscope [71] . | In cells , DNA carries recipes for making proteins , and proteins perform chemical reactions , including replication of DNA . This interdependency raises questions for early evolution , since one molecule seemingly cannot exist without the other . A resolution to this problem is the RNA world , where RNA is postulated to have been both genetic material and primary catalyst . While artificially selected catalytic RNAs strengthen the chemical plausibility of an RNA world , a biological prediction is that some RNAs should date back to this period . In this study , we ask to what degree RNAs in extant organisms trace back to the common ancestor of cellular life . Using the Rfam RNA families database , we systematically screened genomes spanning the three domains of life ( Archaea , Bacteria , Eukarya ) for RNA genes , and examined how far back in evolution known RNA families can be traced . We find that 99% of RNA families are restricted to a single domain . Limited conservation within domains implies ongoing emergence of RNA functions during evolution . Of the remaining 1% , half show evidence of horizontal transfer ( movement of genes between organisms ) , and half show an evolutionary history consistent with an RNA world . The oldest RNAs are primarily associated with protein synthesis and export . | [
"Abstract",
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] | 2012 | Comparative Analysis of RNA Families Reveals Distinct Repertoires for Each Domain of Life |
The recent epidemic of the arthritogenic alphavirus , chikungunya virus ( CHIKV ) has prompted a quest to understand the correlates of protection against virus and disease in order to inform development of new interventions . Herein we highlight the propensity of CHIKV infections to persist long term , both as persistent , steady-state , viraemias in multiple B cell deficient mouse strains , and as persistent RNA ( including negative-strand RNA ) in wild-type mice . The knockout mouse studies provided evidence for a role for T cells ( but not NK cells ) in viraemia suppression , and confirmed the role of T cells in arthritis promotion , with vaccine-induced T cells also shown to be arthritogenic in the absence of antibody responses . However , MHC class II-restricted T cells were not required for production of anti-viral IgG2c responses post CHIKV infection . The anti-viral cytokines , TNF and IFNγ , were persistently elevated in persistently infected B and T cell deficient mice , with adoptive transfer of anti-CHIKV antibodies unable to clear permanently the viraemia from these , or B cell deficient , mice . The NOD background increased viraemia and promoted arthritis , with B , T and NK deficient NOD mice showing high-levels of persistent viraemia and ultimately succumbing to encephalitic disease . In wild-type mice persistent CHIKV RNA and negative strand RNA ( detected for up to 100 days post infection ) was associated with persistence of cellular infiltrates , CHIKV antigen and stimulation of IFNα/β and T cell responses . These studies highlight that , secondary to antibodies , several factors are involved in virus control , and suggest that chronic arthritic disease is a consequence of persistent , replicating and transcriptionally active CHIKV RNA .
The arthritogenic alphaviruses comprise a group of globally distributed , mosquito-borne , single-stranded positive-sense RNA viruses that cause sporadic outbreaks of predominantly rheumatic disease . They include the predominantly Afro-Asian chikungunya virus ( CHIKV ) , the primarily Australian Ross River and Barmah Forest viruses , the African o'nyong-nyong virus , the Sindbis group of viruses and the South American Mayaro virus . Symptomatic infection of adults with these alphaviruses is nearly always associated with rheumatic disease , primarily polyarthralgia and/or polyarthritis . The arthopathy can be chronic and debilitating and usually lasts weeks to months , occasionally longer [1] . The largest documented outbreak of CHIKV disease ever recorded began in 2004 , resulting in an estimated 1 . 4–6 . 5 million cases , mainly in Africa and Asia . Imported cases were reported in nearly 40 countries including Europe , Japan and the USA [1] , [2] . The outbreak continues in 2013/2014 with thousands of cases in Papua New Guinea [3] and the Caribbean [4] , [5] . At present , no licensed vaccine or particularly effective drug is available for human use for any alphavirus , although analgesics and non-steroidal anti-inflammatory drugs can provide relief from symptoms [1] , [6] . Alphavirus infections in vivo result in a brief , usually 5–7 day viraemia , which is primarily controlled by IFNα/β initially , and subsequently by anti-viral antibodies . Infection of genetically modified mice defective in IFNα/β responses have illustrated that a rapid early induction of IFNα/β is required to control the acute viraemia and protect against mortality [7] , [8] , [9] , [10] . Antibodies are also well recognized as mediating protection , with anti-viral antibodies [11] , [12] , [13] , [14] and antibody-based vaccines [15] , [16] , [17] , [18] being developed as potential prophylactic interventions . An important role for CD4 T cells in driving CHIKV arthritis was recently established [19] , [20] . However , the role of T cells in controlling alphaviral viraemia remains controversial with recent reports suggesting they have no role [20] , [21] , whilst early literature described a role for T cells in cross protection between different alphaviruses [22] , [23] , [24] . NK cells appear to have a protective role for alphaviral infections in some settings [25] , but not others [26] , with NK cells also implicated in arthritic disease [27] , [28] . Alphaviruses have a well recognized propensity to establish persistent infections in vitro [29] , [30] , [31] , [32] , [33] and in vivo [34] , [35] , [36] , [37] , with such persistence in joint tissues likely responsible for chronic arthritic disease [38] , [39] , [40] . How such post-viraemia persistence is achieved in the face of robust anti-viral antibody and T cell responses remains a matter of considerable speculation [32] , [41] , [42] , [43] , [44] , [45] , [46] . Antibodies and T cell IFNγ are believed to be involved in the ultimate clearance of persistent Sindbis virus from neurons [47] . However , knowledge regarding the nature of persistent arthritogenic alphavirus infections , and the inflammatory responses stimulated by them , currently remains limited [38] , [40] . We recently developed an adult C57BL/6 ( wild-type ) mouse model of CHIKV infection and arthritis that mimics many aspects of human disease [48] . Herein we use this infection model in a series of genetically modified mouse strains deficient in one or more immune responses to explore the contribution of B , T and NK cells and the non-obese diabetic ( NOD ) background to ( i ) protection against CHIKV viraemia and ( ii ) promotion of arthritic disease . We also show , consistent with human and monkey data [38] , [40] , that in C57BL/6 mice , CHIKV RNA and protein persists for extended periods and continues to stimulate innate and adaptive immune responses .
The mice strains used in this study were: ( i ) NRG ( B , T and NK cell deficient on a NOD background ) , NOD . Cg-Rag1tm1Mom Il2rgtm1Wjl/SzJ , NOD-congenic mice harboring the Rag1null mutation and the IL2rγnull mutation ( JAX ) ; ( ii ) NOD , NOD/ShiLtJ ( non-obese diabetic mouse ) ( JAX ) ; ( iii ) Rag2/Il2rg ( B , T and NK cell deficient on a B6 background ) , B10; B6-Rag2tm1Fwa Il2rgtm1Wjl ( Taconic , Hudson , NY ) , ( iv ) Rag1−/− ( B and T cell deficient on a C57BL/6 background ) , B6 . 129S7-Rag1tm1Mom/J ( JAX ) ; ( v ) µMT ( B cell deficient on a C57BL/6 background , no expression of membrane-bound IgM ) , B6 . 129S2-Igh-6tm1Cgn/J ( JAX ) ; ( vi ) MHCIIΔ/Δ ( CD4 T cell deficient , no class II MHC on a C57BL/6 background ) [49]; ( vii ) FcγR−/− mice ( Fc gamma receptor deficient on a C57BL/6 background ) , B6 . 129P2-Fcer1gtm1Rav N12 ( Taconic ) . All strains ( except FcγR−/− ) were bred at the QIMR Berghofer animal house facility . C57BL/6 mice were purchased from Animal Resources Center ( Canning Vale , WA , Australia ) . All animals were handled in accordance with good animal practice as defined by the National Health and Medical Research Council of Australia . All experiments were approved by the QIMR Berghofer animal ethics committee ( P1060 A0705-603M ) . The Reunion Island isolate ( LR2006-OPY1 ) of CHIKV is a primary isolate obtained from the recent outbreak in Reunion Island and was grown in C6/36 cells , inoculated into mice , and serum viraemia determined as described previously using a modified CPE-based assay on Vero cells [8] , [48] . Female mice were used with an age range of 6–12 weeks ( mean age of each group was 8–10 . 5 weeks ) ; we have not observed significant differences in foot swelling for mice within this age range using this model ( Table S1 in Text S1 ) . Mice were inoculated with 104 CCID50 of virus subcutaneously ( s . c . ) into the dorsal side of both hind feet , toward the ankle . Blood was collected from the tail vein into MiniCollect tubes ( Greiner Bio-One GmbH , Kremsmunster , Austria ) and viral titers expressed as log10 50% cell culture infectivity dose ( CCID50 ) ( method of Spearman and Kaber ) . Foot swelling was measured using digital Vernier calipers and is presented as a group average of the percentage increase in foot height times width for each foot compared with the same foot on day 0 ( i . e . n = 12 feet means n = 6 mice unless stated otherwise ) . Serum cytokine protein levels were analyzed using the BD Cytometric Bead Array Bioanalyzer system ( Becton Dickinson , Franklin Lakes , NJ ) and IFNα levels were determined by Mouse IFN-alpha FlowCytomix Simplex ( eBioscience , San Diego , CA , USA ) according to the manufacturer's instructions . Mice were vaccinated s . c . with 10 µg of inactivated CHIKV as described [48] . Standard proliferation assays using tritiated thymidine uptake were undertaken using splenocytes isolated 3 weeks post vaccination . Briefly , splenocytes ( 2 . 5×105 cells/96 well , 6 replicates ) were cultured with 10 µg/ml of inactivated CHIKV [48] for 3 days , tritiated thymidine was then added and cells harvested the next day onto a MicroBeta Filtermat-96 A using the FilterMate™ Cell Harvester ( PerkinElmer ) . Radioactivity was measured using the MicroBeta Liquid Scintillation Counter ( PerkinElmer ) . Anti-CHIKV IgG2c and IgG1 antibody titers were determined by standard isotype-specific ELISA using ELISA plates coated with inactivated CHIKV as described [15] . Anti-CHIKV anti-serum was generated by infecting C57BL/6 mice with CHIKV and after 10 weeks vaccinating them with 10 µg of inactivated CHIKV [48] . Serum was harvested after 2 weeks and had an end point neutralization titer of 1/2560 determined as described [15] . Tissues were fixed in 10% neutral buffered formalin , feet were decalcified ( 15% EDTA in 0 . 1% phosphate buffer over 10 days ) , tissue was embedded in paraffin wax , and 6 µm-thick sections were cut and stained with hematoxylin-eosin . Sections were digitally scanned using Scan Scope XT digital slide scanner ( Aperio , Vista , CA ) . Image analyses were undertaken using Aperio ImageScope Software ( v10 ) and the Positive Pixel Count v9 algorithm ( default settings ) . Quantitative real time RT-PCR was undertaken as described [48] . Briefly , feet and spleen were stored in RNAlater solution ( Ambion , Austin , TX , USA ) , placed in TRIzol ( Life Technologies , Carlsbad , CA , USA ) and homogenization using steel balls and TissueLyser ( Qiagen ) at 25 Hertz for 6 min . on ice . cDNA was then generated using Superscript III ( Invitrogen ) and random hexamer oligonucleotides . Real-time PCR analysis used the following primers ( 5′ to 3′ ) : CHIKV E1 F AGCTCCGCGTCCTTTACC , R CAAATTGTCCTGGTCTTCCTG; ISG54 F CTCTCTGGAGCAAGCCATTC , R GCCATTGCTTGGTTTTTATG . Quantitative real-time PCR ( qRT-PCR ) was performed in a reaction consisting of 1 µl of cDNA , 10 µl of SYBR green Super mix-UDG ( Invitrogen ) , 1 µl BSA , 6 µl H2O , and 1 µl of 10 µM of forward and reverse primers . cDNA was amplified and PCR products were detected using Rotorgene 6000 ( Corbett Research , Mortlake , Australia ) under the following cycling conditions: one cycle of 50°C for 2 min , one cycle of 95°C for 2 min , 45 cycles of 94°C for 5 sec , 60°C for 10 sec and 72°C for 30 sec . Data were analyzed using Rotor-Gene Real Time Analysis software ( Corbett Research , Australia ) . Each sample was analyzed in duplicate and normalized to RPL13A mRNA as described [48] . Negative-strand specific qRT PCR was undertaken essentially as described [50] . cDNA was synthesized as described [48] with the exception that random hexamer oligonucleotides were substituted with 10 pg of a primer , that comprised a tag sequence linked to a CHIKV nsP1 sequence ( 5′-GGCAGTATCGTGAATTCGATGCGACACGGAGACGCCAACATT-3′; tag sequence in italics ) . qRT PCR used a forward primer with the tag sequence ( 5′-AATAAATCATAAGGCAGTATCGTGAATTCGATGC-3′ ) and a reverse primer from nsP1 ( 5′-AATAAATCATAAGTCTGCTCTCTGTCTACATGA-3′ ) , with flap sequences ( underlined ) added to increase fluorescent signal strength [50] . Analyses were performed using IBM SPSS Statistics ( version 19 ) . The t test was used if the difference in the variances was <4 , skewness was >−2 , and kurtosis was <2 . Where the data was non-parametric and difference in variances was <4 , the Mann Whitney U test was used , if >4 the Kolmogorov-Smirnov test was used . Microarray studies were performed essentially as described [19] . RNA from feet taken day 0 was compared with RNA from feet taken day 30 post infection , two microarrays were undertaken for each time point . Probe sets that did not represent known genes were removed and only expressed genes with a mean log2 expression≥6 and variance >0 . 1 across all 4 samples were included . A t-test was performed to compare gene expression between day 0 and day 30 samples for the 4 , 805 remaining genes . Genes where p<0 . 05 were considered differentially expressed . Differentially expressed genes were analyzed using web-based Ingenuity pathway analysis ( IPA ) using canonical pathway analysis [19] and the upstream regulator function [51] .
The following mouse strains were infected with CHIKV and their viraemias were monitored over time; ( i ) C57BL/6 mice , ( ii ) non obese diabetic ( NOD ) mice , ( iii ) MHCIIΔ/Δ mice ( MHCII deficient mice on a C57BL/6 background ) , which lack of functional Th cells and thus no T cell help for B cells [49] , ( iv ) µMT mice ( B cell deficient on a C57BL/6 background ) , ( v ) Rag1−/− mice ( B and T deficient on a C57BL/6 background ) , ( vi ) Rag2/Il2rg mice ( B , T and NK cell deficient on a C57BL/6 background ) , ( vii ) NRG mice ( B , T and NK cell deficient on a NOD background ) . The mice and their characteristics are fully described in Table S2 in Text S1 . C57BL/6 , NOD and MHCIIΔ/Δ mice were able efficiently to control viraemia by day 5–7 ( Fig . 1 , C57BL/6 , NOD , MHCIIΔ/Δ ) ; these mouse strains all have B cells . The results for C57BL/6 and MHCIIΔ/Δ mice are consistent with previous reports [19] , [20] , [48] . The mean viraemia in MHCIIΔ/Δ mice was ≈2 . 5 logs higher than in C57BL/6 mice on day 4 ( p = 0 . 024 , Kolmogorov Smirnov test ) , ≈1 . 5 logs higher on day 5 ( not significant ) , and ≈0 . 5 logs higher on day 6 ( not significant ) , suggesting a slight delay in viraemia control in these mice ( see also below for antibody responses in these mice ) . In mouse strains lacking B cells , CHIKV viraemias peaked on days 2–3 , and then settled to relatively constant levels that were distinct in several mouse strains ( Fig . 1 , bottom 4 graphs; µMT−/ , Rag1−/− , Rag2/Il2rg and NRG ) . Persistent CHIKV infection in Rag1−/− and µMT−/− mice has been reported previously [36] , [37] . The leveling out of viraemias in these mice ( Fig . 1 ) is reminiscent of peripheral blood set-point viral loads described for HIV , with the set-point levels deemed to be a reflection of functional anti-viral immunity [52] . Applying this concept to the data presented herein ( Fig . 1 ) , the CHIKV set-point viraemias were determined by calculating the mean of all viraemia measurements taken on and after day 10 post infection ( Fig . 1 , values in bold +SD ) . The set-point viraemia levels were ( lowest to highest ) µMT<Rag1−/− = Rag2/Il2rg <NRG , with each “<” representing statistically significant differences ( Fig . 1 , p values ) . These results suggest that T cells contribute to suppression of viraemia as the set-point viraemia was ≈2 logs higher in Rag1−/− mice ( B and T cell deficient ) than in µMT mice ( B cell deficient ) ( Fig . 1 , Rag1−/− vs . µMT ) . NK cells do not appear to play a major role in viraemia control as the set-point viraemia in Rag1−/− mice ( B and T cell deficient ) and Rag2/Il2rg mice ( B , T and NK cell deficient ) was not significantly different ( Fig . 1 , Rag1−/− vs . Rag2/Il2rg ) . The NOD background ( in addition to B , T and NK cell deficiency ) further increased the set-point viraemia , with NRG mice showing a significant mean ≈0 . 8 log higher level than Rag2/Il2rg mice ( Fig . 1 , NRG vs . Rag2/Il2rg ) . The NOD background has defects in a number of innate immune activities that might be responsible for this difference [53] , [54] , [55] . MHCIIΔ/Δ mice are defective for T cell help in B cell IgG class switching and have a dearth of CD4+ Th cells [49] . Analysis of the antibody responses in these mice showed that following CHIKV infection , MHCIIΔ/Δ mice generated no anti-viral IgG1 responses , but did make anti-viral IgG2c responses , albeit at about ≈100 fold lower titers than C57BL/6 mice ( Fig . 2A ) . CD4−/− mice also show reduced anti-CHIKV IgG1 and IgG2c responses following CHIKV infection [37]; however , CD4-negative MHC II-restricted T cells in these mice retain immunoglobulin isotype class switching activity [56] . MHCII-restricted CD4 T cell-independent IgG2c production has been shown previously to be reliant on IFNα/β signaling in B cells [57] , with abundant IFNα/β production well described for CHIKV infections [8] . MHCII-restricted CD4 Th cells thus appear to be required for IgG1 and high titer IgG2c anti-viral responses after CHIKV infection . MHCIIΔ/Δ mice were able effectively to control the viraemia by day 5 ( Fig . 1 , MHCIIΔ/Δ ) . Whether this was due to IgM responses ( intact in MHCIIΔ/Δ mice ) or IgG2c responses remains unresolved , with IgG responses detected in mice using sensitive techniques as early as day 3 post viral infection [58] . The significant ≈2 log difference in set-point viraemia between Rag1−/− and µMT mice ( Fig . 1 ) suggested that T cells play a role in suppressing viraemia ( with this being clearly discernable when B cells are absent ) . CD8 T cells have been shown not to influence viraemia in a Ross River virus mouse model [32] and not to influence viraemia and disease in a CHIKV mouse model [20] , [32] , suggesting CD4 T cells are likely involved . To further investigate the role of T cells in viraemia control ( in the absence of the dominant role of antibodies ) , B cell deficient µMT mice were vaccinated with an inactivated ( non-adjuvanted ) CHIKV whole-virus vaccine . This vaccine was previously shown to provide complete protection against CHIKV viraemia and foot swelling ( arthritis ) in C57BL/6 mice [48] . Vaccinated µMT mice generated similar levels of CHIKV-specific T cell responses to C57BL/6 mice , as measured by standard proliferation assays using inactivated virus as antigen ( Fig . 2B ) . A parallel group of vaccinated and control ( PBS-vaccinated ) µMT mice were challenged with CHIKV . Vaccinated mice showed a significant ≈1 and ≈1 . 5 log lower viraemia on days 3 and 4 , respectively , compared with control µMT mice . This effect was lost at later time points ( Fig . 2C ) , by which time the control µMT mice would presumably have generated CHIKV-specific T cells in response to the infection . Given unadjuvanted , killed , whole-virus vaccines are generally poor at inducing CD8 T cells [59] and CD4 T cell recall responses usually peak around day 4 [60] , this experiment provides further support for an antibody-independent role of CD4 T cells in CHIKV viraemia suppression . Following challenge , the vaccinated µMT mice showed much earlier and higher foot swelling than unvaccinated µMT mice ( Fig . 2D , Control ) . This observation is consistent with the notion that CD4 T cells have an important immunopathological role in arthritis [19] , and highlights a potential risk if a vaccine were to induce T cell responses , but inadequate antibody responses . Adoptive transfer of anti-viral antibodies has been suggested as both prophylactic and therapeutic interventions for CHIKV [11] , [12] , [13] , [14] . To gain insights into how effective such treatments might be , Rag1−/− and µMT mice persistently infected for >480 days , were treated with mouse polyclonal anti-CHIKV anti-serum . The viraemia became undetectable for 10 and 30 days in Rag1−/− and µMT mice , respectively , but then reappeared thereafter to levels seen prior to antibody administration ( Fig . 3A ) . Passive transfer of antibody was thus unable to clear the virus permanently from these mice , an observation that is consistent with the inability of robust anti-viral humoral immunity to clear persistent virus and/or viral RNA from infected monkeys [38] and humans [40] . In these B cell deficient mice , the adoptive transfer of antibodies worked for only a limited period , consistent with the limited serum half-life of adoptively transferred antibodies [1] . To determine what cytokines might be implicated in limiting the viraemia in B and T cell deficient mice , serum cytokine levels were measured in persistently infected Rag1−/− mice . Although acute induction of serum IFNα was observed , levels did not remain elevated despite the ongoing viraemia ( Fig . 3B , IFNα ) . The well described tight control ( and thus transient ) production of IFNα/β [61] , [62] thus appeared to be largely retained in persistently viraemic Rag1−/− mice . In contrast to IFNα/β , serum IFNγ , TNF and IL-6 were persistently up-regulated in persistently infected Rag1−/− mice ( Fig . 3B , IFNγ , TNF , IL-6 ) , with IFNγ and TNF previously shown to have anti-alphaviral activities [63] , [64] . Elevated levels of CCL2/MCP-1 ( a chemokine with no antiviral activity against CHIKV [51] ) were also seen , peaking at ≈1000 pg/ml day 1 and settling to a constant level of 200+13 . 4 pg/ml after day 3 . No IL-12 was detected . The ability of alphaviruses to acquire mutations and better evade the antiviral effects of IFNα/β have been reported [29] , [65] , with CHIKV and other alphaviruses having evolved strategies to counter the host's type I and II interferon responses [66] , [67] . Virus isolated from Rag1−/− mice on 100 behaved no differently from parental virus ( with respect to viraemia and foot swelling ) when isolated from blood , expanded in C6/36 cells , and used to infect C57BL/6 mice ( S1 Figure A in S1 Text ) . Virus isolated from three Rag1−/− mice day 429 post infection also did not show consistent or significant viraemia differences from parental virus in C57BL/6 mice ( S1 Figure A in S1 Text ) . CHIKV thus appears unable to evade further ( via adaptive mutations ) the innate factors that maintain the viraemia at the set-point level in Rag1−/− mice . One might speculate that TNF [64] ( rather than IFNγ [67] ) plays a dominant role in viraemia suppression in these mice ( Fig . 3B ) . For CHIKV to evolve a capacity to counter the anti-viral effects of TNF may be unrealistic in the limited time frame . Deep sequencing of virus isolated day 100 from Rag1−/− mouse serum showed only a limited number of mutations ( S1 Figure B in S1 Text ) and a limited quasi-species diversity ( Fig . S1C in Text S1 ) ; perhaps surprising given the low fidelity of viral RNA replication [68] . Alphavirus isolation generally involves virus expansion in vitro ( in this case using C6/36 cells ) , which may bias the results [69]; however , many genetically diverse alphaviruses can be expanded on C6/36 cells . These results ( S1 Figure B , C , in S1 Text ) would therefore suggest that despite 100 days of continuous replication in Rag1−/− mice , a highly diverse infectious virion quasi-species population was not generated [70] . The mouse strains shown in Fig . 1 were also analyzed for foot swelling post CHIKV infection . Relative to C57BL/6 mice , NOD mice showed a clear increase in foot swelling ( Fig . 4 , NOD ) . Foot swelling in NOD mice was associated with profound cellular infiltrates and edema ( S2 Figure A in S1 Text ) . NOD mice have a range of immune defects that could contribute to exacerbated CHIKV arthritis ( see Discussion ) . MHC IIΔ/Δ and NRG mice show clearly reduced foot swelling when compared with C57BL/6 mice ( Fig . 4 , C57BL/6 , MHC IIΔ/Δ and NRG ) , consistent with previous data showing that CD4 T cells are important for driving CHIKV arthritis [19] , [20] . Curiously , Rag1−/− mice ( also T cell deficient ) showed no reduction in foot swelling compared with C57BL/6 mice . However , histological examination illustrated that this swelling in Rag1−/− mice was largely due to edema , both on day 3 and day 6 , with the density of cellular infiltrates actually lower in Rag1−/− mice than in C57BL/6 mice ( S2 Figure B in S1 Text ) ; an observation consistent with previous findings [36] . T cells would thus appear to be involved in the marked recruitment of inflammatory cells that characterizes arthritic disease in C57BL/6 mice [51] . Rag2/Il2rg mice showed less foot swelling than Rag1−/− mice on day 3 ( Fig . 4 , Rag2/Il2rg vs Rag1−/− ) , perhaps suggesting a role for NK cells in promoting edema ( a contention proposed previously [71] ) . CHIKV infections are well known to induce edema [72] . Foot swelling was significantly higher in µMT mice than C57BL/6 mice on days 4 , 5 , 7 , 8 , 9 and 11 ( Fig . 4 , µMT ) , consistent with a previous report using a different CHIKV isolate [37] . The density of the cellular infiltrates was similar in µMT and C57BL/6 mice ( S2 Figure in S1 Text ) , illustrating that the foot swelling in µMT mice was not simply due to edema . The increased arthritis from day 4 onwards in µMT mice ( which have T cells ) is consistent with the arthritogenic role of CHIKV-specific CD4 T cells [19] , [20] . Loss of viraemia control in B cell-deficient mice ( including µMT mice ) significantly diverged from C57BL/6 mice on day 4 post-infection ( Fig . S3A in Text S1 ) , consistent with the appearance of neutralizing antibodies on day 4 post-infection in C57BL/6 mice ( S3 Figure B in S1 Text ) . The increased viraemia from day 4 onwards in µMT mice presumably leads to the exacerbated arthritic disease . Despite the reported roles of Fc receptors in suppressing antiviral responses and promoting arboviral disease [73] , [74] , [75] , foot swelling and viraemia was largely unaffected in mice deficient for the common gamma chain of the Fc receptor ( FcRγ ) ( S4 Figure in S1 Text ) . The persistent viraemias in the B cell deficient mouse strains ( with the exception of NRG mice - see below ) resulted in surprisingly little overt pathology with mice appearing and behaving normally based on regular monitoring by trained animal house staff . To investigate further the pathological effects of a persistent alphaviral infection , the liver , lungs , brain , spleen , lymph nodes , muscle , skin , and feet of Rag1−/− mice chronically infected for 430 days were examined by histology . The only clear histopathological modifications associated with infection were a marked increase in granulocytosis and granulopoiesis in the spleen ( S5 Fig in S1 Text ) , a feature previously associated with infection and inflammation [76] . Persistent alphaviral replication in Rag1−/− mice was thus associated with surprisingly little pathology identifiable by standard histology . A previous study using young Rag1−/− mice , showed persistent infection and mild persistent joint pathology [36] , perhaps consistent with increased disease associated with alphavirus infection of young mice [10] , [77] , [78] . In NRG mice the chronic CHIKV viraemia was eventually associated with morbidity and mortality . NRG mice often showed altered gait and balance , and impaired hind foot limb movement , with animals requiring euthanasia between days 120 and 230 ( S6 Figure in S1 Text ) . These signs and symptoms are suggestive of neurological disease [79] , [80] , [81] . Histological examination of brain tissue from euthanized mice showed clear signs of on-going inflammation in the central nervous system , with severe vacuolization and edema , astrocytosis , microgliosis , and mild degeneration of neurons evident ( S7 Figure A in S1 Text ) . Immunohistochemistry with an anti-capsid antibody showed that both neurons and oligodendrocytes were infected ( S7 Fig B in S1 Text ) . Infection of these cells has been reported previously for the encephalopathies caused by CHIKV [82] and other alphaviruses [81] . A number of reports have suggested that virus and/or viral RNA of CHIKV and other alphaviruses persists in vivo long after the viraemia has abated [20] , [36] , [38] , [40] , [66] , [83] . Persistence of CHIKV RNA was also seen in our C57BL/6 mouse model , with significant levels of CHIKV RNA detected by standard qRT PCR for 100 days post infection in feet ( Fig . 5A ) ; this method measures the levels of both positive-strand and negative-strand CHIKV RNA . Significant levels of negative-strand RNA , detected by strand-specific qRT PCR [50] , were also seen over the same period ( Fig . 5B ) . qRT-PCR analyses of spleens revealed that no significant levels of CHIKV RNA were detectable from day 14 ( S8 Fig A in S1 Text ) , consistent with a published report using CHIKV infection of young mice [84] . The time course of viral RNA and negative-stranded RNA levels in feet showed a rapid decline from the peak viraemia ( day 3 ) to the end of the viraemic period ( day 6 ) ( Fig . 5A , B; Fig . S8B in Text S1 ) , a drop likely largely mediated by anti-CHIKV antibodies inhibiting viral infection ( S3 Figure A , B , in S1 Text ) . Thereafter CHIKV RNA levels in the feet fell more slowly ( Fig . 5A , B; S8 Figure B in S1 Text ) , with curve fitting from day 14 onwards suggestive of an exponential decay with a half life of ≈10–11 days for both RNA and negative-strand RNA levels ( S8 Figure B in S1 Text ) . Significant levels of mRNA of the IFNα/β stimulated gene , ISG54 [85] , were detected for up to 60 days post infection ( Fig . 5C ) , suggesting ongoing stimulation of IFNα/β responses by persistent CHIKV RNA . Immunohistochemistry using a new monoclonal antibody that recognizes the capsid protein of CHIKV ( Goh et al . submitted ) , also indicated the presence of capsid-positive cells in foot tissues on day 30 post infection ( S9 Figure in S1 Text ) , suggesting the persistent RNA is translationally active . Although foot swelling was no longer detectable after day 10–12 in C57BL/6 mice , histological examination of feet illustrated the presence of small foci of inflammatory infiltrates; examples of such lesions in muscle and synovial membrane are shown ( Fig . 5D ) . Quantitation using Aperio Positive Pixel Count analyses of whole foot sections confirmed that significantly elevated levels of these infiltrates could be detected up to day 45 post infection ( Fig . 5E ) . This mouse model of CHIKV infection thus recapitulates the persistence of viral RNA and protein seen in monkeys and humans [38] , [40] , and supports the view that such persistence gives rise to chronic inflammatory arthropathy [39] , [86] . To gain insights into the chronic inflammatory signature in C57BL/6 mice , a microarray analysis was undertaken as described previously [19] using feet from C57BL/6 mice at day 0 and 30 post-infection; ( a principal component analysis is shown in S10 Figure A in S1 Text ) . The fold changes in gene expression on day 30 ( relative to day 0 ) were generally much lower ( range 1 . 44–12 . 21 fold , S3 Table in S1 Text ) than the changes seen during peak disease ( day 7 ) [19] , likely due to the >2 logs lower levels of CHIKV in the feet at this time ( Fig . 5A , B ) . Nevertheless , 192 significantly up-regulated genes were identified ( S3 Table S3 in S1 Text ) ; ( a heat map of these genes is shown in S10 Figure B in S1 Text ) . Differential expression of two genes ( in addition to ISG54 , Fig . 5C ) was also demonstrated by qRT-PCR ( S10 Figure C in S1 Text ) . Ingenuity Pathway Analysis of the 192 genes suggested activation of canonical pathways associated with T cells , autoimmunity , antigen presentation , NK cells , innate sensing ( primarily IFNα/β pathways ) , monocytes/macrophages , apoptosis and cytokines ( S11 Figure in S1 Text ) . These pathway groupings were broadly similar to those described for day 7 post infection [19] , suggesting acute and chronic arthritis share many inflammatory processes . The same 192 up-regulated genes were analyzed using Ingenuity Pathway Analysis of upstream regulators ( Table 1 , S4 Table in S1 Text ) . This analysis provided evidence for stimulation of pathways involved in type I IFN responses and supports the view that persistent CHIKV RNA continues to stimulate these responses; it is also consistent with Fig . 5C and the up-regulation of IFNα in joints of chronic CHIKV patients [40] . Poly ( ADP-ribose ) polymerase-1 ( PARP-1 ) and IL-6 were identified as upstream regulators ( Table 1 ) , with these also up-regulated in the synovial tissues of a chronic CHIKV patient [40] . Cleavage of PARP-1 is associated with CHIKV-induced apoptosis [87] and up-regulation of serum IL-6 has been associated with chronic CHIKV disease [88] . Upstream regulators associated with T cells and Th1 responses ( IFNγ and IL-12 ) were identified ( Table 1 ) , with such responses again seen in chronic CHIKV patients [40] , [46] . These results suggest that the mouse model used herein recapitulates many of the chronic inflammatory pathways seen in humans . STAT3 was also identified ( Table 1 ) , with this transcription factor associated with M2 macrophage differentiation [89] , [90]; M2 differentiation was recently shown to be associated with CHIKV persistence [91] . IRGM ( Table 1 ) is an autophagy-associated protein targeted by CHIKV-NS2 and E3 proteins [92] , with viruses believed to manipulate autophagy to promote their own replication [93] .
Herein we show that mice deficient in B cells maintain persistent , relatively stable “set point” viraemias reminiscent of those seen in HIV patients [52] . These experiments suggest that T cells play a role ( albeit secondary to IFNα/β and antibodies ) in suppressing viraemia , with the set-point viraemia ≈2 logs higher in Rag1−/− mice ( B and T cell deficient ) than in µMT mice ( B cell deficient ) . Vaccination studies in µMT mice further support a role for T cells in CHIKV viraemia suppression ( Fig . 2 B , C ) . CD4 T cells ( rather than CD8 T cells ) are implicated in this anti-viral activity [20] , [32] , [59] , with direct antiviral roles for CD4 T cells also described for other viruses [94] , including encephalitic alphaviruses [95] . Cytokine analysis in Rag1−/− mice showed persistently elevated levels of circulating IFNγ and TNF , with both of these cytokines known to have anti-alphaviral activity [63] , [64] . Although neutralization of TNF has been shown to be lethal in the related Ross River virus mouse model [64] , CHIKV infection of IFNγ −/− mice did not show any significant increases in viraemia when compared with C57BL/6 mice in this mouse model [19] , ( although slight increases were observed by others using a slight different model and assay system [20] ) . As both antibody [96] and IFNα/β responses remain active in IFNγ−/− mice , the contribution of IFNγ may be hard to dissect in this setting . The ≈0 . 8 log higher set point viraemia in T , B and NK deficient mice with a NOD background compared with T , B and NK deficient mice on a C57BL/6 background ( Fig . 1 , NRG vs . Rag2/Il2rg ) , suggests that further innate factors are involved in viraemia suppression . The NOD background has a number of defects in innate immunity that might be involved including ( i ) altered apoptosis [54] , IFNγ signaling [53] and/or IL-1β production [97] , [98] in macrophages , ( ii ) NKT cell deficiencies [99] , and/or ( iii ) lack of C5 complement activity [55] , [100] . Given that NK cells do not appear to be involved in viraemia control , the NK defects in NOD mice [101] are presumably not involved . Viraemia levels were not a good predictor of arthritic disease , consistent with human studies [102] . Instead , arthritic disease was associated with the presence of T cells , consistent with the arthritogenic role of CD4 T cells in CHIKV infections [19] , [20] . Vaccination experiments in µMT mice also highlighted a potential problem if vaccines were to induce CD4 T cell responses , but inadequate B cell responses , with such a scenario resulting in exacerbated arthritic disease post infection ( Fig . 2D ) . T cell driven pathology may also contribute to the severe disease seen in neonates born to CHIKV infected mothers ( see references in [1] ) , as maternal antibodies are well known to inhibit the offspring's own antibody production , whilst allowing generation of T cell responses [103] . The increase in arthritis seen in NOD mice ( that have intact T , B and NK cells ) when compared with C57BL/6 mice ( Fig . 4 ) may involve the NOD defects listed above and/or other defects [104] , [105] , although complement defects might be expected to ameliorate disease [100] , [106] . Increased viral replication ( as suggested by increased viraemia in NRG mice - Fig . 1 ) in some key cell types may also play a role in exacerbating arthritis . A role for autoimmune T cells is improbable as there is no evidence that such cells are responsible for alphaviral arthritides [39] , with self-reactive diabetogenic T cells in NOD mice restricted to a subset of T cells that recognize a specific insulin epitope [107] . The mouse strains lacking B cell responses developed surprisingly little pathology , despite the persistent viraemias , suggesting that the cytopathic alphaviral infections , in themselves , are generally not major drivers of disease . However , this contention likely does not hold true in NRG mice , which show relatively higher levels of persistent viraemia and ultimately develop encephalitis , with CHIKV infection of neurons and oligodendrocytes evident . Infection and killing of neurons is believed to be responsible for encephalopathy in the Sindbis virus mouse model [80] and may also play a role in CHIKV encephalitis [82] , [108] , [109] . In contrast to the alphaviral encephalitis induced by Semliki Forest virus [81] , conventional T cells are unlikely to be involved in NRG mice ( as these mice are defective for Rag1 activity ) . The high viraemias in NRG mice may promote the encephalopathy , as high CHIKV viraemias have been associated with lethal encephalitis ( i ) in mice deficient for the IFNα/β receptor [10] and ( ii ) in monkeys inoculated with high levels of CHIKV [38] . However , CHIKV encephalitis in humans and primates generally occurs during acute disease and near the peak viraemia [38] , [110] , [111] , rather than eventually arising from an extended viraemia . The C57BL/6 adult mouse model used herein recapitulates ( i ) the persistence of CHIKV RNA and protein seen in humans and monkeys and ( ii ) many of the persistent inflammatory responses seen in humans with chronic CHIKV arthropathy . This mouse model might thus be viewed as a model of both acute [48] and chronic CHIKV disease . The nature of persistent CHIKV RNA and protein remains poorly understood . A key question is whether such persistence simply represents material left over after active replication of virus in tissues , or alternatively , involves ongoing replication of virus or viral RNA [67] , [112] . Notwithstanding the propensity of alphaviruses to maintain persistent infections in vivo ( Fig . 1 ) and the aforementioned human and monkey studies [38] , [40] , several lines of evidence presented herein support the view that the persistent CHIKV RNA is replicating: ( i ) the relatively long , 10–11 day half-life of CHIKV RNA ( S8 Figure B in S1 Text ) compared with the reported 3–10 hour half-life of cellular Sindbis virus RNA [113] , [114]; ( ii ) the presence of CHIKV negative-strand RNA [50] ( packaged virions only containing positive-strand RNA [115] ) ; ( iii ) ongoing induction of double-stranded RNA , TLR3 and IFNα/β-inducible ISG54 [85] , [116] and ( iv ) the ability to detect CHIKV structural proteins on day 30 post-infection , with capsid synthesis requiring generation of subgenomic positive-strand RNA ( from negative strand RNA ) by viral RNA-dependent RNA polymerase [115] . Although CHIKV RNA appears to persist , we have been unable to isolate infectious virus from C57BL/6 mice beyond day 14 using a number of sensitive techniques ( S5 Table in S1 Text ) . This observation is consistent with the inability to isolate infectious virus from patients with chronic alphaviral disease , despite the presence of persistent alphaviral RNA [40] , [117] , [118] . The microarray study suggests chronic inflammatory disease is similar in mice and humans [40] , [46] , [88] , with IFNα/β , T cells , IL-12 , IFNγ and IL-6 continuing to be stimulated long after the end of the viraemic period . Such responses are likely involved in ongoing arthritic inflammation and chronic disease [40] , [88] . However , whether they also ultimately help to clear persistent CHIKV RNA/protein is unclear; clearance of persistent Sindbis virus from neurons is thought to involve antibodies and T cell IFNγ [47] . Aged monkeys with reduced T cell responses also showed increased viral persistence [119]; however , T cell responses were not different in recovered compared with chronic CHIKV patients [46] . Persistent CHIKV RNA is believed to reside in macrophages [38] , [40] , with tissue-resident rather than inflammation-recruited macrophages recently implicated [51] . Perhaps noteworthy is that the estimated ≈10-11 day half-life of persistent RNA ( Fig . S8B in Text S1 ) is nominally remarkably close to the natural turnover rate of tissue macrophages , estimated to be 21–27 days for total replacement [120] , [121] . Further work is clearly needed to understand how viral RNA persists , and to differentiate between those immune responses required for viral clearance and those driving chronic arthropathy . | The largest epidemic ever recorded for chikungunya virus ( CHIKV ) started in 2004 in Africa , then spread across Asia and recently caused tens of thousands of cases in Papua New Guinea and the Caribbean . This mosquito-borne alphavirus primarily causes an often debilitating , acute and chronic polyarthritis/polyarthalgia . Despite robust anti-viral immune responses CHIKV is able to persist , with such persistence poorly understood and the likely cause of chronic disease . Herein we highlight the propensity of CHIKV to persist long term , both as a persistent viraemia in different B cell deficient mouse strains , but also as persistent viral RNA in wild-type mice . These studies suggest that , aside from antibodies , other immune factors , such as CD4 T cells and TNF , are active in viraemia control . The work also supports the notion that CHIKV disease , with the exception of encephalitis , is largely an immunopathology . Persistent CHIKV RNA in wild-type mice continues to stimulate type I interferon and T cell responses , with this model of chronic disease recapitulating many of the features seen in chronic CHIKV patients . | [
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] | 2014 | Multiple Immune Factors Are Involved in Controlling Acute and Chronic Chikungunya Virus Infection |
Kinesin stepping is thought to involve both concerted conformational changes and diffusive movement , but the relative roles played by these two processes are not clear . The neck linker docking model is widely accepted in the field , but the remainder of the step – diffusion of the tethered head to the next binding site – is often assumed to occur rapidly with little mechanical resistance . Here , we investigate the effect of tethering by the neck linker on the diffusive movement of the kinesin head , and focus on the predicted behavior of motors with naturally or artificially extended neck linker domains . The kinesin chemomechanical cycle was modeled using a discrete-state Markov chain to describe chemical transitions . Brownian dynamics were used to model the tethered diffusion of the free head , incorporating resistive forces from the neck linker and a position-dependent microtubule binding rate . The Brownian dynamics and chemomechanical cycle were coupled to model processive runs consisting of many 8 nm steps . Three mechanical models of the neck linker were investigated: Constant Stiffness ( a simple spring ) , Increasing Stiffness ( analogous to a Worm-Like Chain ) , and Reflecting ( negligible stiffness up to a limiting contour length ) . Motor velocities and run lengths from simulated paths were compared to experimental results from Kinesin-1 and a mutant containing an extended neck linker domain . When tethered by an increasingly stiff spring , the head is predicted to spend an unrealistically short amount of time within the binding zone , and extending the neck is predicted to increase both the velocity and processivity , contrary to experiments . These results suggest that the Worm-Like Chain is not an adequate model for the flexible neck linker domain . The model can be reconciled with experimental data if the neck linker is either much more compliant or much stiffer than generally assumed , or if weak kinesin-microtubule interactions stabilize the diffusing head near its binding site .
Motor proteins in the kinesin superfamily are molecular machines that use the energy derived from ATP hydrolysis to transport organelles and other cellular cargo along microtubules . The 14 kinesin families are structurally diverse and display differences in motor velocity , directionality , and processivity that relate to their various cellular functions [1] , [2] . Kinesin-1 , ( conventional kinesin ) , contains two 110 kDa heavy chains that consist of the N-terminal motor head , the flexible neck linker domain , the coiled-coil stalk , and the C-terminal cargo-binding tail . The primary cellular function of Kinesin-1 is the long distance transport of vesicles and organelles in neurons . Kinesin-1 is a processive motor , meaning it takes many steps of roughly 8 nm along the microtubule without detaching . This processive behavior requires coordination between the chemomechanical cycles of the two heads , such that at least one motor head remains attached to the microtubule at any given point in the cycle [3] , [4] . The Kinesin-1 neck linker , a 14 amino acid domain that connects the globular motor head to the coiled-coil dimerization domain , has been the subject of intense experimental and theoretical investigations . This neck linker domain is thought to transition from a conformationally flexible unstructured state to a structured and docked state upon ATP binding , providing the principal conformational change in the motor [5] , [6] . This neck linker docking provides a forward ( plus-ended ) bias to the motor and enables the free tethered head to diffuse to the next binding site approximately 8 nm away . Importantly , during this diffusive search , the neck linker serves as a tether that constrains the search of the motor head for the next microtubule binding site and ensures that that lateral or backward steps are exceedingly rare [7] , [8] . Furthermore , when both heads are simultaneously bound to the microtubule , the neck linker needs to be sufficiently stiff that mechanical forces can be transmitted between the head domains to enable mechanochemical coordination between the two head domains [4] , [9]–[11] . Hence , there are two competing design pressures – the neck linker must be sufficiently extensible to enable diffusional search of the tethered head for its next binding site , but it must be sufficiently stiff to transmit forces between the heads when both heads are bound to the microtubule . To understand the dynamics of tethered diffusion in the kinesin walking mechanism , we have created a model of kinesin stepping that incorporates both chemical kinetics of the kinesin hydrolysis cycle and Brownian dynamics to represent the diffusion of the free motor head tethered by its flexible neck linker segment . The diffusion of the free head is modeled using a position-dependent stochastic differential equation where the drift ( i . e . potential ) is determined by the current chemical state of the motor , similar to a Brownian or flashing ratchet [12] . The mechanical properties of the neck linker domain play a central role in determining the diffusional characteristics of the free motor head , but its small size complicates experimental characterization . Hence , we have chosen to keep the diffusional model intentionally simple so as to minimize the number of assumptions and have used the model to test different mechanical representations of the flexible neck linker domain . Hyeon and Onuchic previously used a computational approach based on crystal structures of kinesin and tubulin to explore the dynamics of tethered head binding to the microtubule , but they did not explicitly investigate the role that neck linker mechanics play in this diffusive search [13] . The Brownian dynamics approach used here is similar to that of Atzberger et al . [14] , with the difference that we have focused on a one dimensional model to highlight the role of different models for the neck linker and have expanded the chemical hydrolysis cycle to better account for the current state of the field . The kinetic model for the Kinesin-1 hydrolysis cycle that underlies this work is presented in Figure 1 . This model is built on a large body of kinesin biophysical and biochemical studies [3] , [4] , [6] , [15] , [16] and was recently used to investigate differences between Kinesin-1 and Kinesin-2 motors [17] , [18] . In the model the motor starts in State 2 with one head bound and the tethered head freely diffusing and able to bind to either the next binding site on the microtubule or its previous binding site . ATP binding causes ordering of the neck linker domain and displacement of the tethered head toward the plus-end of the microtubule ( State 3 ) . Following ATP hydrolysis ( State 4 ) , the tethered head diffusively searches for the next binding site and binds there ( State 1 ) or , if this attachment is too slow the bound head releases from the microtubule ( State 5 ) , terminating the run . By incorporating rate constants into a standard Markov stepping model , this model was able to reproduce Kinesin-1 velocity and processivity characteristics across a range of ATP concentrations [9] , [17] . A principal motivation of the present study is to understand how extending the kinesin neck linker alters kinesin stepping behavior . The consensus from structural studies is that for Kinesin-1 to take an 8 nm step , the neck linker must extend a distance approaching its full contour length [10] , [19] , [20] . Interestingly , sequence analysis suggests that diverse kinesins that carry out quite different transport functions in cells and have considerably different motor properties from Kinesin-1 possess longer neck linkers [21] . We recently showed that Kinesin-2 motors , which have a 3 amino acid insertion in their neck linker are less processive than Kinesin-1 motors [17] . We then went on to show that extending the 14 amino acid Kinesin-1 neck linker decreases motor processivity considerably and shortening the 17 amino acid Kinesin-2 neck linker enhances processivity , while motor velocity is only weakly correlated with neck linker length [18] . These results are essentially consistent with recent studies from three other labs , with discrepancies largely accounted for by differences in experimental methodology [8] , [22] , [23] . While it is clear that extending the neck linker reduces motor processivity , what is not clear is which step or steps in the kinesin chemomechanical cycle are altered . As can be seen in Figure 1 , the probability that a motor detaches during each step is controlled by a race between binding of the tethered head to the next binding site ( State 4 to State 1 transition , kattach ) versus unbinding of the bound head ( State 4 to State 5 transition , kunbind ) . Hence , any perturbation that alters the rate that the tethered head binds to the microtubule is expected to alter motor processivity . Because tethered head binding involves diffusion of the head to the next binding site , followed by tight binding and ADP release , any constraints on this diffusional search imposed by the mechanical properties of the neck linker domain are expected to have a strong effect on motor processivity . The goal of the present simulations is to use the constraints provided by the experimental data to better understand the mechanical properties and dynamic behavior of the kinesin neck linker domain . In the present study , we examine the tethered diffusion of the kinesin head under three different qualitative regimes , corresponding to three mechanical representations of the neck linker domain . Each of these approaches constrains the diffusion about a central point through a restoring force that depends on the current chemical state of the motor , but the nature of the restoring force differs ( Figure 2 ) . The Constant Stiffness Model is analogous to a simple Hookean spring , in which the restoring force is proportional to the distance from the center point . The Increasing Stiffness Model is qualitatively similar to a Worm-Like Chain ( WLC ) entropic spring , in which the restoring force increases nonlinearly with extension . The Worm-Like Chain is the most common model used to describe the force-extension properties of unstructured polypeptides , and both AFM experiments [24] , [25] and Molecular Dynamics simulations [21] provide evidence that it is a reasonable approximation of neck linker mechanics . Finally , the Reflecting Model consists of a compliant Hookean spring up to a maximum contour length where the restoring force is infinite . Surprisingly , the Increasing Stiffness Model simulations do not agree well with experimental data , while the Reflecting Model simulations do agree with experiments . These results suggest that the Worm-Like Chain may not be an appropriate description of Kinesin-1 neck linker mechanics or at least must be modified from its current form to accurately describe the diffusive tethering of the free motor head . Alternatively , the results can be explained by positing a weak-binding state that stabilizes the tethered head near its binding site on the microtubule .
Polymers such as DNA and unfolded polypeptides are often described as “entropic springs” because stretching them , which reduces their number of possible conformational states , requires energy input to compensate for the loss of entropy [27] . From the WLC formalism , the force , fWLC ( x ) , required to extend a polypeptide chain an end-to-end distance x is given as [28] , [29]: ( 1 ) where , kBT is the Boltzmann constant times the absolute temperature , Lp is the persistence length , and Lc is the contour length of the polymer . The persistence length of unstructured polypeptide chains has been measured to be in the range of 0 . 5 to 2 nm [24] , [25] , [30] , [31] , though the sequence dependence and the degree to which these measurements extrapolate to chains as short as 14 residues are not clear . We recently carried out molecular dynamics simulations to measure the force-extension properties of the Kinesin-1 neck linker domain [21] . The results of these simulations are replotted in Figure 2B along with a curves for a WLC model with Lp of 0 . 7 nm , which accounts well for the data , and a Lp of 2 nm , which is less able to account for the data , and a contour length , Lc , of 0 . 364 nm per amino acid . Most studies in the literature [24] , [25] , [30] , [31] use a contour length of 0 . 38 nm per amino acid , which is the dimension of a single amino acid residue [32] . However , this value ignores the bond angle between adjacent amino acids , which , when taken into account yields a Lc of 0 . 364 nm per amino acid [33] . Because this improved value gave better fits to our molecular dynamics data , all of our Increasing Stiffness Model simulations used Lc = 0 . 364 nm per amino acid and Lp of 0 . 7 nm or , for comparison an Lp of 2 nm . The position of the tethered head , X ( t ) , was computed using the overdamped Langevin equation comprising viscous forces , tethering by the neck linker domain , and Brownian forces on the head domain . Mathematically , this was expressed as: ( 2 ) where ξ is the friction coefficient , ftether is the force of the tethering neck linker domain , D is the diffusion constant of the head domain and B ( t ) is a Wiener process representing Brownian motion of the head ( see Methods for further details ) [34] , [35] . Numerical solutions to the Langevin equation under the Increasing Stiffness Model were obtained using the Euler method [36] . To explore the different elasticity models in greater detail , Brownian dynamics simulations were performed to obtain stationary distributions of the motor head during the diffusive search . While these stationary distributions are only suggestive of phenomena in the full model where transient behavior can be a factor , they can provide insight into the behavior of the competing models . Figure 3 shows that in no-nucleotide states where both neck linkers are disordered and there is no positional bias of the free head , thermal motion is insufficient to achieve either forward or rearward binding of the free head . Characteristic of the Increasing Stiffness Model , when the free head diffuses more than a few nanometers away from its resting position , the restoring forces rise dramatically , preventing further progress . In contrast , following ATP binding , which docks one neck linker and provides a 4 nm forward displacement bias , the free motor head is able to diffuse to the next binding zone ( Figure 3 ) . However , even with this 4 nm displacement the probability that the tethered head is within ±1 nm of the next binding site is very low ( p = 0 . 008 ) . These stationary distributions suggest that for a 14 amino acid neck linker modeled with a drift corresponding the force extension curve of the WLC , the force required to stretch the chain in the range of 3–5 nm is sufficiently high that diffusion to these extended distances is very rarely achieved . These diffusive steps were integrated into the kinetic model shown in Figure 1 , and motor velocity and run length were obtained through simulations of the full hydrolysis cycle using the kinetic parameters given in Table 1 . The binding step that is being modeled ( State 4 to State 1 transition in Figure 1 ) involves diffusion of the head to the binding site followed by microtubule attachment . Thus , the attachment rate constant when the head is in the ±1 nm binding zone , kattach must be chosen empirically to achieve an effective attachment rate that is faster than the overall motor stepping rate of ∼100 s−1 . Hence , kattach was set to 7 . 5×104 s−1 . Note that this is a first-order rate constant , with the probability of being within the binding zone accounting for the concentration term . While this rate constant appears fast , the relative concentration of one motor in a hemispheric volume of radius 1 nm around the binding site is 0 . 8 M , so the equivalent bimolecular on-rate is ∼105 M−1s−1 ( also see Discussion ) . As seen in Table 2 , the predicted Kinesin-1 velocity ( 860±9 nm/s , mean ± SEM , N = 50 runs ) and run length ( 1541±198 nm ) agreed well with the experimentally observed values of 703±136 nm/s and 1747±199 nm [17] , respectively . To test the validity of the Increasing Stiffness Model , we simulated the behavior of a Kinesin-1 motor containing a three amino acid insert in the neck linker domain , Kinesin-1+DAL . These three residues correspond to the last three residues in the Kinesin-2 neck linker domain , which is 17 amino acids compared to 14 for Kinesin-1 . In recent single molecule experiments , Kinesin-1+DAL was shown to move at 552±103 nm/s , slightly slower than wild-type , and have a run length of 355±14 nm , which is more than four-fold shorter than wild-type [17] . Compared to wild-type Kinesin-1 , the stationary distribution for Kinesin-1+DAL is significantly broadened ( Figure 4 ) , meaning intuitively that under the increasing force model the motor spends a larger proportion of its time within 1 nm of the binding zone . As a result , when the diffusive step was integrated into the entire kinetic model , simulations predicted a moderate increase in the mean velocity to 944±10 nm/s and a significant increase in the run length to 3707±469 nm ( Table 2 ) . Inspection of the model makes this clear – State 4 is a vulnerable state and increasing the effective attachment rate ( equal to kattach multiplied by the fraction of time the head spends in the binding zone ) decreases the probability of the motor detaching from that state . Similar behavior was observed when the persistence length in the Increasing Stiffness Model was increased from 0 . 7 nm to 2 nm ( Table 1 ) . In an attempt to better account for the experimentally observed reduction in the velocity and run length of Kinesin-1+DAL , the Increasing Stiffness Model was simplified to a Constant Stiffness Model corresponding to a Hookean spring . As seen in Figure 2B , the Hookean spring has a much more liberal force-extension curve than the Increasing Stiffness Model and is predicted to allow the motor to diffuse to the binding site much more readily . A spring stiffness of 1 pN/nm was chosen , which is comparable to the observed entropic elasticity of disordered polypeptides during extension [27] , [37] . Kinesin-1+DAL neck linkers were modeled by adjusting the spring stiffness to a value of 0 . 8 pN/nm to reflect the increase in length from 14 to 17 amino acids . For the Constant Stiffness Model , which is represented by a linear stochastic differential equation , transition probabilities are Gaussian allowing for an exact simulation on the discrete time steps . As seen in Figure 4 , the stationary positional distribution of the free head for both Kinesin-1 and Kinesin-1+DAL were significantly broader for the Constant Stiffness Model than for the Increasing Stiffness Model , meaning that the free head has a higher probability of existing within 1 nm of the binding site ( p = 0 . 058 ) . Setting kattach to 104 s−1 resulted in a velocity of 860±6 nm/s and run length of 1915±247 nm for Kinesin-1 , consistent with experimental data ( Table 2 ) . The velocity and run length values for Kinesin-1+DAL were slightly elevated , which , like the Increasing Stiffness Model , is inconsistent with the experimental data . While the Constant Stiffness Model significantly reduced the constraints on the diffusion of the free head , it is physically unreasonable to predict that the neck linker domain will stretch beyond its maximum contour length . Hence , the third neck linker model examined included constant stiffness up to a reflecting barrier , which broadly corresponds to a Finitely Extensible Nonlinear Elastic ( FENE ) model having a small stiffness [35] , [38] . Conceptually , the reflecting model is similar to rectified Brownian movement and is described by a reflected diffusion process with a strict upper and lower bound [39] . Quantitatively , the Reflecting Model combines a loose Hookean spring ( fSpring ) with barriers set by the contour length of the neck linker . The force-extension profile of the Reflecting Model is shown in Figure 2C , and the position of the motor head can be described as: ( 3 ) We implement the model as a reflected diffusion ( see [40] for an accessible introduction to reflected diffusion processes ) . Intuitively , if the diffusive forces on the motor head are sufficient to pass the limiting barriers during any time step , then the location of the motor head is constrained by the term K ( t ) to stay within the boundaries [35] , [41] . At each time step , a numerical solution to Equation 3 is obtained by using Lepingle's adapted Euler method for reflected diffusions [41] . Lepingle's method uses a reflected Brownian motion approximation to the diffusion process near the barriers preventing an excess number of values at the boundary . The limiting barriers were positioned at a distance equal to the contour length of the tethering neck linker away from the anchor point of the spring ( 5 . 3 nm for Kinesin-1 ) . Analysis of positional distributions using different spring constants revealed that a spring stiffness of ≤0 . 01 pN/nm allowed for the motor head to experience nearly unbiased diffusion ( i . e . a flat distribution ) . The Kinesin-1 and Kinesin-1+DAL stationary distributions using a Reflecting Model with a spring constant k = 0 . 01 pN/nm are shown in Figure 4 . Because diffusion of the free head is relatively unconstrained ( within its maximal limits ) in the Reflecting Model , the free head spends a significant fraction of its time ( p = 0 . 18 ) within ±1 nm of the binding site , and a kattach of 3 , 500 s−1 is sufficient to achieve an effective attachment rate that is faster than the overall stepping rate . When this diffusional model was integrated into the entire kinetic cycle , the Kinesin-1 simulations ( 858±8 nm/s velocity and 1777±238 nm run length ) again agreed with experimental data . More importantly Kinesin-1+DAL had a slightly reduced motor velocity ( 800±17 nm/s ) and a run length ( 1346±221 nm ) that was shorter than wild-type ( Table 2 ) . This result qualitatively agrees with the experimental data – extending the neck linker domain reduces the motor run length . This reduction in the Kinesin-1+DAL run length can be understood by examining Figure 4 – extending the Kinesin-1 neck linker effectively expands the region over which the free head diffuses , thus decreasing the proportion of time the motor spends within 1 nm of the binding zone . Using an identical kattach leads to a slower effective attachment rate and increases the probability of detachment during each diffusive step .
Mechanistic models describing the directed movement of molecular motors can involve concerted conformational changes , Brownian motion , or a combination of these mechanisms . For Kinesin-1 , a body of experimental data supports the idea that ATP binding docks the neck linker of the bound head and displaces the free head toward the next binding site . However , to complete the step the free head must diffuse to its binding site , bind there , and release its bound ADP to achieve a high affinity microtubule-bound state ( Figure 1 ) . Because the free head is tethered during this diffusive step , the mechanical properties of the neck linker domain play an important role . If the neck linker is too short and/or too stiff , then the free head cannot reach the next binding site . However , if the neck linker domain is too long and/or too compliant , then the inter-head tension will be insufficient to coordinate the chemomechanical cycles of the two heads ( front-head and rear-head gating ) [4] . The need for investigating the role of the neck linker domain in tethered diffusion is of particular importance for understanding recent studies that have shown that artificially extending the Kinesin-1 neck linker profoundly affects motor behavior [8] , [17] , [42] . Because neck linker domains in diverse members of the kinesin superfamily diverge in sequence and length , understanding neck linker dynamics will also help to uncover how different kinesins are evolutionarily tuned to their specific cellular functions . Here , we model the free kinesin head as a sphere and the microtubule as a one-dimensional lattice of binding sites , and we investigate the diffusion of the free head tethered by different qualitative representations of the flexible neck linker domain . Because the WLC is the most commonly used model to describe the force-extension characteristics of unstructured polypeptides , our analysis initially focused on the Increasing Stiffness Model . The striking result is that due to the stiffness of the neck linker , the diffusing free head spends only a small fraction of the time ( p = 0 . 008 ) near its binding site , and thus extending the neck linker domain is expected to increase the processivity , contrasting with experimental results . The first question to address is whether the fast attachment rate ( kattach = 7 . 5×104 s−1 ) needed to reproduce the experimental Kinesin-1 velocity and run length results using the Increasing Stiffness Model is realistic . While this first-order on-rate is consistent with a reasonable bimolecular on-rate of ∼105 M−1 s−1 , achieving tight binding to the microtubule also requires ADP release , which is thought to occur at a rate slower than 103 s−1 [22] , [42] . Without this tight binding resulting from ADP release , the head would rapidly unbind and diffuse back toward its resting position , significantly slowing down the process . Furthermore , extending the model to three dimensions would amplify this discrepancy – if the probability of being within ±1 nm of the binding site is 0 . 008 in one dimension , then it would drop to <10−6 in three dimensions . Because the effective attachment rate is equal to kattach multiplied by the probability the head is within 1 nm of its binding site , a 10−6 probability would require a kattach greater than 108 s−1 to achieve a 100 s−1 overall motor stepping rate . Hence , in the Increasing Stiffness Model there is a significant discrepancy between the fast attachment rate needed for the model to work and the observed ADP release rate , which is the step that regulated tight binding of the head to the microtubule . The second and more fundamental argument against the Increasing Stiffness model is that it predicts that mutations that extend the Kinesin-1 neck linker will enhance both motor velocity and processivity , which is the opposite of what is seen experimentally [8] , [17] , [42] . This point deserves closer inspection . State 4 is a vulnerable state in the kinesin hydrolysis cycle because following ATP binding and hydrolysis there is a competition between binding of the tethered head and unbinding of the attached head . Due to this kinetic bifurcation , any mechanism that slows down the attachment step without altering the unbinding step will increase the probability of detachment and therefore reduce the overall run length . Quantitatively , the probability of detaching per step is equal to , so this dependence holds true even if this attachment step is nowhere near rate limiting . Importantly , using the chemomechanical cycle shown in Figure 1 , any neck linker model that includes non-negligible restoring forces will predict an increase in motor processivity when the neck linker domain is extended . This includes the Increasing Stiffness Model using a 2 nm persistence length ( Table 2 ) and the Constant Stiffness Model , and it would also be expected for polymer models such as a Freely Jointed Chain . The reason is that in all of these models , extending the neck linker increases the probability that the tethered head will be near its binding site , which increases the effective attachment rate . While it can't be ruled out that extending the neck linker alters other rate constants in the chemomechanical cycle , because no other steps are as intimately linked to motor processivity , it is unlikely that these compensating changes can resolve the discrepancy between experimental results and the Increasing Stiffness Model simulations . In contrast to the Increasing Stiffness Model , when the neck linker was modeled as a reflecting process , the free head spent a significant fraction of its time within ±1 nm of the next binding site . Hence , achieving a reasonable effective attachment rate only required a kattach of 3 , 500 s−1 , which is closer to experimentally measured ADP release rates [22] , [42] . Furthermore , extending the neck linker predicted a decrease in both motor velocity and run length , consistent with experimental results . The drawback to the Reflecting Model is that it ignores any entropic spring characteristics of the flexible neck linker and instead assumes an extremely compliant neck linker domain up to the maximum limits of extension . Quantitatively , a Worm-Like Chain with Lp>Lc achieves this same force-extension profile , but because the WLC approximation was developed for polymers with Lc>Lp and ignores any compressive forces , this comparison should be treated cautiously . How is it possible to reconcile the Increasing Stiffness Model simulations , which suggest that the neck linker strongly limits diffusion of the free head , with the more experimentally consistent Reflecting Model results that rely on a physically improbable model of the neck linker domain ? There are two possible resolutions to this conflict . The first possibility is that the undocked neck linker is actually much stiffer than predictions from the WLC ( Figure 5 ) . A 14 or 17 amino acid polypeptide is considerably shorter than polymers such as titin that have been experimentally measured and successfully fit with the WLC model [24] , [25] , [30] , [31] . While the Molecular Dynamics simulations presented in Figure 2 suggest that the Kinesin-1 neck linker properties are reasonably well fit by the WLC , these simulations did not include other regions of the motor domain that may help to stabilize the neck linker in a more extended conformation . It should be noted that in a crystal structure of the mitotic motor Eg5 ( Kinesin-5 ) in ADP , the neck linker interacts stably with the head in a perpendicular position [43] . This suggests that the neck linker in the leading head would be relatively straight and stabilized and would not act as a flexible tether at all . An analogous neck linker position for Kinesin-1 was observed by Rice et al ( Figure 4d in [5] ) , although key residues that stabilize this conformation in Kinesin-5 are absent in Kinesin-1 . Nonetheless , if the neck linker domain were considerably stiffer as a result of this docking mechanism or some other structural feature , then it would act more as a pivoting rod and the tethered head would spend considerably more time near the next binding site . Neck linker extensions would then be expected to have slower attachment rates because the head is “pushed” beyond its optimal position . In principle , this hypothesis could be tested by attaching fluorescent probes to either end of the neck linker domain and monitoring its end-to-end length by fluorescence resonance energy transfer . A second way to resolve the models is to posit a weak binding state preceding ADP release of the tethered head ( Figure 5 ) . Despite the head residing near the binding site less than 1% of the time in the Increasing Stiffness Model simulations , the recurrence time ( mean time to return to the binding zone after leaving ) is still under one microsecond ( 350 nsec for the Increasing Stiffness Model ) . Hence , the kinetics of reaching the binding site are not at all limiting , and instead the problem is that the head rapidly diffuses away from this extended position before having a chance to bind . If there were a stabilizing interaction between the head and the microtubule ( a weak binding state ) , such that the head was held at this extended position , this would increase the fraction of time the head remained in the binding zone and hence increase the probability that ADP was released to trigger tight binding . Positive charge in the kinesin motor domain , neck linker domain , and neck coil domain have all been shown to enhance processivity [18] , [44] , [45] . Such a weak binding state for kinesin has been proposed by Cross ( M·KTRAPPED·ADP ) [16] , and similar weak binding states have been characterized in myosin [46] . For this weakly-bound state to facilitate ADP release and thus resolve this kinetic disparity , it would need to significantly shift the equilibrium to the bound state against the restoring force of the extended neck linker; however this interaction couldn't be too tight or it would slow the subsequent detachment of the head during the next step ( i . e . kdetach in Figure 1 ) . Because this weak-binding conformation would be expected to be stabilized by electrostatic interactions between the kinesin head and the microtubule , this hypothesis could in principle be tested by introducing mutations in the microtubule binding site and/or increasing the ionic strength and measuring whether the processivity is diminished . By integrating tethered diffusion into a chemical kinetic model of the kinesin hydrolysis cycle , we find that restoring forces imposed by the flexible neck linker domain profoundly constrain the ability of the free head to diffuse to its binding site . When the neck linker domain is modeled as a spring with a length-dependent stiffness ( a WLC ) , the required attachment rates for Kinesin-1 are very high and the predicted behavior of motors with extended neck linkers contrasts with experimental results . The present modeling work suggests that either a ) the neck linker domain is very compliant up to an inextensible limit ( Reflecting Model ) , b ) the neck linker resides in a more extended conformation than is generally thought , perhaps stabilized by the core motor domain , or c ) stabilizing interactions between the tethered free head and the microtubule ( a weak binding state ) hold the tethered head in place to allow ADP release and strong binding that completes the motor step . These hypotheses can be tested by structural and kinetic analysis of wild-type and mutant kinesins , as well as by comparing the behavior of diverse motors across the kinesin superfamily .
To make this description more concrete , we present a sketch of the algorithm used for simulation . The description below details the conditions required to transition through each of the four chemical states of a full cycle that comprises a single mechanical step . The full algorithm requires keeping track of each individual head and the distance each moves while free . State 1: Both Heads Bound . State 2: Initial condition for head is set to location of binding site , −8 . 2 nm . Potential is centered between binding sites at −4 . 1 nm . Set time in State 2: t = 0 , n = 0 . Set attachment rate k ( x ) = kattach if head is within 1 nm of either forward or rearward binding site , otherwise k ( x ) = 0 . State 3: Initial condition for head is determined by the terminal location of the free head from the previous chemical state ( 2 or 3 ) . Center of the potential is moved to the location of the bound head ( 0 nm ) . Set time in State 3: t = 0 . State 4: Initial condition for head is determined by the terminal location of the free head from the previous chemical state ( 3 or 1 ) . Center of the potential is moved to a position x = 4 . 1 nm forward of the bound head , corresponding to ATP-induced docking of the neck linker domain . Set time in State 4: t = 0 . Define attachment rate k ( x ) = kattach if head is within 1 nm of next binding site , otherwise k ( x ) = 0 . | Kinesin molecular motors provide a valuable model for uncovering the interplay between nanoscale mechanics and biochemistry at the level of single protein molecules . The mechanism by which kinesin motors “walk” along microtubules involves both conformational changes in the motor domains , or “heads” , as well as diffusive movements in which one head searches for its next binding site on the microtubule . This diffusive search is constrained by the 14 amino acid neck linker domain , which must be sufficiently flexible to allow the free head to diffuse forward , yet sufficiently stiff to enable mechanical communication to the rest of the molecule . We have modeled this diffusive search and integrated it into a stochastic model of the kinesin chemomechanical cycle . We find that modeling the neck linker as a Worm-Like Chain , the model most frequently used to describe unstructured polypeptide chains , results in motor behavior that conflicts with published experimental results for kinesins containing naturally or artificially extended neck linker domains . These results suggest that either the mechanical properties of the neck linker domain must be fundamentally reevaluated or that there are motor-microtubule interactions that stabilize the motor domain at its next binding site . | [
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] | 2010 | Monte Carlo Analysis of Neck Linker Extension in Kinesin Molecular Motors |
All cells are subject to structural damage that must be addressed for continued growth . A wide range of damage affects the genome , meaning multiple pathways have evolved to repair or bypass the resulting DNA lesions . Though many repair pathways are conserved , their presence or function can reflect the life style of individual organisms . To identify genome maintenance pathways in a divergent eukaryote and important parasite , Trypanosoma brucei , we performed RNAi screens to identify genes important for survival following exposure to the alkylating agent methyl methanesulphonate . Amongst a cohort of broadly conserved and , therefore , early evolved repair pathways , we reveal multiple activities not so far examined functionally in T . brucei , including DNA polymerases , DNA helicases and chromatin factors . In addition , the screens reveal Trypanosoma- or kinetoplastid-specific repair-associated activities . We also provide focused analyses of repair-associated protein kinases and show that loss of at least nine , and potentially as many as 30 protein kinases , including a nuclear aurora kinase , sensitises T . brucei to alkylation damage . Our results demonstrate the potential for synthetic lethal genome-wide screening of gene function in T . brucei and provide an evolutionary perspective on the repair pathways that underpin effective responses to damage , with particular relevance for related kinetoplastid pathogens . By revealing that a large number of diverse T . brucei protein kinases act in the response to damage , we expand the range of eukaryotic signalling factors implicated in genome maintenance activities .
Faithful genome transmission is necessary for the growth and propagation of all organisms . Damage to the genome can arise from a myriad of sources , including exposure to mutagenic chemicals and metabolic or replicative by-products . If damage is left unrepaired , the genetic information can be altered , leading to death , reduced fecundity and disease in multicellular organisms . To counter all potential genotoxic lesions , a wide range of DNA repair pathways , collectively known as the DNA damage response ( DDR ) , are found in all three domains of life , though with variation in the underlying machineries of each pathway and their relative use in different organisms [1 , 2] . More widely , genome repair is one arm of a range of processes that allow cells to limit or tackle cellular damage . Trypanosoma brucei is an extracellular protozoan parasite of mammals , causing the neglected disease African trypanosomiasis ( sleeping sickness in humans , Nagana in cattle ) [3] . In common with related kinetoplastids , T . brucei shows divergence in several core cellular processes , including the near universal use of multigenic transcription and reliance on post-transcriptional strategies for gene expression control . Nonetheless , T . brucei is a genetically tractable protozoan , making it a valuable model amongst eukaryotic microbes . Multiple DDR pathways operate in kinetoplastids , including three forms of excision repair ( mismatch , nucleotide and base ) and at least two forms of DNA break repair ( homology- and microhomology-directed ) [1 , 4] . Non-homologous end-joining ( NHEJ ) , an important break repair pathway in all domains of life , appears to be absent in kinetoplastids , despite the presence of both subunits of the Ku heterodimer [5–9] . Furthermore , homologous recombination ( HR ) not only provides for DNA break repair genome-wide , but also catalyses the locus-directed movement of Variant Surface Glycoprotein ( VSG ) genes that underpins immune evasion by antigenic variation in T . brucei [10] . The above knowledge has been accrued through homology-informed candidate gene studies , meaning several DDR activities have not been functionally tested and potentially kinetoplastid-specific activities may have escaped detection . Virtually no work has examined how the DDR , cell and life cycle progression are linked in kinetoplastids . Protein kinase ( PK ) signalling is likely to play a central role in such links . However , no work has described any PK that acts in the kinetoplastid DDR , despite phosphorylation of several T . brucei repair proteins , including BRCA2 and RAD50 , having been described [11] , though the functional significance of the modifications is unknown . Damage-dependent phosphorylation of T . brucei histone H2A on Thr130 , generating the kinetoplastid variant of the γH2A ( X ) chromatin modification [12] , has also been described [13] , but the parasite PK ( s ) that directs this alteration and its contribution to repair has not been examined . These gaps in understanding of PK signalling and wider aspects of the kinetoplastid DDR are impediments to understanding the evolution of the eukaryotic DDR and to evaluating the potential anti-parasite efficacy of compounds that target repair-associated factors , such as anti-cancer approaches acting on the phosphatidyl inositol 3-kinase-like PKs ATR and ATM [14 , 15] , which play key roles in recognising DNA breaks and directing the appropriate repair pathway , and have homologues in T . brucei . To identify the full complement of gene products and pathways that act in damage repair , comprehensive screens are needed , such as have been deployed in characterising the DDR in other eukaryotes [16] . In T . cruzi , changes in RNA [17] and protein [18] levels after exposure to ionizing radiation have been assessed , but genome-wide screening of kinetoplastid mutants after exposure to damage has not been attempted . RNAi coupled with next generation sequencing , termed RNAi target sequencing ( RIT-seq ) , has been shown to be a feasible approach to evaluate the importance of potentially all genes in T . brucei during growth and differentiation [19] . Subsequent RIT-seq screens have identified genes involved in anti-trypanosome drug action [20–22] , human serum susceptibility [23] and quorum-sensing [24] , in each case by selecting for cells in the population that can grow in the presence of selection only after RNAi . To date , RIT-seq has not been used to screen for T . brucei genes whose loss by RNAi increases sensitivity to a treatment . Here , we describe such a ‘synthetic lethal’ RIT-seq approach , seeking to identify genes whose loss sensitises T . brucei to methyl methanesulphonate ( MMS ) , an Sn2 alkylator [25] . MMS causes DNA lesions , including breaks , which can be toxic , mutagenic and prevent DNA synthesis by impeding replication fork movement . The transcriptional and proteomic responses of several eukaryotic cells to MMS have been described , revealing wide-ranging changes suggestive of a network of adaptations to cope with MMS-induced damage , some common to other types of DNA damage and stress [26] . In addition , three studies , two in yeast using gene mutants [27 , 28] and one in Drosophila melanogaster using RNAi [29] , have described genes involved in MMS tolerance and confirm that multiple pathways , including DDR reactions , contribute to the response to this widely used genotoxic agent . RIT-seq screening of MMS-treated bloodstream form ( BSF ) T . brucei described here revealed several MMS damage response pathways , including homologous recombination and nucleotide excision repair , which are common between the kinetoplastid parasite , yeast and D . melanogaster , though at least two pathways appear not to act in T . brucei: transcriptional control and Notch signalling Several of the conserved MMS damage response pathways we reveal have not been examined previously . In addition , many putative T . brucei-specific MMS repair-associated proteins are revealed whose functions could not have been evaluated previously , as they lack sequence homology with other eukaryotes . Finally , a focus on PKs revealed 30 proteins ( many of which appear essential ) whose loss is predicted to sensitise BSF T . brucei cells to MMS . We provide targeted validation of nine novel T . brucei PKs that act in MMS damage response , including detailed analysis of an aurora PK . The range of PK families uncovered exceeds the PKs previously implicated in the eukaryotic damage response , suggesting unanticipated functions . The two screens therefore provide insight into cellular repair activities in T . brucei , some novel and some likely conserved in other eukaryotes .
We used BSF T . brucei cells , the life cycle stage that causes mammalian disease , to run a RIT-seq screen for MMS damage response factors ( Fig 1 ) . To this end , an RNAi fragment library representing >99% of the genome in a population of ~ 10 million cells [19 , 30] was grown for 24 hours ( 3–4 population doublings ) in the presence of tetracycline ( Tet ) , which induces RNAi ( Fig 1 ) . Genomic DNA was prepared from a sample of the population , which was then split into four cultures and allowed to grow for another four days in the presence of Tet . Two of the cultures were grown throughout the four days in 0 . 0003% MMS , a concentration that induces damage ( as evidenced by increased γH2A levels ) [13] and impairs , but does not prevent , growth ( see below ) . Genomic DNA was then prepared from all four BSF populations ( subjected to RNAi for a total of 5 days ) . By mapping loss of gene-specific reads in cells that were both RNAi induced and MMS-treated relative to cells subjected to RNAi but not to MMS , we sought to identify genes specifically required to maintain growth in the presence of MMS induced damage . To do so , we PCR-amplified the RNAi targets using primers that flank all RNAi constructs integrated into the genome [31] and limited cycle PCR . The PCR resulted in a range of products between ~0 . 2–1 . 6 kbp in all samples ( S1 Fig ) that reflects the sizes of the RNAi target fragments in the RNAi library [30] . The PCR products were then sequenced and reads were mapped to a ‘minimal’ version of the T . brucei genome that included only the 9849 predicted CDS , with a comparable read depth profile to a previous RITseq after RNAi alone ( S2 Fig ) [19] . Fig 2 shows an evaluation of the effect of MMS on gene abundance in the population after 5 days of RNAi-induction . For each sample , the number of sequence reads that mapped to every annotated gene was determined and normalised relative to CDS length and total number of reads in the library . These read depth values were then averaged for the two Tet+ , MMS- samples and for the two Tet+ , MMS+ samples , and the ratio of reads in the latter determined relative to the former . The resulting MMS+/MMS- ratio for every gene was viewed in a scatter plot relative to gene position on the 11 T . brucei chromosomes ( Fig 2A and 2B; individual gene data in S1 Table ) . Given the limitations of having only duplicate samples at one control and one experimental time point using a single concentration of MMS , we consider it likely that the screen is most robust when considering read depth trends across damage response pathways or networks , and should be viewed with caution when comparing read depth to evaluate the roles of individual genes . Thus , we first examined cohorts of genes characterised to act in three DNA repair pathways ( Fig 2A ) . HR and nucleotide excision repair ( NER ) pathways have been extensively characterised in T . brucei and have been implicated in the MMS damage response in Drosophila and yeast [29] . MMS+/MMS- ratios for multiple HR and NER genes whose functions have been examined previously revealed a trend towards <1 ( five of seven HR genes , seven of eight NER genes; S2 Table ) , indicating that cells in which RNAi targets these genes are depleted in the MMS-treated population relative to the untreated control . Indeed , the MMS+/MMS- ratios of the NER genes matched what is known regarding the novel operation of this repair pathway . Specifically , global genome NER in T . brucei has undergone neofunctionalisation to act in an essential genome repair pathway , manifesting as increased sensitivity to inter-strand crosslink damage upon depletion of these NER factors [32] . NER in response to transcription stalling ( transcription-coupled NER ) is the main form of NER in T . brucei and bypasses recruitment of TFIIH-associated XPB and XPD helicases , using instead a novel XPB orthologue ( XPBz ) [33] . Thus , T . brucei XPBz registered an MMS+/MMS- ratio of 1 . 36 , consistent with the lack of MMS sensitivity previously described in xpbz null mutants [33] . In contrast , XPC ( the lesion recognition factor during global genome NER ) showed the lowest MMS+/MMS- ratio ( ~0 . 41 ) , suggesting a repair function involving recruitment of the XPB and XPD helicases ( consistent with MMS sensitivity following RNAi of TFIIH components ) [33 , 34] and the XPF/ERCC1 endonuclease complex; all of these factors had MMS+/MMS- ratios of <0 . 75 . Ratios of the transcription-coupled NER factors CSB and XPG ( 0 . 86 and 0 . 75 ) were higher than for global genome NER factors , suggesting this NER pathway plays a lesser role in the response to MMS . Overall , the RIT-seq outputs suggest global NER plays a more profound role in response to MMS genome damage than HR , since the MMS+/MMS- ratios of most HR factors were rarely as low ( Fig 2A , S2 Table ) . The two HR genes with the lowest MMS+/MMS- ratios were RAD51 paralogues , RAD51-3 and RAD51-6 ( 0 . 46 and 0 . 61 ) [35 , 36] , perhaps indicating specialised activities , such as those described in Leishmania [37] and related to MMS-induced replicative stress [38] . Though mismatch repair ( MMR ) has been implicated in MMS repair elsewhere [29] , the evidence for a similar role in T . brucei is weak ( Fig 2A ) . Here , three of six annotated MMR genes had MMS+/MMS- ratios <1 , none of which have been functionally examined in any kinetoplastid; nonetheless , MSH3 ( 0 . 50 ) is known to contribute to processing of HR strand exchange intermediates in other eukaryotes [39] . Base excision repair ( BER ) is a key response pathway to MMS damage [29] , but evaluating this in T . brucei is complicated by lack of genetic analyses of most constituent genes and the unusual targeting of two DNA polymerase ( Pol ) beta homologues to the kinetoplast [40] , questioning if and how BER operates in the nucleus . Nevertheless , a role for T . brucei BER in tackling MMS damage is consistent with six of ten putative BER factors displaying MMS+/MMS- ratios < 1 ( S2 Table ) . Amongst these factors , one DNA glycosylase ( OGG1 ) , so far only examined functionally in T . cruzi [41] , showed the strongest evidence for MMS repair , consistent with MMS generating oxidative DNA damage [29] . In addition , one of the two mitochondrial DNA Pol beta genes displayed an MMS+/MMS- ratio of 0 . 79 , though whether this indicates a role in repair of nuclear or kinetoplast damage is unknown [42] . BER also contributes to repair of single strand DNA breaks , which MMS generates . Poly ( ADP-ribose ) polymerase ( PARP ) recognises such breaks [43] and , intriguingly , a potentially novel and uncharacterised PARP showed clearer evidence of a repair role ( S2 Table; see Fig 3 , below ) than the more conventional kinetoplastid PARP homologue [44] . Another key factor in responding to genome damage is the heterotrimeric Rad9-Rad1-Hus1 ( 9-1-1 ) complex and the MMS+/MMS- ratios of the component genes suggest the T . brucei complex acts in genome surveillance ( S2 Table ) . Indeed , the subtly different MMS+/MMS- ratios for Rad9 ( 0 . 82 ) and Hus1 ( 1 . 08 ) appear consistent with the different phenotypes of the two mutants in Leishmania after exposure to MMS , suggesting the T . brucei factors may also play distinct roles outside the 9-1-1 complex [45] . The above analysis relies upon a trend for MMS+/MMS- ratios <1 amongst a cohort of DNA repair genes . To test this predictive approach , we examined the MMS+/MMS- ratios in three gene cohorts not expected to act in the MMS damage response ( Fig 2B ) . First , none of 14 genes implicated in intraflagellar transport had an MMS+/MMS- ratio < 1 . Second , we examined core and variant histones [46] , plotting the maximum and minimum MMS+/MMS- ratios amongst the multigene arrays encoding histones H2A , H2B , H3 and H4; only three of 13 values were < 1 , and the lowest was 0 . 92 . Finally , we examined eight proteins implicated in kinetoplast replication ( six DNA Pols and two primases ) ; seven of the MMS+/MMS- ratios were 0 . 97 or above , and the gene with the lowest ratio ( 0 . 79 ) encodes DNA Pol Beta ( discussed above ) . To examine more broadly how T . brucei responds to MMS exposure , we selected the 274 genes that had an average MMS+/MMS- ratio of 0 . 5 or less , indicating 2-fold or greater loss of reads after RNAi in the presence of MMS than the absence . 44 were predicted to encode VSGs or VSG pseudogenes and were discounted as mapping artefacts ( S3 Table ) . Though for the majority of the remaining 230 genes no predicted function is currently available ( as they are annotated as hypothetical or hypothetical-unlikely; Fig 2C ) , we examined what processes are represented in the gene set by asking which gene ontology ( GO ) terms , in two classifications , displayed significant enrichment ( Fig 2D; all significantly enriched GO terms are shown in S3 Table ) . Enrichment of genes involved in DNA functions was widespread , and the pronounced enrichment of the GO terms ‘DNA repair’ and ‘damaged DNA binding’ ( both P values <0 . 0001 ) is consistent with the above analysis of known DNA repair pathways ( Fig 2 , S2 Table ) . Thus , the cohort of hypothetical genes in this set is likely a rich source of previously undiscovered damage-repair factors . An RNAi screen in D . melanogaster predicted 13 pathways that act to tackle MMS damage ( S3 Table ) , with many conserved in yeast and mammals [29] . Six of these pathways involve DNA repair , including the four pathways discussed above . One other pathway was DNA damage signalling initiated by DNA interacting PKs ( see below for discussion of kinome-focused RIT-seq ) . The sixth repair pathway involved RecQ-like helicases , of which D . melanogaster encodes four . Two RecQ helicases are found in T . brucei , one of which displayed an MMS+/MMS- ratio of 0 . 86 ( S2 Table ) , consistent with MMS sensitivity of null mutants [47] . Given the ubiquitous roles of helicases in DNA and RNA biology and the availability of classification and functional predictions for the estimated 112 helicases in T . brucei [48] , we examined the MMS+/MMS- ratios of all putative helicase genes ( Fig 3A , S2 Table ) . Separating the helicases into those most likely to act on DNA or RNA suggested that tackling DNA damage by MMS is more critical , since a larger percentage of DNA helicases ( 62% ) than RNA helicases ( 32% ) displayed MMS+/MMS- ratios <1 . Of the 21 DNA helicases with a ratio <1 , 11 have yet to be functionally examined ( including three with ratios <0 . 5; Fig 3A , S3 Fig , S2 Table ) , suggesting the existence of unexplored pathways of genome maintenance . Amongst the characterised DNA helicases are eight Pif1-like helicases , most of which are mitochondrial; five of these genes displayed MMS+/MMS- ratios <1 but separating repair functions from predicted kinetoplast replication roles ( PIF1 , PIF8 ) would require further analysis [49–51] . Two genes encoding RuvB-like factors , potentially T . brucei homologues of Pontin and Reptin in other eukaryotes [52] , are notable for displaying >5 fold increased reads in the MMS-treated cells relative to the controls ( S1 and S2 Tables ) . However , analysing genes or gene sets that show enrichment in the presence of MMS is more problematic to interpret in terms of the damage response than enhanced sensitivity , and so we did not explore this category of genes further . Seven MMS damage response pathways potentially unlinked to DNA repair have been predicted ( S3 Table ) : transcription and translation , ATP and glutathione metabolism , Notch and TOR signalling , and proteasome function [29] . None of these pathways were significantly over-represented in the cohort of 230 genes described above , but only two can be ruled out as damage response strategies in T . brucei ( S3 Table ) . The Notch pathway acts to determine cell fate [53] , but no evidence of this pathway has been described in any kinetoplastid to date . MMS sensitivity as a result of transcription factor loss , as well as changes in the expression of core transcription factor genes after exposure to MMS , has been described in a number of eukaryotes [28 , 29 , 54] . Thus , the absence of enrichment in GO terms associated with transcription in the MMS+/MMS- <0 . 5 gene set , despite >200 genes annotated as transcription factors in T . brucei , appears meaningful . Perhaps the ubiquitous use of multigenic transcription , and the resulting devolution of gene expression controls to post-transcriptional mechanisms in kinetoplastids [55] , means T . brucei cannot respond to MMS stress by upregulating gene transcription . The RNAi approach we have taken to identify MMS damage response factors is likely to under-represent essential genes , since mapped reads would be low after five days of RNAi even in the absence of MMS . Since many components involved in translation , proteasome function and ATP metabolism are essential [19 , 56] , it is intriguing that a small number of genes ( two , one and three , respectively; Fig 2C ) involved in each of these functions was detected amongst the 230 genes in the MMS+/MMS- <0 . 5 set . In this regard , it is notable that transcriptome [17] and proteome [18] analyses indicate that active ( and perhaps enhanced ) translation is needed to allow T . cruzi to recover from ionising radiation exposure . In kinetoplastids , many aspects of glutathione metabolism have been usurped by trypanothione , which has a key role in defence against oxidative damage [57] , and MMS+/MMS- ratios of component genes ( S2 Table; three of eight genes <1 ) may suggest this novel pathway contributes to the T . brucei MMS damage response . Kinetoplastids are unusual amongst single cell eukaryotes in encoding four Target of Rapamycin ( TOR ) PKs , which signal a wide range of cellular activities [58] . Two of the four T . brucei TOR PKs ( TOR1 and TOR4 ) displayed MMS+/MMS- ratios <1 ( 0 . 75 and 0 . 74 , respectively ) and TOR4 was recovered in a kinome-specific MMS RIT-seq analysis ( see below ) , despite being essential for growth [59] , suggesting at least one arm of the expanded T . brucei TOR signalling network contributes to the MMS damage response . Around 20% of genes in the MMS+/MMS- <0 . 5 gene set ( ‘unknown’ in Fig 2C ) have annotated functions that have not been associated with the MMS damage response , many of which may be false positives , since they have roles in cellular processes that are unlikely to contribute to tackling MMS damage ( e . g . cell motility ) . Others provide predicted functions that cannot be readily discounted ( e . g . multiple peptidases and proteins implicated in cell division ) [28] . Perhaps , given the wide range of functions recovered in DNA damage screens [60] , some of these factors may yet prove to act in the T . brucei response to MMS-mediated DNA damage . Enrichment of GO terms associated with DNA Pol and PK activities , as well as nuclear transport , suggested potentially unexplored T . brucei MMS damage response pathways ( Fig 2C and 2D ) that were not predicted by RNAi in D . melanogaster [29] . The clearest cohort of genes associated with nuclear and nucleocytoplasmic transport detected in the MMS+/MMS- <0 . 5 gene set were ras or RAB GTPases ( Fig 2C ) , a class of enzymes that also contributes to chromosome segregation and cytokinesis in other eukaryotes [61] . In S . cerevisiae , mutants of nuclear pore complex factors are sensitised to MMS exposure [27] , but only four of 20 annotated T . brucei nucleoporin proteins had MMS+/MMS- ratios of <1 ( S1 Table ) [62] . Thus , the putative T . brucei nuclear transport response to MMS damage is unclear from the available data . In contrast , analysis of DNA Pol activities was more revealing . We generated two plots: the MMS+/MMS- ratios of all genes that have been annotated as DNA Pols and all genes whose annotation implicates them in genome replication ( Fig 3B and 3C; S2 Table ) . Together , these plots reveal that proteins providing core functions in genome replication do not act in the MMS damage response . For example , only one of the six subunits of the MCM2-7 replicative helicase [63 , 64] and only two of the five subunits of the divergent Origin Recognition Complex [65] had an MMS+/MMS- ratio <1 . Instead , putative accessory replication factors appear to play a crucial role in responding to MMS damage: translesion DNA Pols promote DNA replication across damage that cannot readily be repaired [66] and it is these enzymes that account for GO term enrichment of DNA Pol in the MMS+/MMS- <0 . 5 gene set . Only two translesion DNA Pols have been examined functionally in T . brucei , PrimPOL1 and PrimPOL2 [67] . Our RIT-seq screen indicated PrimPOL2 acts in the MMS damage response ( Fig 3A ) , consistent with the protein accumulating at DNA damage lesions after MMS exposure [67] . However , the most notable translesion DNA Pol identified in our screen was DNA POLK ( Kappa; Fig 3A ) , an enzyme found in multicopy in T . brucei but only in duplicate in T . cruzi [68] , where one isoform localises to the kinetoplast and permits bypass of 8-oxo-guanine lesions ( an oxidised base generated by MMS ) . The selection pressure that led to POLK expansion in T . brucei is unknown . A second putative MMS damage-response translesion DNA Pol is a putative homologue of the Rev3 component of DNA Pol zeta ( POLZ ) ( Fig 3B and 3C ) , a multisubunit B family DNA Pol [69] that has not been examined in any kinetoplastid . A further gene ( MMS+/MMS- ratio 0 . 63 ) encodes a putative subunit of Poly ( A ) Pol ( Fig 3A ) , which may be of interest because RNA processing enzymes are emerging as playing direct and indirect roles in responding to DNA damage [70 , 71] . In the broader class of replication-associated genes , the most prominent hit ( MMS+/MMS- 0 . 29; Fig 3C ) putatively encodes MCM8 , a replicative helicase paralogue that acts with MCM9 to promote HR [72] , which also has not been examined in kinetoplastids . The above data implicate a range of DNA replication functions in the T . brucei response to MMS , consistent with the need to complete S phase after damage ( 28 ) . To ask if wider genome-associated activities act in the T . brucei MMS damage response , we examined the MMS+/MMS- ratios of genes with annotated chromosome- ( Fig 3D ) and chromatin-associated ( S2 Table ) functions . Structural maintenance of chromosome ( SMC ) proteins play widespread roles in eukaryotic genome maintenance [73] , though RNAi of neither the primary T . brucei cohesin ( SMC1 and SMC3 ) nor condensin ( SMC2 and SMC4 ) subunits resulted in loss of reads after MMS exposure ( Fig 3C ) , suggesting no roles in damage repair . This is perhaps surprising , given that T . brucei homologues of SMC5 or SMC6 ( which provide repair functions amongst eukaryotic SMC complexes ) [74] have not been identified [75] . Perhaps SMC5/6 functions are assumed by the two putative nuclear T . brucei Topoisomerase II isoforms [76–78] ( S2 Table ) . A further T . brucei topoisomerase , Top3α , displayed an MMS+/MMS- ratio of 0 . 87 ( S2 Table ) , consistent with sensitivity of null mutants to other forms of damage [79] . It has long been known that eukaryotic telomeres present a paradox , in being DNA ends that do not elicit a damage response [80] . Four T . brucei telomere-associated factors , including KU70 and KU80 , each displayed MMS+/MMS- ratios <1 ( Fig 3C ) , in keeping with predictions that such factors impede DNA damage signalling and inappropriate repair . Unlike TRF [81] or RAP1 [82] , KU null mutants have been described and are viable [83 , 84] but lack sensitivity to DNA damaging agents , consistent with the apparent lack of KU-mediated NHEJ in kinetoplastids [5–9] . MMS-sensitivity after RNAi suggests the primary role of KU is telomere-related and complete loss of KU may cause adaptation to cope with telomere attrition [83 , 84] , such as by alternative lengthening of telomeres[85] . Amongst factors implicated in T . brucei chromatin ( S2 Table ) , 21 genes encoding potential chromatin modifying factors displayed MMS+/MMS- ratios <1 , three of which were putative SIR2-related factors: null mutants of the nuclear factor , SIR2rp1 , shows sensitivity to MMS [86] . Furthermore , a putative bromodomain-containing factor ( BDF2 ) was identified that has not been examined functionally in T . brucei but whose expression increases after UV damage in T . cruzi [87] . The number of , to date , unexplored factors suggests wider roles for chromatin in the response to damage , either by directly effecting repair or by altering transcription or replication [88] . One group of factors was putative arginine methyltransferases ( PRMTs ) , of which three out of four annotated genes displayed MMS+/MMS- ratios <1 ( S2 Table ) . PRMT6 displayed the lowest ratio ( 0 . 56 ) and has been previously implicated in T . brucei cytokinesis [89] , while PRMT1 in Toxoplasma gondii influences chromosome replication [90] , suggesting some of these factors act in genome maintenance . PKs were amongst the largest gene family that displayed GO enrichment ( Fig 2D , S3 Table ) , with eight genes belonging to six functional PK families ( Fig 4 ) in the MMS+/MMS- <0 . 5 gene set , as well as a PK regulator ( S3 Table ) . None of these eight PKs has been predicted to provide damage response functions and so we tested the RIT-seq prediction of this novel gene cohort . We first evaluated the sequence mapping for each gene and found consistently lower reads for six of the eight PK genes ( Fig 4C , S4A Fig ) in the Tet+ , MMS+ cells compared with the Tet+ , MMS-; for the two other genes ( Tb927 . 2 . 5230 and Tb927 . 6 . 4220 ) the average RIT-seq ratios ( Fig 4B ) masked variation in read depth between the replicates ( S4B Fig ) and so these PKs were not tested further . For the six remaining PKs , BSF cells carrying a single Tet-inducible RNAi target for each PK gene [91] were used to monitor growth before and after RNAi induction in the presence or absence of 0 . 0003% MMS ( Fig 5 , S4A Fig ) . For comparison , growth analysis was also conducted with the parental 2T1 cell line ( which does not induce dsRNA targeting any gene ) [92] . We also examined the T . brucei homologues of tousled-like kinase ( TLKs ) . Though T . brucei TLK1 and TLK2 did not display MMS+/MMS- ratios <0 . 5 ( 1 . 15 and 0 . 68 , respectively ) , metazoan TLKs contribute to DNA damage signalling and recovery [93 , 94] and conserved interactions have been described for the T . brucei TLKs ( phosphorylation by an aurora kinase , and phosphorylation of histone H3 and anti-silencing factor homologues ) [95] . As expected , Tet addition had no effect on growth of 2T1 cells , and addition of MMS slowed growth ( Fig 5 ) . Induction of RNAi that targeted both TLK1 and TLK2 caused slowing of growth , comparable to that seen after TLK1-specific RNAi in PCF cells [95] , and the growth reduction caused by MMS was exacerbated ( Fig 5 ) , indicating loss of one or both TLKs causes increased MMS sensitivity . For four PKs ( Fig 5 ) we translationally fused the endogenous gene with 12 copies of the myc epitope in the cognate RNAi cell and , in all cases , loss of tagged protein was seen 24 or 48 hrs after RNAi induction , with modest slowing of growth in two cases ( Tb927 . 10 . 7780 , KFR1; Tb927 . 9 . 6560 ) and little change in the others ( Tb927 . 3 . 3920 , AUK2; Tb927 . 2 . 1820 ) ( Fig 5 ) . For each of these PKs , addition of MMS after RNAi resulted in slower growth than in MMS-treated uninduced cells or RNAi-induced untreated cells , indicating loss of each PK sensitises BSF T . brucei to alkylation damage , consistent with the RIT-seq screen . Preliminary growth analysis of the final two PKs , Tb927 . 8 . 5890 and Tb927 . 8 . 5390 ( CRK4 ) , without evaluation of RNA or protein levels ( S4A Fig ) , provided no clear evidence for increased MMS sensitivity after Tet addition . It is possible these genes are false positives , but kinome RIT-seq ( below ) provides support for the whole-genome RIT-seq analysis of CRK4 . To ask if the four novel PKs and TLK1/2 act in genotoxic stress signalling , we evaluated levels of γH2A , which were low in untreated 2T1 cells but increased substantially after 48 hrs growth in 0 . 0003% MMS ( Fig 5 ) . TLK1/2 RNAi resulted in elevated γH2A levels in the absence of MMS , indicating that loss of this PK resulted in accumulation of nuclear genome damage . A similar but lesser increase in γH2A levels was seen after RNAi without MMS for KFR1 and AUK2 . The absence of a detectable increase in γH2A after RNAi against Tb927 . 9 . 6560 , which causes a notable growth defect ( Fig 5 ) , suggests H2A modification is not merely a result of defective BSF cell replication . Levels of γH2A after MMS exposure and RNAi were never lower than that seen in uninduced cells treated with MMS , and showed limited evidence for further increases , indicating that none of these PKs strongly influence the phosphorylation or dephosphorylation of H2A . To ask if the PKs have roles in regulating cell cycle progression , such as checkpoint signalling after damage , DNA was stained with DAPI in fixed cells from each RNAi cell line 24 and 48 hrs after RNAi , with or without exposure to 0 . 0003% MMS ( S5 Fig ) . Visualisation of the nuclear ( N ) and kinetoplast ( K ) DNA permits the approximate cell cycle stage of individual cells in a population to be assessed [96] . Only for TLK1/2 did RNAi without MMS cause a pronounced change in cell cycle distribution ( S5 Fig ) ; this change differed from the effect described following RNAi of TLK1 in PCF cells [95] in that accumulation of 1N2K ( S/G2 ) cells was not seen and , instead , cells emerged with aberrant N and K configurations , including 0N1K ‘zoids’ . For all of the PK cell lines , MMS treatment without induction of RNAi did not result in a detectable accumulation of cells in a specific cell cycle stage , but instead reduced numbers of 1N1K ( G1/S ) , 1N2K and 2N2K ( post-M ) cells were seen with an associated accumulation of cells with aberrant DNA content . Perhaps surprisingly , these data suggest BSF T . brucei cells continue to undergo cell division and DNA replication after MMS exposure , meaning they do not enact a clear checkpoint after treatment and mis-segregate their damaged genomes . Nonetheless , RNAi of each PK in the presence of MMS resulted in greater numbers of aberrant cells , consistent with increased MMS sensitivity . The whole genome MMS RIT-seq strategy we adopted is limited for two main reasons . First , we sampled at only one time point ( 5 days post-RNAi induction ) , meaning essential genes may be missed . Second , RNAi target number per gene is variable , meaning mapping coverage may be limited in some cases , such as for small genes . To address these limitations for PKs , we took advantage of the availability of a kinome-wide library of BSF T . brucei cells [91] , which allows Tet-induced RNAi using a single , defined RNAi target for each putative PK . 177 clonal RNAi cell lines , targeting 183 PKs , were pooled to allow kinome-wide MMS RIT-seq . The pooled cells were first inoculated at a density of 1 x 105 cells . ml-1 and grown for 24 hrs without or with addition of Tet , providing a control population and an RNAi-induced population , respectively ( Fig 6A ) . The two populations were then each split into three and grown without addition of MMS , or with the addition of 0 . 0002% or 0 . 0003% MMS . The six resulting populations were all grown for a further four days and genomic DNA prepared each day . To determine the abundance of PK-targeting cells in the populations and at the increasing time points , limited cycle PCR was performed from the DNA preparations using primers that amplify each PK RNAi target . The PCR reactions were then sequenced and mapped to a minimal genome , equivalent to the whole-genome RIT-seq but here limited to the PK RNAi targets . Normalised MMS+/MMS- ratios for each day and at both MMS concentrations are shown for every PK gene in S4 Table , while genes that show , after RNAi , reduced reads in the presence of MMS are highlighted in Fig 6B . The advantage of the kinome RIT-seq was most apparent in the ability to follow changes in PK gene levels with time . As shown in Fig 6B , 22 genes followed a pattern of decreasing MMS+/MMS- ratios from days two to five , and greater read losses at 0 . 0003% MMS compared with 0 . 0002%: eight genes ( ‘no loss of fitness’ in Fig 6B ) registered no significant fitness cost after RNAi , as judged by unchanged read levels in the Tet+ , MMS- control samples; for 14 genes ( ‘loss of fitness’ in Fig 6B ) , reads diminished with time in the same controls , indicating loss of fitness after RNAi . Three further genes ( ‘weak evidence’ in Fig 6B ) showed some evidence for increased sensitivity to MMS after RNAi , but with less clear time dependence . The kinome-focused MMS RIT-seq revealed two things: confirmation of the whole-genome RIT-seq , and an expanded repertoire of MMS damage response PKs . Amongst the eight PKs with MMS+/MMS- ratios <0 . 5 in the whole genome RIT-seq , three were identified in the kinome screen with MMS+/MMS- ratios of <0 . 5 ( Fig 6B ) . Two ( AUK2 and Tb927 . 2 . 1820 ) were validated by independent RNAi ( Fig 5 ) , while the third ( Tb927 . 8 . 5950 ) was only subject to preliminary growth analysis ( S4A Fig ) , but its presence in both screens strengthens the suggestion it is an MMS damage response PK . Two further whole-genome RIT-seq PKs ( Tb927 . 2 . 5230 ( CRK4 ) , Tb927 . 6 . 4220 ) were not pursued due to inconsistent mapping ( S4B Fig ) , while preliminary RNAi of one other ( Tb927 . 8 . 5390; S4A Fig ) was not supportive of an MMS damage response function . Intriguingly , though each of these PKs did not display MMS+/MMS- ratios <0 . 5 in the kinome screen , all displayed modest loss of reads in the MMS-treated cells relative to the untreated , an effect that increased with time and at the higher MMS level ( S4 Table ) . The kinome RIT-seq also clearly revealed TLK1/2 as showing MMS-specific read losses after RNAi . Overall , therefore , seven of nine PKs considered to this point showed good correspondence between the whole-genome and kinome-focused RIT-seq data , though only three have been validated by targeted analyses . Two whole-genome RIT-seq PKs , KFR1 and Tb927 . 9 . 6560 , showed no evidence for MMS-specific read losses in the kinome RIT-seq ( S4 Table ) . The basis for this discord is unclear , since independent RNAi validated both PKs as MMS damage response factors ( Fig 5 ) . Within the expanded repertoire of putative MMS damage response PKs predicted by the kinome RIT-seq ( Fig 6B ) , the putative T . brucei homologues of ATM , ATR and TOR4 were recovered ( the latter two showing evidence for loss of fitness , consistent with growth analysis after targeted RNAi ) [59 , 91] . None of these PKs have been examined for a role in T . brucei DNA repair or the MMS damage response , but such functions are consistent with work in other eukaryotes [29] . To test the wider kinome RIT-seq predictions , we performed targeted RNAi for seven PKs ( Fig 7 , S6 Fig ) . Preliminary growth curves suggested RNAi against only two PKs ( S6B Fig ) did not result in increased MMS sensitivity , whereas growth of the five others ( Fig 7 , S6A Fig ) , spanning four functional PK classes , was slower after RNAi in the presence of 0 . 0003% MMS relative to growth in the absence of damage or in the presence of MMS without RNAi . Addition of a 12myc tag to four of the PKs demonstrated loss of protein after RNAi ( Fig 7 ) , though we have so far been unable to similarly tag one PK ( Tb927 . 11 . 7010 ) ( S6A Fig ) , which we therefore did not analyse further . Only for Tb927 . 10 . 5410 ( MPK2 ) did slowed growth following RNAi without MMS clearly mirror the RIT-seq predicted loss of fitness ( Fig 7 ) , resulting in a pronounced accumulation of aberrant cells that was not seen following RNAi against the three other PKs ( S7 Fig ) . To ask if the PKs might act in genome maintenance , we examined γH2A levels ( Fig 7 ) . RNAi against none of the PKs abrogated the MMS-dependent increase in γH2A signal , indicating none mediate this phosphorylation . However , for Tb927 . 6 . 3110 ( CRK11 ) [97] , γH2A levels increased after RNAi without MMS , despite the lack of a growth defect , and the signal was higher after RNAi induction and MMS treatment than in MMS-treated cells without RNAi induction , suggesting loss of this PK causes accumulation of nuclear DNA damage ( Fig 7 ) . No such effects were seen after RNAi against Tb927 . 10 . 5410 , which resulted in impaired growth ( Fig 7 ) , nor Tb927 . 11 . 1180 ( CRK6 ) [98] , where some accumulation of aberrant cells was seen ( S7 Fig ) . Finally , though the RIT-seq and growth analysis suggest Tb927 . 7 . 690 ( which encodes a predicted CMGC/SRPK class PK ) is non-essential , further RNAi data indicate an important role in T . brucei survival in mice [99] . Tb927 . 3 . 3920 encodes AUK2 , one of three predicted T . brucei aurora kinases ( AUKs ) [100] . The presence of three AUKs in a single-celled eukaryote is unusual , since whereas mammals have three ( AUKA , AUKB and AUKC ) , yeast and Dictyostelium discoideum have a single AUK . Mammalian AUKA and AUKB have important but distinct roles in mitosis and cytokinesis by monitoring and contributing to centrosome function , microtubule attachment to the centromere and chromosome segregation , while AUKC appears to act during meiosis [101] . Functional studies in T . brucei have focused on AUK1 , which is essential , provides AUKB-like functions [102 , 103] and is considered a drug target [104 , 105] , building on anti-cancer compounds that target AUKs . Why kinetoplastids express two further AUKs , and whether they might also be targets for chemotherapy , is unclear . RNAi of AUK2 had little effect on BSF T . brucei growth ( Fig 5 , S4 Table ) , suggesting the PK is not essential in vitro . To test this , null mutants were generated in BSF cells by replacing the two allelic ORFs with antibiotic resistance markers ( S8A–S8C Fig ) . Though viable , auk2 null ( -/- ) mutants displayed significantly impaired growth relative to wild type ( WT ) cells in vitro ( Fig 8 ) . Furthermore , a significant increase ( ~6 fold ) in cells with aberrant N-K ratios was seen in the -/- mutants relative to WT ( Fig 8E ) or heterozygous cells ( +/- ) ( S8D Fig ) , with a range of abnormal DNA configurations observed ( S8E Fig ) . Growth of auk2-/- mutants was significantly more impaired than WT cells in the presence of MMS ( Fig 8A ) , consistent with the AUK2 RIT-seq and RNAi data . Indeed , MMS sensitivity after AUK2 loss appears to reflect a wider role for this PK in response to genotoxic damage , since the auk2-/- mutants also grew more slowly than WT cells in the presence of phleomycin or hydroxyurea , and after exposure to UV ( Fig 8B–8D , S9 Fig ) . To ask if AUK2 acts in the T . brucei DDR , levels of γH2A were assessed by western blot , revealing a 2 . 5-fold increased expression in two null mutant clones relative to WT ( Fig 9A , S10A Fig ) ; indeed , immunofluorescence imaging indicted greater numbers of -/- cells than WT displayed nuclear γH2A signal ( S10B Fig ) . To explore this increased endogenous damage further , indirect immunofluorescence was performed to examine localisation of RAD51 , a factor that binds ssDNA at a DNA break , which can be observed as localisation to discrete sub-nuclear foci . ~1% of WT cells displayed RAD51 foci ( Fig 9B ) , consistent with previous reports [47] , but this basal level increased to 6–7% in the auk2-/- mutants . Together , these data show loss of AUK2 affects integrity of the T . brucei nuclear genome , impedes survival following exposure to a range of genotoxic agents and impedes completion of the BSF cell cycle . To scrutinise AUK2 function further , cell and nuclear morphology of the auk2-/- mutants was examined . The cell body and the mitotic spindle in fixed WT and mutant cells were visualised by staining with anti-tubulin KMX-1 antiserum [106] , and the N- and KDNA were stained with DAPI . Only ~4% of WT cells deviated from the typical T . brucei BSF shape , a proportion that increased to ~35% of the auk2-/- population , a ~9-fold increase that closely mirrored the increased numbers of null mutants with aberrant DNA content ( Fig 8E ) . The predominant defect seen in WT cells was an enlarged , unclassifiable ( ‘aberrant’ ) cell morphology ( ~85% of aberrant cells ) ( Fig 10A ) . In contrast , ~25% of the aberrant auk2-/- cells displayed a characteristic ‘rounded’ morphology ( Fig 10A and 10B ) , akin to defects reported following AUK1 RNAi silencing [103] . Increased levels of nuclear defects were also observed in the auk2-/- mutants . Electron microscopy ( Fig 10C ) revealed mutants with aberrant nuclear membrane organisation , including the presence of nuclear ‘blebs’ ( which were seen in ~20% of auk2-/- mutants , a ~10-fold increase relative to WT; S11A Fig ) . Furthermore , the number of 1N2K cells with a detectable mitotic spindle was reduced by ~50% in the auk2-/- cells relative to WT ( S11B Fig ) . Together , these phenotypes suggest loss of AUK2 results in impaired nuclear architecture and genome division , perhaps because of failure to enact appropriate damage checkpoints from G2 to cytokinesis . To localise AUK2 , 12 copies of the myc epitope were fused to the C-terminus of the protein by targeting the intact allele in AUK2+/- cells ( Fig 11A ) . Unaltered growth of the resulting AUK2+/-::12myc cells relative to WT or AUK2+/- cells suggested expression of the tagged protein did not compromise function ( S12A Fig ) . Indirect immunofluorescence with anti-myc antiserum revealed an exclusively nuclear signal ( Fig 11B ) , though in ~10% of 1N1K cells no staining could be detected ( S12B Fig ) . Structure illuminated super-resolution microscopy ( Fig 11C ) and 3D modelling ( Fig 11D ) revealed that AUK2-12myc localisation or expression is dynamic , with puncta seen throughout the nucleus in 1N1K cells and the signal relocalising to the centre of the nucleus in 1N2K cells . Consistent with dynamism , structure illumination microscopy could not resolve any localisation in 2N2K cells ( S12D Fig ) and myc signal varied across the cell cycle ( S12C Fig ) . Collectively these data establish AUK2 as having BSF nuclear genome maintenance functions , potentially acting during replication and mitosis . The non-essentiality of AUK2 in vitro suggests a subservient or distinct function from AUK1 , though recent data suggest AUK2 may be critical during growth in mice [99] . In this work we describe the first synthetic-lethality whole genome and protein kinase-focussed RIT-seq screens applied to understand damage response pathways in T . brucei . MMS RIT-seq revealed multiple previously unexamined pathways that allow T . brucei to survive alkylation damage , with considerable overlap in the number and character of these pathways relative to D . melanogaster and yeast . Many of the MMS damage response pathways act in T . brucei genome maintenance , including repair , replication and telomere protection , but even within these well characterised pathways we reveal unexplored repair activities , including novel DNA helicases and translesion DNA polymerases . In addition , we reveal many putative MMS damage response genes that are currently annotated ‘hypothetical’ , raising the possibility that T . brucei or kinetoplastid-specific survival functions are present . Finally , this study predicts ~30 PKs whose loss sensitises BSF T . brucei to MMS exposure . This number represents ~16% of the T . brucei kinome [107] and spans ~11 functional classes , suggesting widespread and unanticipated roles for PKs in responding to MMS damage . Of the PKs predicted from the screens , three are repair-associated PIKKs and one a repair-associated TLK , and we have validated eight further novel damage response PKs belonging to four classes , three of which assume greater importance to survival in mice [99] . Thus , our study uncovers a range of conserved and novel DNA repair factors , signalling factors and pathways that operate in trypanosomatids and highlights the flexibility of RNAi-based synthetic lethal screens for study of gene function in T . brucei .
BSF RNAi cell lines derived from the T . brucei strain 2T1 [108] were cultured at 37 oC in 5% CO2 in HMI-9 medium supplemented with 10% ( v/v ) tetracycline-free foetal calf serum ( Sigma-Aldrich ) and 1% ( v/v ) penicillin-streptomycin solution . Cell lines were maintained in 5 μg . ml-1 phleomycin and 5 μg . ml-1 hygromycin . Cells lines expressing myc tagged proteins were grown in 10 μg . ml-1 of blasticidin . For all other BSF cell lines derived from WT Lister 427 cells , HMI-9 medium was supplemented with 20% ( v/v ) foetal calf serum . Null mutants , heterozygote cell lines and heterozygote cell lines expressing tagged proteins were maintained in the appropriate drug-free medium for no longer than 4 weeks continuous culture . Endogenous epitope tagging of the genes was performed using PCR with the oligonucleotide primer sequences detailed in S5 Table . To N-terminally 12-myc tag Tb927 . 11 . 1180 , a modified pEnT6B construct [109]was used ( kindly provided by R . Devlin ) . Cloning was performed as described in Devlin et al . [47] . The remaining PKs were C-terminally tagged using the vector pNATx12myc [92] . The whole genome RIT-seq approach was adapted from the protocol described in [31] . Pooled RNAi target fragments were amplified from genomic DNA extracted from the T . brucei populations using primers LIB2f ( TAGCCCCTCGAGGGCCAGT ) , LIB2r ( GGAATTCGATATCAAGCTTGGC ) and 21 cycles at the following conditions: 95°C for 30 seconds , 57°C for 30 seconds , and 72°C for 90 seconds . The amplified PCR products ranged in size from 200 bp to 1 . 6 kbp , as evaluated by agarose gel electrophoresis ( S1 Fig ) . The PCR products were cleaned up using the Qiagen QIAquick PCR purification kit then enzymatically fragmented , size selected to ~220 bp and sequencing libraries constructed , following standard protocols for Thermo Scientific Ion Proton sequence library preparation . The RNAi fragment libraries were sequenced on a Thermo Scientific Ion Torrent Proton platform using the 200 base pair sequencing kit . For the kinome-focused RIT-seq , RNAi cell lines were generated as previously described [91] . RNAi lines were pooled , initially into 9 pools each containing 19–25 cell lines and frozen . These pools were then defrosted and further pooled to make a culture with all PK RNAi cells , which was again frozen . To perform the RIT-seq the whole kinome pool of cells was defrosted , grown for 24 h and diluted , in triplicate , to 1 x 105 cells . ml-1 in 100 ml . Each 100 ml culture was then split into two 50 ml flasks and grown for 24 h with or without addition of tetracycline ( 1 μg . ml-1 ) . The induced and uninduced control cultures were then again diluted to 1 x 105 cells . ml-1 and grown for 120 h , reducing cell density to 1x105 cells ml-1 every 24 h and sampling 1x107 cells daily for genomic DNA prior to dilution . At the start of the 120 h growth three parallel cultures were derived from each of the induced and uninduced cultures: one in which no MMS was added , one in which 0 . 0002% MMS was added to the medium , and one with 0 . 0003% MMS added . To recover the RNAi target sequences from the populations , a single universal primer ( 5’- TAATGCCAACTTTGTACAAA-3’ ) was used . The primer was barcoded with 14 different 6-nucleotide tags that permitted combining equal amounts of PCR products in a single sequencing sample . Reads were assigned to each experimental condition later in silico . 10 ng of genomic DNA obtained per sample was PCR- amplified in a 50 μl reaction using Q5 High-Fidelity DNA polymerase ( NEB , Ipswich , USA ) . The PCR program was: an initial 3 minutes at 98 oC , followed by 28 cycles of 10 seconds at 98 oC , 10 seconds at 64 oC and 30 seconds at 72 oC , with a final extension step at 72 oC for 10 minutes . PCR products were cleaned up with a Minelute PCR purification kit ( Qiagen , Venlo , Netherlands ) . Groups of 14 barcoded PCR products were pooled in a single sequencing sample , and 400 ng processed according to Illumina Miseq library protocols . To map the RNAi reads , 'virtual' chromosomes were generated in silico by concatenating sequences of interest ( e . g . the complete transcripts recorded in the TriTrypDB database for the whole genome approach or the 183 amplicons relevant to the kinome experiment ) , each separated by a buffer sequence of 15 random bases . The coordinates of each sequence were recorded and their artificial chromosome sequence location indexed for use in Bowtie2 ( short-read alignment software ) . The assignment of reads to particular experimental conditions was performed by use of the Illumina bar-coding methodology in the case of the genomic experiments , and a combination of the bar-coding methodology and the presence of primer specific hexamers in the case of the kinome experiment . Single end reads ( IonTorrent ) or the forward sense reads ( Illumina ) generated from each sample containing the RNAi insert were selected by the presence of a 9 base diagnostic tag [GCCAACTTT] , derived from the universal primer , allowing for 1 base mismatch ( insertion , deletion or substitution ) . Selected reads were then mapped to the artificial chromosome with Bowtie2 ( local mode alignment , default parameters ) . The “ . sam” format files thus generated were parsed and the coordinates to which the reads mapped were recorded . Mapped reads were assigned to their appropriate PK gene using indexes generated above . A read was assigned if it lay entirely within a sequence of interest , or overlapped the ends of such a sequence . In each replicate , accumulated read abundances were normalized by multiplying raw counts 106 times , dividing by the sum of total valid reads accepted for analysis in the whole sample and rounding to the next integer . For growth analysis of cell lines targeting individual PK genes , cell cultures were adjusted to 1 x 104 cells . ml-1 and the flask was split in two . To induce RNAi , tetracycline ( diluted in 50% ethanol ) was added ( final concentration of 1 μg . ml-1 ) to one flask . Both flasks were mixed and 1 . 2 ml of culture aliquoted into a well in a 24 well plate , assessing cell density over 72–96 h using a Neubauer improved haemocytometer . For UV exposure , cultures were set up and RNAi induced ( or not ) as described above for 24 hours , after which 2 ml of each culture was aliquoted into a 6 well plate and exposed to the required UV dose ( 1500 J/m2 ) using a Stratalinker UV Crosslinker 2400 ( Stratagene; the lid of the plate was removed during UV exposure ) . After UV exposure , 1 . 2 ml of each culture was aliquoted into a 24 well plate . To examine growth in other forms of damage , induced or uninduced cells were aliquoted into a 24 well plate as before and 0 . 0003% MMS ( from a 0 . 1% stock ) , 0 . 1 μg . ml-1 phleomycin ( from a 20 mg . ml-1 stock ) or 0 . 06 mM hydroxyurea ( from a 200 mM stock ) added to the 1 . 2 ml cultures . Cell density was plotted with the error bars showing SEM of three independent experiments , except in the case of the growth curve performed for Tb927 . 7 . 960 , which was performed twice . Statistical significance was assessed in Prism ( GraphPad , v . 5 ) using a Mann-Whitney U test or an unpaired t-test ( for Tb927 . 7 . 960 ) . For cell cycle analysis , cultures were adjusted to a density of 1 x 105 cells . ml-1 and split into two flasks , one of which was RNAi induced as described above . The flasks were further split in two and MMS ( to a concentration of 0 . 0003% ) was added to two of them ( induced and uninduced ) . Cells were harvested by centrifugation at the indicated time points following induction , fixed in 4% Paraformaldehyde ( PF ) and stained with DAPI ( see immunofluorescence ) . The ratio of N- and K-DNA was determined for over 200 cells/timepoint for three independent experiments . To evaluate levels of γH2A or myc-tagged proteins by western blotting , over 2 . 5 x 106 cells were harvested by centrifugation at 1620 g for 10 mins at room temperature . The supernatant was removed and the pellets re-suspended in an appropriate amount of 1x protein loading buffer ( PLB: 250 μl 4x NuPAGE LDS sample buffer [Invitrogen] , 750 μl 1x PBS and 25 μl β-mercaptoethanol ) to permit the loading of 2 . 5 x106 cells per 10 μl and denatured at 100 oC for 10 mins . Samples were stored at -20 oC until required . For high molecular weight proteins , 20 μl 2x Roche Complete Mini protease inhibitor cocktail tablets was added to the loading buffer . Cell lysates were separated by SDS-PAGE using the following NuPAGE Novex pre-cast gels: 4–12% Bis-Tris , 10% Bis-Tris , 12% Bis-Tris or 3–8% Tris-acetate gels . The appropriate gel was selected based on protein size and was run as per the manufacturer’s instructions . For blotting on to PVDF membrane ( Amersham Bio ) , proteins from the SDS-PAGE gel were transferred using a Mini Trans-Blot Cell ( Bio-Rad ) . Transfer was performed by electrophoresis at 100 V for 2 hrs or , for high molecular weight proteins , overnight at 4 oC . The membrane was incubated for 10 mins in the dark with Ponceau-S solution ( Sigma ) to confirm transfer of proteins had occurred . After transfer , membranes were washed once in 1x PBST ( PBS , 0 . 01% Tween-20 [Sigma] ) for 10 mins then incubated for 1 hr in blocking solution ( 1x PBST , 5% Milk powder [Marvel] ) or , if required , overnight at 4 oC . Next the membrane was rinsed for 10 mins in 1x PBST and placed in blocking buffer containing the required primary antisera for one hour ( rabbit antiserum recognising phosphorylated γH2A was used at a 1:1000 dilution; mouse anti-myc antiserum ( Millipore ) was used at 1:7000; mouse anti-EF1a ( Millipore ) was used at 1:20000 ) . The membrane was then rinsed once in 1x PBST for 20 mins and placed in blocking solution containing the appropriate secondary antisera for one hour ( HRP-conjugated goat anti-mouse antiserum was used at 1:3000 , and HRP-conjugated goat anti-rabbit antiserum was used at 1:5000; both ThermoFisher ) . After this , the membrane was washed in 1x PBST for 30 mins and SuperSignal West Pico Chemiluminescent Substrate ( Thermo-Fisher ) or ECL Prime Western Blotting Detection Reagent ( Amersham ) added and incubated in the dark for 5 mins . The membrane was then exposed to an X-ray film ( Kodak ) or an ECL Hyperfilm ( Amersham ) for ~1 sec to overnight and the film developed using a Kodak M-25-M X-omat processor . For western quantification , the following modifications were applied . Westerns were blocked in 5% milk powder in 1x PBS overnight at 4 oC . Chameleon Duo Pre-Stained Protein Ladder ( 2 μl; Li-Cor ) was loaded to confirm protein sizes . The following secondary antibodies were used: IRDye 680 goat anti-mouse and IRDye 800 goat anti-rabbit ( both 1:10000 , Li-Cor ) . Before imaging after the final 1x PBST wash , the membranes were subject to a final wash in 1x PBS . The images were captured using an Odyssey CLx Imager ( Li-Cor ) using the in-built software ( ImageStudio ) to obtain the intensities of each band . The fold change was calculated by normalising each sample to the loading control and calculating the relative fold change to the control sample . The numerical data were analysed using GraphPad Prism 5 . 0 . Heterozygous ( +/- ) and homozygous ( -/- ) mutants of auk2 were generated by replacing most of the gene’s ORF with a selective drug marker . Two modified versions of the pmtl23 plasmid ( gift , Marshall Stark , University of Glasgow ) , containing either the blasticidin or neomycin resistance genes , were used . Details of the cloning approach are described in [47] . To generate the knockout constructs , PCR was performed from T . brucei genomic DNA to amplify the 5’ or 3’ ORF flanks using primers 141 and 142 , and 143 and 144 , respectively ( S6 Table ) . RNA in the mutants was analysed by RT-PCR , amplifying a region of the ORF with primers 147 and 148 , or by qRT-PCR with primers OL31 and OL32 . RNA was extracted from cells using the Qiagen RNeasy kit , and cDNA synthesis was performed using random primers and the Primer Design Precision nanoScript Reverse Transcription kit ( Primer Design ) , according to manufacturer’s instructions . For qRT-PCR , each analysis was performed as a technical triplicate . Master mix was as follows ( prepared at 4 oC , but not in direct contact with ice ) : 12 . 5 μl SYBR Green PCR Master Mix ( Applied Biosystems ) , 5 μl RNase free ddH20 ( Qiagen ) , 2 . 5 μl of each primer ( 300 nM stock ) and 2 . 5 μl of the appropriate cDNA . The master mix was pipetted into a MicroAmp Optical 96-well reaction plate ( Thermo Fisher ) . Actin ( primers OL29 and OL30 ) were used as an endogenous control , and ddH20 ( RNase free ) was used as a negative control . AB 7500 RT PCR system thermocycler was used and conditions for all reactions were 50 oC for 2 min , 95 oC for 10 min , and 40 cycles of 95 oC for 15 sec followed by 60 oC for 1 min , with a final dissociation step of 95 oC for 15 secs , 60 oC for 1 min , 95 oC for 15 secs and , finally , 60 oC for 15 secs . The data was processed as detailed in the Applied Biosystems manual using the ddCt approach . For immunofluorescence and DAPI analysis , approximately 2x 106 cells were harvested by centrifugation ( 405 g for 10 mins ) . The pellet was washed in 1x PBS by centrifugation ( 405 g for 3 mins ) , the supernatant removed and the pellet re-suspended in ~50 μl 1xPBS . The cells were settled for 5 mins on a 12 well glass ( Menzel-Gläser ) slide treated with Poly-L-Lysine ( Sigma ) . A wax barrier was drawn around the wells using a PAP pen ( Life Technologies ) . The supernatant was removed and 25 μl 4% formaldehyde ( FA ) was added for 4 mins . The FA was then removed and the cells washed 3 times in 50 μl 1x PBS for 5 mins . To stain DNA , 5 μl of DAPI ( Southern Biotech ) was added to each well and incubated at room temperature for 4 mins . A coverslip was then added and sealed with nail varnish . Slides were stored in the dark at 4 oC . For immunofluorescence cells were permeabilised with 25 μl 1x PBS/Triton X-100 ( Thermo Scientific ) for 10 mins . To neutralise free -aldehyde groups , 100 mM glycine in PBS was added for 20 mins . The wells were then washed three times in 1x PBS for 5 mins . The wells were blocked for 1 hr with 25 μl blocking solution ( 1% BSA [Sigma] , 0 . 2% Tween-20 in 1 x PBS ) in a wet chamber . Afterwards , 25 μl of the required primary antiserum diluted in blocking solution was then added and incubated for 1 hr in a wet chamber: rabbit anti-RAD51 at 1:1000; rabbit anti-γH2A at 1:1000; and AlexaFluor 488 conjugated mouse-anti-myc ( Millipore ) at 1:500 . The wells were then washed 2 x with 1 x PBS for 5 mins . 25 μl of the appropriate secondary antisera ( always goat AlexaFluor 488 or 594 anti-mouse or anti-rabbit from Millipore at 1:1000 ) were added to each well and then incubated for 1 hr in a wet chamber , after which the cells were washed three times with 1x PBS for 5 mins . For immunofluorescence requiring anti-KMX-1 antiserum , blocking was performed for 1 hr in 25 μl PBS . The cells were then DAPI stained and the slides stored as described above . Standard images were captured on an Axioskop 2 ( Zeiss ) fluorescence microscope , using a 63 x DIC magnification lens and ZEN software package ( Zeiss ) . Alternatively , images were captured on an Olympus IX71 DeltaVision Core System ( Applied Precision , GW ) using a 1 . 40/100 x lens and acquired using the SoftWoRx suite 2 . 0 software ( Applied Precision , GE ) . Z-stacks were acquired of varying thickness ( no more than 10 μm ) ; images were de-convolved ( conservative ratio; 1024x1024 resolution ) by the SoftWoRx software . Super-resolution structure illuminated images were captured on an Elyra PS . 1 super resolution microscope ( Zeiss ) . Raw images were acquired using the provided ZEN Black Edition Imaging Software tool ( Zeiss ) . The images were then aligned to the channel alignment files generated on the day of imaging using the same software . All images were processed in ImageJ/Fiji ( http://fiji . sc/Fiji ) . For most images , excluding the ones used for quantification of the DAPI signal , both the contrast and brightness of the DAPI signal was enhanced to improve visualisation . For all images , the background was subtracted and suitable false colours were assigned to the fluorescence channels . Approximately 5 x106 cells were fixed in 2 . 5% glutaraldehyde and 4% PF in 0 . 1 M sodium cacodylate buffer ( pH 7 . 2 ) then post-fixed for 45 mins in 1% osmium tetroxide and 2 . 5% potassium ferrocyanide ( pH 7 . 3 ) in 0 . 1 M sodium cacodylate buffer in the dark . The cells were washed several times with 0 . 1 M cacodylate buffer and the samples stained ( en bloc ) with 2% aqueous uranyl acetate the dehydrated in acetone solutions ( 30 , 50 , 70 , 90 and 100% ) . The samples were then embedded in Epon resin and sectioned ( ultrathin sectioning ) . The samples were visualised on a Tecnai T20 transmission electron microscope ( FEI , Netherlands ) . Sequences used in the mapping have been deposited in the European Nucleotide Archive ( accession numbers PRJEB19516 and PRJEB19634; http://www . ebi . ac . uk/ena ) . RITseq data will be hosted at TriTryDB ( http://tritrypdb . org/tritrypdb/ ) in an upcoming release . | Damage to the genome is a universal threat to life . Though the repair pathways used to tackle damage can be widely conserved , lineage-specific specialisations are found , reflecting the differing life styles of extant organisms . Using RNAi coupled with next generation sequencing we have screened for genes that are important for growth of Trypanosoma brucei , a diverged eukaryotic microbe and important parasite , in the presence of alkylation damage caused by methyl methanesulphonate . We reveal both repair pathway conservation relative to characterised eukaryotes and specialisation , including uncharacterised roles for translesion DNA polymerases , DNA helicases and chromatin factors . Furthermore , we demonstrate that loss of around 15% of T . brucei protein kinases sensitises the parasites to alkylation , indicating phosphorylation signalling plays widespread and under-investigated roles in the damage response pathways of eukaryotes . | [
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] | 2017 | Genome-wide and protein kinase-focused RNAi screens reveal conserved and novel damage response pathways in Trypanosoma brucei |
The development of a vaccine against dengue faces unique challenges , including the complexity of the immune responses to the four antigenically distinct serotypes . Genome-wide transcriptional profiling provides insight into the pathways and molecular features that underlie responses to immune system stimulation , and may facilitate predictions of immune protection . In this study , we measured early transcriptional responses in the peripheral blood of cynomolgus macaques following vaccination with a live , attenuated tetravalent dengue vaccine candidate , TDV , which is based on a DENV-2 backbone . Different doses and routes of vaccine administration were used , and viral load and neutralizing antibody titers were measured at different time-points following vaccination . All 30 vaccinated animals developed a neutralizing antibody response to each of the four dengue serotypes , and only 3 of these animals had detectable serum viral RNA after challenge with wild-type dengue virus ( DENV ) , suggesting protection of vaccinated animals to DENV infection . The vaccine induced statistically significant changes in 595 gene transcripts on days 1 , 3 , 5 and 7 as compared with baseline and placebo-treated animals . Genes involved in the type I interferon ( IFN ) response , including IFI44 , DDX58 , MX1 and OASL , exhibited the highest fold-change in transcript abundance , and this response was strongest following double dose and subcutaneous ( versus intradermal ) vaccine administration . In addition , modules of genes involved in antigen presentation , dendritic cell activation , and T cell activation and signaling were enriched following vaccination . Increased abundance of gene transcripts related to T cell activation on day 5 , and the type I IFN response on day 7 , were significantly correlated with the development of high neutralizing antibody titers on day 30 . These results suggest that early transcriptional responses may be predictive of development of adaptive immunity to TDV vaccination in cynomolgus macaques , and will inform studies of human responses to dengue vaccines .
Over the last 50 years , the incidence of dengue has increased 30-fold , and now more than half of the world’s population is at risk of dengue virus ( DENV ) infection [1] . Transmitted by Aedes mosquitos , DENV has become the leading cause of mosquito-borne viral infections worldwide , with an estimated 390 million infections occurring each year [1] . The outcome of infection ranges from an asymptomatic state to classic dengue fever ( DF ) and severe and potentially life-threatening dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) . Each of the four antigenically distinct serotypes of dengue virus ( DENV1 –DENV4 ) is capable of causing severe disease . While infection with one serotype provides long-lasting protection against re-infection with that serotype , cross-protective immunity is temporary and lasts only several months [2] . Furthermore , secondary infection with a heterologous serotype greatly increases the risk of developing severe disease [3 , 4] . While there is currently no licensed vaccine against dengue , there are several dengue vaccine candidates in development [5] . Takeda Vaccines’ Tetravalent Dengue Vaccine Candidate ( TDV ) ( formerly DENVax , Inviragen ) consists of a live attenuated DENV-2 strain ( TDV-2 ) and three chimeric viruses containing the prM and E protein genes of DENV-1 , -3 and -4 expressed in the context of the TDV-2 genome backbone ( TDV-1 , TDV-3 , and TDV-4 , respectively ) [6 , 7] . TDV has been shown to be immunogenic and efficacious in animal models [8–10] , generally well tolerated in humans [11] , and is currently in phase 2 clinical trials . Studies of dengue infection have revealed unique transcriptional signatures during the acute phase of infection that are associated with subsequent disease severity [12–16] . A recent study examined the role of the innate immune response in modulating the humoral immune response [16] . Understanding the mechanisms underlying the development of protection against dengue , and responses to dengue vaccination , may be useful in the further development of an effective vaccine against dengue . Host genome-wide transcriptional profiling provides a means to identify changes in gene expression occurring immediately following vaccination that may play a role in the development of protective immunity . This approach has revealed useful , clinically-relevant signatures following immunization with a variety of different vaccines [17–22] . In this study , we used transcriptional profiling to characterize early changes in gene expression in peripheral blood cells of cynomolgus macaques following vaccination with TDV , involving different doses ( single dose or double dose on day 0 ) , and routes ( subcutaneous or intradermal ) of vaccine administration . We compared changes in transcript abundance following TDV vaccination with those following infection with wild-type ( wt ) DENV in cynomolgus macaques . Gene transcript abundances were correlated with measurements of viral load and neutralizing antibody titer to identify markers predictive of vaccine immunogenicity .
TDV was generated from cDNA clone-derived DENV-2 VV45R virus , based on the DENV-2 PDK-53 genome [10] , and from three chimeric strains based on DENV-2 PDK-53 expressing the prM and E genes of wt DENV-1 , DENV-3 or DENV-4 , as described previously [10 , 8] . For the neutralization antibody assays , we used the wt viruses from which the prM and E genes for each TDV virus were derived [9 , 11 , 23] . DENV-2 New Guinea C and DENV-4 Dominica/81 were used for viral challenge , generously provided by Dr . Stephen Whitehead ( US National Institutes of Health , Bethesda , MD ) . Animal work was conducted at the Charmany Instructional Facility of the School of Veterinary Medicine , University of Wisconsin-Madison , and at the Wisconsin National Primate Research Center . All animal procedures were approved by the University of Wisconsin-Madison’s Graduate School Animal Care and Use Committee , and the protocol , #G00634 , in which these procedures are described , was approved on 10/22/2013 . The regulations/guidelines to which animal care and the animal use protocol adhered are “The Guide for the Care and Use Of Laboratory Animals , 8th Edition”; the United States Department of Agriculture ( USDA ) Animal Welfare Act and Animal Welfare Regulations; US Public Health Service Policy on Humane Care and Use of Laboratory Animals; US Government Principles for the Utilization and Care of Vertebrate Animals Used in Testing , Research , and Training; and the USDA Policy Manual . Monkeys were singly housed in standard stainless steel primate cages ( Suburban Surgical , Chicago IL ) . All animals had visual and auditory contact with each other in the same room . They were fed twice daily with commercial chow ( Harlan Teklad #2050 , 20% protein Primate Diet , Madison , WI ) and given a variety of fruit in the afternoons . In addition , we provided foraging activities and physical environmental enrichment at least weekly for both activities . Housing rooms were maintained at 65–75°F , 30–70% humidity and on a 12:12 light–dark cycle ( ON: 0600 , OFF: 1800 ) . Standard veterinary analgesia was available to animals if necessary with either NSAIDs or opioid derivative drugs such as buprenorphine . Thirty-five adult male , DENV-seronegative cynomolgus macaques from Vietnam were placed in quarantine for 30 days prior to the start of the study . Five groups of animals ( n = 6 animals per group ) received TDV either subcutaneously ( SC ) in 0 . 5 mL inocula or intradermally ( ID ) in 0 . 1 mL inocula using a needle-free injector ( PharmaJet device ) or needle and syringe ( N&S ) , as outlined in Table 1 . Detailed experimental methods appear in S1 Text . Viral RNA in serum samples was measured using a quantitative reverse transcription-polymerase chain reaction ( qRT-PCR ) as described previously [9 , 23] . The limit of detection for the qRT-PCR of 3 . 6 log10 copies/mL was determined for each viral RNA standard by creating a standard curve consisting of nine replicates per dilution . Heat-inactivated serum samples ( 56°C for 30 min ) were tested for neutralizing activity using a viral immunofocus reduction microneutralization assay with an ELISpot reader ( AID , San Diego , CA ) , as previously described [23] . Fifty percent of the average number of foci in the negative control serum defined the cut-off point ( PRNT50 ) . The serum dilution closest to the cut-off was recorded as the reciprocal neutralizing titer . Total RNA was extracted using the PAXGene Blood RNA kits ( Qiagen , Valencia , CA ) , and 500 ng of total RNA was amplified using the TargetAmp Aminoallyl aRNA amplification kit ( Epicentre , Madison , CA ) . 8 ug of amplified RNA and 5 ug Universal Human Reference aRNA ( Stratagene , La Jolla , CA ) were labeled using Cy5 and Cy3 dyes , respectively , and hybridized to Human Exonic Evidence Based Oligonucleotide ( HEEBO ) microarrays . Details of the protocol have been described previously [15] . HEEBO microarrays , containing 44 , 544 probes , were printed by the Stanford Functional Genomics Core Facility . A detailed description of this probe set can be found at ( http://microarray . org/sfgf/heebo . do ) . Microarray data were submitted to the Princeton University MicroArray database for subsequent analyses . Previous studies have shown that HEEBO arrays can accurately measure the abundance of gene transcripts in nonhuman primates [24–26] . Data were normalized by local background subtraction and a global mean normalization using regression correlation . Data were filtered to exclude probes that did not demonstrate a regression correlation of ≥0 . 6 between Cy5 and Cy3 signal over the pixels comprising the array element , and intensity/background ratio >2 in at least one channel . The microarray data are available at Gene Expression Omnibus ( GEO accession number GSE72430 ) . Changes in transcript abundance were calculated by subtracting the log2 abundance value at baseline ( average of days -11 and -2 ) from each subsequent time-point ( days 1 , 3 , 5 , 7 , etc . ) for each animal . Baseline-transformed data were used for comparison between the different groups . Significance Analysis of Microarrays ( SAM ) was used to identify significantly differentially expressed genes between predetermined groups; genes and their transcripts were considered significant at a false-discovery rate ( FDR ) <5% and fold-change ≥1 . 3 [27] . The fold-change reported is the difference in the average relative abundance between each of the two groups in the comparison . Gene enrichment analysis was performed using the Database for Annotation , Visualization and Integrated Discovery ( DAVID ) [28] . DAVID keywords included Gene Ontology ( GO ) terms , biological pathways from the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) , and Swiss-Prot keywords . Keywords were considered significant when p<0 . 05 after Benjamini-Hochberg correction for multiple testing . Pathway analysis was performed using Gene Set Enrichment Analysis ( Broad Institute ) [29] and Blood Transcription Modules ( BTMs ) [17] on genes pre-ranked by SAM score or Spearman correlation coefficient . BTMs were considered significant at a FDR<5% . Differences in median transcript abundance levels of specific genes between immunization groups were tested using the Mann-Whitney U test . Spearman correlation was used to correlate relative transcript abundance of 15 , 705 variable genes ( filtered for probes with an intensity/background >2 and present in at least 80% of the samples ) with peak viral RNA abundance and duration of detectable viral RNA after vaccination , and DENV-2 specific neutralizing antibody titer and median neutralizing antibody titer ( median PRNT50 of the 4 serotypes ) on day 30 .
To characterize the overall response to TDV , we compared changes in transcript abundance by day in all 30 vaccinated animals against their pre-vaccinated baseline values . Five hundred and ninety-five genes were significantly differentially expressed ( DE ) in vaccinated animals compared with their own baseline on at least one day during the first week following vaccination with TDV ( FDR<5% , fold-change≥1 . 3 ) . Forty-six additional genes were differentially expressed in an unpaired SAM comparison between animals vaccinated with TDV and those that received the placebo ( n = 5 ) ; these genes were included in this list ( S1 and S2 Tables ) . A more stringent fold-change cut-off of 1 . 5 resulted in 217 DE genes ( Fig 1A ) , and so we decided to use the 1 . 3-fold change cut-off in order to include a greater number of genes in the functional analysis . Functional annotation using DAVID identified GO terms associated with these gene sets that increased and decreased in abundance by day ( S1 Table ) . Genes associated with the GO term “antiviral defense” ( including DDX58 , EIF2AK2 , ISG15 , IRF7 , MX1 , IFI44 , STAT1 ) increased in abundance on day 1 , but the association with this GO term was strongest on day 5 along with additional terms related to the response to vaccination ( Fig 1B ) . Genes associated with cell cycle and transcription ( including CDC73 , CCAR1 , CNOT7 , CREBZF , ZEB1 , and FOS ) decreased in abundance on days 1 and 3 . No GO terms were significantly enriched on day 7 . Transcripts for genes involved in viral recognition ( DDX58 ( RIG-I ) and EIF2AK2 ( PKR ) ) , the type I interferon ( IFN ) response ( IRF7 , OAS2 and OASL ) , the antiviral response ( MX1 ) , regulation of cytokine signaling ( STAT1 ) , and apoptosis ( XAF1 ) exhibited the highest fold-changes in abundance compared with baseline ( maximum fold-change 2 . 4 ) ( S1 Table ) . The IFN stimulated gene ( ISG ) ISG15 was up-regulated following TDV vaccination and is typically induced in response to type I IFN [30] . Secreted ISG15 acts on T and natural killer ( NK ) lymphocytes , in which it induces IFN-γ production , and has been shown to inhibit viral replication and suppress particle release of DENV-2 [30] . Transcripts for TNFSF13B , a gene associated with B cell activation [31] also increased in abundance following TDV vaccination . To complement the gene-level analysis , we applied Gene Set Enrichment Analysis ( GSEA ) using the 334 Blood Transcription Modules ( BTMs ) constructed from publicly available microarray data specific to human blood [17] , supplemented with 4 additional modules comprising cytokine-induced gene sets identified in our previous work [32] ( S3 Table ) . Blood transcription modules also include sets of cell-type specific genes , which indicate the cell types that may contribute to the response based on the most differentially expressed genes [17] . Following vaccination , 86 BTMs were enriched for genes that increased in abundance ( positively enriched ) , and 34 BTMs were enriched for genes that decreased in abundance ( negatively enriched ) ( FDR<5% ) ( Fig 2 , S4 Table ) . Positively enriched modules included those with functions associated with the innate immune response to viruses , antigen presentation , and activation of T cells , dendritic cells ( DCs ) and platelets . There was significant enrichment of genes specific to T cells , NK cells , neutrophils , monocytes and DCs ( Fig 2 , S4 Table ) . Negatively enriched modules were associated with mitosis , other aspects of the cell cycle and cell division , and inositol signaling . Inositol signaling has been shown to control the amplitude of type I IFN secretion and pDC activation , and usually inhibits cell activation [33] . The transcriptional response in animals that received TDV by ID route ( Groups 1 and 2 ) differed from those that received TDV via SC route ( Groups 4 and 5 ) primarily by the temporal pattern of transcript abundance . Following ID immunization , most transcripts increased in abundance on day 1 ( n = 142 ) and then decreased by day through day 7 ( n = 0 ) . On day 1 there was an increase in abundance of genes involved in lymphocyte activation ( THY1/CD90 , CORO1A , LST1 , STXBP2 , TNFSF13 and TNSF13B ) , and viral recognition and the antiviral response ( DDX58 , EIF2AK2 , ISG15 , IRF7 , IRF9 , IFI44 , STAT1 , OASL , OAS2 , IFITM3 and MX1 ) , which persisted on days 3 and 5 . Following SC immunization , transcript abundance increased over time from day 1 ( n = 18 ) , 3 ( n = 157 ) , to 5 ( n = 1460 ) , and then decreased by day 7 ( n = 17 ) . On day 5 following SC immunization , there was an increase in abundance of genes related to T and B cell activation and differentiation ( including BCL2 , CD28 , CD47 , IL15 , TNFSF13B and KLRK1 ) , regulation of cytokine production , and the JAK/STAT signaling cascade ( JAK2 , IL6ST , STAT1 , STAT2 , STAT4 , and SOC2S ) . On day 7 , only genes involved in viral recognition and the antiviral response were enriched ( DDX58 , EIF2AK2 , ISG15 , IFI44 , STAT1 , OAS2 , OASL , XAF1 and MX1 ) . No significant differences in gene enrichment were observed by direct comparisons of relative transcript abundance levels between animals that received ID vaccination versus SC vaccination . In all groups , the highest fold-change in gene abundance following vaccination was observed for genes involved in the antiviral and type I IFN response . To investigate how the observed responses were modulated by dose and route of vaccine administration we created a master list of 379 genes involved in the antiviral/type I IFN response by combining the genes present in relevant BTMs . Overall , 38 of these genes were significantly differentially expressed following TDV vaccination ( Fig 3A ) . A greater number of these genes were significantly differentially expressed in the groups of animals that received SC immunization ( single or double dose ) compared with those that received ID immunization ( S1 Fig ) , suggesting that SC immunization results in a broader antiviral/type I IFN response than ID immunization . To evaluate whether dose or route of vaccine administration altered the strength of the antiviral/type I IFN response , we compared expression of each gene by vaccination group over time ( Mann-Whitney U test , p<0 . 05 ) . Abundances for 11 gene transcripts differed significantly between SC and ID vaccination ( single and double dose groups combined ) ; the majority had higher levels following SC vaccination ( Fig 3A ) . For dose , we focused on the SC double ( SCd ) and single ( SCs ) dose groups ( groups 4 and 5 ) since a greater number of changes in transcript abundance were observed in these groups compared with the ID groups ( groups 1–3 ) , and because SC administration is a route used for human trials of TDV . Following double dose SC vaccination , 20 genes had significantly higher transcript abundances during at least one time-point compared with single dose SC vaccination ( Fig 3A and 3B ) . Most of these differences occurred on day 7 ( 15 genes ) , suggesting that increasing the dose of TDV elicits a stronger and more prolonged response for these genes . Wild-type ( wt ) challenge of placebo animals with DENV-2 ( 3 animals ) or DENV-4 ( 1 animal ) by SC inoculation led to significant changes in the abundance of 135 genes over time ( Fig 4A ) . The smaller number of significant genes may reflect the smaller sample size of 4 animals . Unsupervised hierarchical clustering of these genes revealed 3 clusters ( Fig 4B , S5 Table ) . Clusters 1 and 2 were both significantly enriched for genes we previously identified as induced by type I IFN ( p<1E-10 for each cluster ) [32] , though only genes from cluster 1 were significantly associated with GO terms , including “response to virus” ( p = 1 . 2E-8 ) , “immune response” ( p = 0 . 0001 ) , and “GTP-binding” ( p = 0 . 04 ) . Cluster 3 was not associated with any GO terms . While abundance of transcripts in clusters 1 and 2 increased following both infection with wt DENV and vaccination , the magnitude of response was much greater following wt virus infection , as seen in median fold-change over time of transcripts for genes in each cluster ( Fig 4B ) . Fold-change peaked at 19 . 9 ( ISG15 ) following wt DENV infection compared with 5 . 7 ( IFI44 ) following double dose SC TDV vaccination . Wt virus caused a decrease in transcript abundance for genes in Cluster 3 . Proteins expressed by genes in this cluster include IL1-R2 , a negative regulator of IL-1 signaling [34] , and CRISPLD2 , a serum protein produced by monocytes , NK cells , and T cells in response to stimulation by LPS and other PAMPs , including poly ( I:C ) [35 , 36] . Over half of the animals ( 17/30 ) had detectable viral RNA ( vRNA ) ( range: 3 . 8–5 . 6 log10 copies/mL ) between days 5 and 14 following primary immunization ( mean duration , 5 days ) ( Table 2 ) . Only TDV-2 virus RNA was detected in all cases , consistent with previous studies of TDV immunization [9 , 11] . Most animals that received TDV by SC ( single and double dose ) and ID ( double dose only ) routes had vRNA , compared with only 1 animal that received TDV by single dose ID route ( Table 2 ) . After challenge with wt DENV , only 3 of the vaccinated animals developed detectable vRNA , compared with all placebo ( sham-immunized ) animals ( range: 3 . 7–5 . 8 log10 copies/mL; mean duration , 4 days ) , suggesting that TDV protected against wt DENV challenge ( Table 2 ) . Correlation of transcriptional responses ( relative transcript abundance by day ) to the duration and peak of viral load revealed significant correlations with genes involved in B and T cell activation and differentiation on day 5 post-immunization . Genes included IRF4 ( Spearman’s ρ = 0 . 70 , p = 1 . 76E-05 ) and ITK ( Spearman’s ρ = 0 . 58 , p = 0 . 0009 ) , which encode intracellular tyrosine kinases involved in the regulation of T cell development and differentiation , and CCR6 ( Spearman’s ρ = 0 . 52 , p = 0 . 006 ) , which encodes a beta chemokine receptor important for B-lineage maturation and antigen-driven B-cell differentiation . Both the duration and peak of TDV-2 virus load were positively correlated with the development of higher DENV-2 neutralizing antibody titers on day 30 , when they were highest ( r2 = 0 . 42 and 0 . 40 , respectively ) ( S2 Fig ) . All animals vaccinated with TDV developed a neutralizing antibody response to each of the four dengue serotypes ( S3 Fig ) . Median neutralizing antibody titer ( median PRNT50 ) was calculated to represent an overall response to all serotypes of the tetravalent vaccine . Median PRNT50 differed between the groups , and was highest for animals administered SC TDV ( Fig 5A ) . Median titers were higher in animals that received double dose TDV rather than a single dose before boost ( days 30 and 53 ) , but were similar post-boost ( days 75 and 88 ) , and did not change significantly after challenge with wt DENV ( Fig 5A and S3 Fig ) . Six hundred and thirteen genes were significantly correlated with median PRNT50 on day 30 during at least 1 time-point following vaccination ( Spearman’s rank correlation coefficient , p<0 . 01 ) . The strongest correlations were seen with genes related to the type I IFN response on day 7 , including DHX58 , OASL , GBP1 , GBP2 , IFI27 , XAF1 and STAT1 ( Spearman’s ρ: 0 . 58–0 . 64 , p<0 . 0007 ) . On day 5 , transcript abundance for KLRC3 ( killer cell lectin-like receptor subfamily c , member 3 ) , which is expressed primarily in NK cells and involved in T cell responses , was positively correlated with median PRNT50 on day 30 ( Spearman’s ρ = 0 . 52 , p = 0 . 004 ) . Forty-two of these genes also showed significant changes in abundance following vaccination ( S6 Table ) , and 11 were involved in the antiviral/type I IFN response described above . The temporal transcript abundance pattern of these 11 genes differed between all ‘low responders’ ( n = 5 animals whose PRNT50 on day 30 was less than 4-fold greater than baseline ) and ‘high responders’ ( n = 5 animals with the highest PRNT50 on day 30 ) ( Fig 5B and 5C ) . The type I IFN response is induced in response to viral sensing , but the transcript median relative abundance of this type I IFN gene set was not significantly correlated with either the extent or duration of viremia ( peak viral load r2 = 0 . 34 , duration of viremia r2 = 0 . 32 ) ( S4 Fig ) . Pathway analysis using BTMs of genes ranked by correlation score also showed that modules of genes related to T cell activation and differentiation were enriched on day 5 , and the type I IFN response was enriched on day 7 , supporting these findings ( S7 Table ) . In most cases , the dominant neutralizing antibody response was directed against serotype 2 ( S3 Fig ) , as seen previously [9 , 11] . Comparison of transcriptional responses by day to DENV-2 specific neutralizing antibody titers pre-boost on day 30 revealed a positive correlation with the relative transcript abundance for genes involved in T cell proliferation and activation on day 5 . These genes included PRKCQ , which encodes the enzyme , protein kinase C theta , required for T cell activation ( Spearman’s ρ = 0 . 6 , p = 0 . 001 ) , and IL6ST , which encodes a cytokine signal transducer ( Spearman’s ρ = 0 . 62 , p = 0 . 0004 ) . When serotype responses were examined separately , 37 transcripts were significantly correlated with the day 30 neutralizing antibody response for at least 3 of the 4 DENV serotypes . While there were no significant gene ontologies , positive regulation of apoptosis was a common signature , and included the genes XAF1 , BARD1 , GCH1 , MAGED1 , PRKCA . Four of these transcripts , which are all involved in the antiviral/type I IFN response , were also significantly enriched following TDV vaccination ( DHX58 , IFI27 , ISG15 , and XAF1 ) .
Understanding the mechanisms that underlie the development of protective immunity against dengue infection may assist in the development of an effective vaccine against dengue . In this study , we used genome-wide transcriptional profiling to identify early transcriptional responses to vaccination that may act as predictors of an effective vaccine response . All animals vaccinated with TDV developed neutralizing antibodies to each of the 4 dengue serotypes , and in most cases the dominant antibody response was to serotype 2 , as seen previously [9 , 11] . TDV-2 was the only strain with vRNA detected after vaccination , and the abundance of TDV-2 vRNA was positively correlated with the development of DENV-2 neutralizing antibodies . Furthermore , we observed changes in transcript abundance for genes related to T cell activation on day 5 that were positively correlated with the presence of TDV-2 vRNA on days 5–14 , and the development of higher DENV-2 specific neutralizing antibody titers on day 30 , suggesting that TDV-2 is more immunogenic than TDV-1 , TDV-3 and TDV-4 , as seen in humans [11] . Changes in abundance of transcripts related to the antiviral and type I IFN response were the most notable features of the response to TDV vaccination These changes were strongest following subcutaneous compared with intradermal vaccination , and double dose administration on day 0 compared with single dose . The type I IFN response is well described for natural DENV infection [12–16] , and appears to be associated with vaccination using live attenuated viruses . The live , attenuated yellow fever vaccine ( YF-17D ) [17 , 19 , 22] and influenza vaccine ( LAIV ) , both resulted in a type I IFN response , but trivalent inactivated influenza vaccine ( TIV ) did not , suggesting that viral replication may also increase immunogenicity of LAIV vaccines [20] . It has recently been shown that IFN-α production by pDCs following dengue infection requires active viral replication in neighboring infected cells , but is triggered by internalization in pDCs of non-infectious viral components [37] . In this study , animals that experienced higher viral loads tended to have higher abundances of gene transcripts related to the type I IFN response , but the correlation was not significant . While we cannot conclude from these data that a stronger IFN response clears the virus , studies of dengue infection in humans and mice indicate that the innate response is important for controlling viral replication and pathology , and that stronger IFN responses are beneficial to the host . Type I IFNs ( IFN-α/β ) are pleiotropic cytokines that play important roles in both innate and adaptive immune responses [32 , 38–40] . During dengue infection , viral recognition of dsRNA by pattern recognition receptors and the cytoplasmic helicases retinoic-acid-inducible gene I ( RIG-I ) and melanoma differentiation-associated gene 5 ( MDA5 ) , leads to an intracellular signaling cascade responsible for the production of IFN-α/β[38] . IFN-α/β activates the JAK/STAT pathway , inducing expression of many interferon stimulated genes ( ISGs ) . Transcripts for several genes in this pathway became more abundant following TDV vaccination , including DDX58 ( RIG-I ) , EIF2AK2 ( PKR ) , STAT1 , IRF7 , OASL and OAS2 , MX1 , IFI44 , ISG15 , and XAF1 . The magnitude of the type I IFN response was much greater following infection with wt DENV , which might reflect greater replication and increased immunogenicity of the wt virus compared with vaccine viruses . However , the response observed in NHPs following wt DENV infection was more subtle compared with natural infection in humans [15] , consistent with the lack of pathology in non-human primates following dengue infection . The type I IFN response that dominated the response to TDV was modulated by both dose and route . After ID immunization , most of the increases in abundance of genes related to the type I IFN response and lymphocyte activation occurred on day 1 , whereas after SC immunization these genes increased in abundance on days 5 and 7 . ID immunization targets Langerhans and dermal dendritic cells , and macrophages in the epidermis and dermis . The dermis is highly vascularized , and skin DCs , antigen presenting cells , and monocytes and neutrophils recruited from the peripheral blood , transport antigens and vaccine components to draining lymph nodes , limiting transfer to the peripheral blood circulation [41] . The earlier type I IFN transcriptional response , and reduced detectable viremia , following ID immunization may be due to the skin’s resident immune cells and faster clearance of the vaccine . The stronger , and later response in a subset of type I IFN genes following subcutaneous immunization , particularly double dose administration , may have resulted from the larger volume and/or dose of vaccine inoculum ( S1 Materials and Methods ) , and prolonged persistence of the vaccine virus in the adipose tissue of the SC layer [41 , 42] . Numerous toll-like receptors ( TLRs ) are expressed by the skin’s immune cells , and by fibroblasts , adipocytes and macrophages of the subcutaneous tissue . TLRs recognize microbial pathogens and trigger signaling cascades involved in both innate and adaptive immune responses . Transcripts encoding TLR4 and TLR5 increased in abundance following TDV vaccination , yet there were no significant differences in expression between ID and SC routes . Increased abundances of transcripts for genes involved in the type I IFN response were correlated with the development of higher neutralizing antibody titers on day 30 , suggesting that type I IFN may play a role in the activation of B cells . The sequence of events linking type I interferon responses to neutralizing antibody titer in our study is not known , but the importance of type I IFN for the activation of the production of antibody by B cells in other systems has been described previously [43–45] . Deal et al . demonstrated that pDC-derived type I IFN was required to activate B cells for production of virus-specific antibodies in human in vitro and mouse in vivo models of rotavirus infection [46] . While neutralizing antibody titers are the primary measure of immunity to dengue virus infection and the response to vaccination , they do not always reflect protection . In fact , T-cell responses may also play an important role in immunity to dengue [47] . The live-attenuated tetravalent chimeric yellow fever-dengue vaccine ( CYD23 ) resulted in no protection against DENV-2 in a phase 2b efficacy trial , and lower efficacy ( 35% ) in a phase 3 trial , despite high neutralizing antibody titers against all four serotypes [48 , 49] . This may be due to the absence in CYD23 of the dengue backbone that harbors important epitopes targeted by CD8T+ T-cells [47] . In our study , increased abundance of genes related to T cell activation on day 5 correlated with the concomitant presence of TDV-2 vRNA ( days 5–14 ) and subsequent DENV-2 specific neutralizing antibody titers on day 30 , suggesting a TDV-2 specific adaptive response . We were unable to correlate the transcriptional response with the development of cell-mediated immune responses , as these were only measured for animals in Group 4 ( SCd PhJ ) [23] . In these animals , TDV induced CD4+ and CD8+ T cells that targeted DENV-2 NS1 , NS3 and NS5 proteins and that cross-reacted with DENV-4 NS3 and NS5 proteins [23] . Additional studies will be useful for evaluating links between the early transcriptional response and the development of cell-mediated immune responses . This study focused on the response to TDV in non-human primates , which serve as important animal models for understanding vaccine responses and efficacy [50] . The responses we observed are similar to those elicited by DENV infection in humans; however , these non-human primates do not develop signs of disease following DENV infection . Characterization of gene expression changes that occur in humans vaccinated with dengue vaccines will be valuable for investigating the role of specific cell types in shaping innate and adaptive immunity . | Dengue has become the leading cause of mosquito-borne virus infections worldwide . Despite considerable effort , development of a successful vaccine against dengue virus ( DENV ) has been challenging due to the co-circulation of the four DENV serotypes in endemic areas—to which humans develop distinct immune responses , and the increased risk of severe disease in those with pre-existing immunity to one serotype when they are infected with a different serotype . In this study , we investigated the responses in macaques to vaccination with the tetravalent , live-attenuated vaccine , TDV , by different doses and routes of vaccine administration . We identify changes in macaque gene expression that occurred in the days immediately following vaccination with TDV , a time-period that is difficult to study during natural infection . The gene expression response was characterized by features of the innate immune response to virus , notably the type I interferon response , and began the day after TDV vaccination . This response correlated with the development of neutralizing antibodies , which means that it might serve as an early indicator of a subsequent protective immune response to dengue vaccines . | [
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"organisms"
] | 2016 | Early Transcriptional Signatures of the Immune Response to a Live Attenuated Tetravalent Dengue Vaccine Candidate in Non-human Primates |
Paromomycin is an aminoglycosidic antibiotic that targets the RNA of the bacterial small ribosomal subunit . It binds in the A-site , which is one of the three tRNA binding sites , and affects translational fidelity by stabilizing two adenines ( A1492 and A1493 ) in the flipped-out state . Experiments have shown that various mutations in the A-site result in bacterial resistance to aminoglycosides . In this study , we performed multiple molecular dynamics simulations of the mutated A-site RNA fragment in explicit solvent to analyze changes in the physicochemical features of the A-site that were introduced by substitutions of specific bases . The simulations were conducted for free RNA and in complex with paromomycin . We found that the specific mutations affect the shape and dynamics of the binding cleft as well as significantly alter its electrostatic properties . The most pronounced changes were observed in the U1406C∶U1495A mutant , where important hydrogen bonds between the RNA and paromomycin were disrupted . The present study aims to clarify the underlying physicochemical mechanisms of bacterial resistance to aminoglycosides due to target mutations .
A well-known problem related to the use of antibacterial compounds is the emergence of resistant bacterial strains [1] . Bacteria constantly improve their resistance techniques by utilizing their abilities to mutate quickly . Their proliferation rate can be as short as minutes [2] , and bacteria can also easily incorporate DNA from the environment . Therefore , there is a pressing need to identify new antibiotics that specifically and efficiently target the processes that are crucial for the life of the bacterial cell . One of the pivotal molecules in the cell is the ribosome , which is a macromolecular complex involved in peptide synthesis , and is composed of ribosomal RNA ( rRNA ) and proteins . The ribosome consists of two subunits: the small subunit ( in prokaryotic organisms called 30S ) and the large subunit ( 50S ) . Several antibiotics target various sites on the ribosomal subunits and interfere with bacterial translation at different stages . Three transfer RNA ( tRNA ) binding sites are located at the interface between the 30S and 50S subunit ( denoted as A , P , and E ) . The A-site on the 16S rRNA of the 30S subunit contains the binding site for most aminoglycosidic antibiotics [3] , [4] . The nucleotide sequence of the A-site is highly conserved in all species [5] , making it difficult for bacteria to gain resistance against aminoglycosides by simple random nucleotide substitutions , since mutations in these conservative regions often lead to death of bacterial cell [1] , [6] . However , studies have shown that bacteria with only one mutation in the A-site , such as A1408G , which resembles the eukaryotic sequence , were no longer susceptible to aminoglycosides [7]–[9] . Furthermore , other experiments have proven that several other single point mutations exist that can successfully block the effect of these antibiotics [7] , [10] , [11] . However , aminoglycosides can bind to a variety of RNA targets and their specificity toward the A-site is not high . Therefore , finding out why a single base substitution in the A-site has such a large effect on the susceptibility of bacteria to aminoglycosides is of high relevance . A variety of computational tools have emerged during the last few decades with the specific aim of complementing experimental structural approaches . In particular , molecular dynamics ( MD ) simulations have demonstrated the potential for revealing the dynamic and flexible properties of biomolecules at an atomic level of detail . Although the application of MD simulations to RNA is a relatively new field , much attention has been paid to adapt the MD methodology to these specific biomolecules ( see refs . [12]–[14] for recent overviews of the improvements and achievements of the use of MD for nucleic acids ) . Several computational studies have been conducted on 16S rRNA fragments containing the A-site as well as on the entire ribosome . MD simulations – classical [15] , replica-exchange [16] and targeted [17] – have shown that the adenines A1492 and A1493 are very mobile in the absence of the antibiotic . These bases are positioned opposite base 1408 , and their mobility has been shown to be important for the fidelity of translation [17]–[22] . In the absence of the antibiotic , these adenines are almost in equilibrium between the flipped-out and flipped-in state , with a slight bias toward the flipped-in conformation [23] , [24] . A1492 and A1493 are responsible for the proper recognition of tRNA , and upon the approach of the cognate tRNA , acquire an extra-helical position that accommodates the tRNA in the A-site . Aminoglycoside binding causes A1492 and A1493 to face to the outside of the 16S rRNA helix toward the solvent [25]–[28] , which promotes the incorporation of near-cognate or non-cognate tRNAs . The MD studies mentioned above have shown that in the absence of antibiotic , the intra-helical state of A1492 and A1493 is energetically favored . Other MD simulations of the model A-site RNA fragment in complex with paromomycin [29] , as well as with other aminoglycosides [30] , have focused on the RNA solvation patterns and antibiotic binding free energies . Brownian dynamics simulation of the model A-site [31] and the entire 30S subunit [32] have investigated aminoglycoside association pathways and rates , but have not focused on the intrinsic dynamics of the binding site . Moreover , none of the theoretical studies to date have investigated the properties of the mutant A-site structures . In our previous study [33] , we identified the differences in physicochemical properties and internal dynamics of the model A-site between the prokaryotic and the eukaryotic-resembling structure when the adenine at position 1408 was substituted with guanine . In that study , we showed that the A1408G mutation affected the mobility of A1492 and A1493 . We also observed that in the intra-helical state , these adenines sometimes form hydrogen bonds with the opposite base at position 1408 . The base pair that formed is more stable in the eukaryotic-like structure ( when guanine occupies position 1408 ) than in the prokaryotic structure ( with adenine in position 1408 ) . Most likely , the increased stability of this base pair has some hindrance to the binding of aminoglycosides to the A-site of the eukaryotic ribosome . We also observed that the A1408G substitution changes the electrostatic potential inside the binding cleft . Aminoglycosides are ionized in physiological pH [25] , [34] , and therefore electrostatic interactions are important for their proper binding . Here , we have significantly extended our previous studies by analyzing how other experimentally reported mutations affect the features of the A-site RNA . We present the results of eight , 20 ns-long MD simulations of the model A-site mutated in silico: three single point mutants and one double mutant , both in the presence and absence of an aminoglycoside , paromomycin . The mutated sites were selected based on previous experimental studies [7] , [10] , [11] , [35] , where the authors compared the impact of different mutations in the A-site of Mycobacterium smegmatis , Escherichia coli , and other bacteria . The base substitutions that caused the most pronounced changes in minimal inhibitory concentrations ( MIC ) for selected bacterial species were chosen , especially in response to treatment with paromomycin .
We analyzed the flexibility of the entire model A-site by calculating the average root mean square deviation ( RMSD ) of atomic positions and root mean square fluctuations ( RMSF ) of each nucleotide as well as paromomycin . The average RMSD from the initial structure , that was calculated for all heavy atoms , did not exceed 2 . 9 Å in every simulation ( Figure S2 ) . Previous studies have shown that paromomycin stabilizes both the wild-type [30] , [33] and the A1408G mutated [33] A-site RNA structure ( for base numbering see Figure 1a; throughout the paper the E . coli numbering convention of the A-site is used ) . In this study , we observed a similar stabilizing effect by the presence of paromomycin on the single point mutant structures G1491U and G1491A . A slightly less pronounced effect of paromomycin was also observed in the U1495C simulation . The stabilizing effects are reflected by the RMSF and RMSD values , which are shown in Figure 2 and Figure S2 , respectively . For example , in the structure that contains the G1491U mutation without the drug , the bases that were in proximity to the mutated site as well as on the opposite strand of the RNA helix ( i . e . , A1408 and C1409 ) showed larger fluctuations , particularly in one section of the RNA fragment . In the presence of the antibiotic , all of the residues became more conformationally restrained . In contrast , the overall decrease in RMSF ( Figure 2 ) or RMSD ( Figure S2 ) that occurred in the presence of the antibiotic was substantially less in the simulation with the double mutation ( U1406C/U1495A vs . U1406C/U1495A_PAR ) . Unlike the other simulations of RNA with paromomycin , the U1406C/U1495A_PAR trajectory showed that the drug itself was more dynamic and significantly changed its conformation ( RMSF values for the two paromomycin molecules in the structure: 2 . 99 and 1 . 59 Å ) . In addition , one paromomycin in the G1491A_PAR simulation was characterized by a higher RMSF of 2 . 8 Å , which indicated a change in conformation . This finding was confirmed by visualizing the trajectory ( discussed below ) . The elevated RMSF of A1492 , A1493 , and A1408 were expected , since these three bases form a bulge in the original crystal structure and their flexibility is necessary for the fidelity of the translation process [17]–[22] . In the MD simulations of the original crystal structure of the model A-site without paromomycin [33] the adenines A1492 and A1493 were flexible and acquired both extra and intra-helical states . They moved from the flipped-out state to the flipped-in conformation , through the minor groove of the RNA helix . Three important conformations of A1492 and A1493 can be distinguished [17] , [19]–[22] , [25]–[28] . Conformation ( a ) , where both adenines occupy the inside of the RNA helix ( , ; see “Methods” for the definition of the angle ) , which is a conformation that prevents the binding of the aminoglycoside and may also cause rejection of a non- or near-cognate tRNA during the translation process . In conformation ( b ) A1492 is flipped out ( or ) and A1493 stays inside the helix ( ) ; this conformation occurs when the translation termination factor has to be recognized and accepted . Finally , conformation ( c ) , where both A1492 and A1493 are outside the RNA helix ( or , and or ) , which occurs upon the acceptance of a cognate tRNA and also enables aminoglycoside binding . To quantify the variance of the conformations of A1492 and A1493 acquired in MD simulations , we used the pseudo-dihedral angle ( ) between the conformationally stable base G1494 and each of the adenines ( Figure S3 ) . From the distribution of the measured values ( Figure 3 ) , the changes in adenine motions caused by the mutations were observed . In addition , we calculated the overall percentages of time that the adenines were inside the RNA helix as another measurement of adenine flexibility ( Table 2 ) . All of the conformations of A1492 and A1493 described above were observed in the NON_MUT simulations ( Figure 3 , dots in shades of gray ) . The G1491A and G1491U mutations restricted the adenines to the flipped-in ensemble of states ( a ) . The smallest changes in the adenines' movement were introduced by the mutation U1495C , while the largest deviation from the original NON_MUT simulation was seen in the U1406C/U1495A simulation , where A1492 and A1493 were positioned outside of the helix for the majority of the time ( Table 2 ) . Experimental studies have shown that the G1491A and G1491U mutations cause an increased read-through of the stop codon [36] . Based on the data presented in Figure 3 , we noticed that the ( b ) area was almost not visited by the adenines in the mutated structures – they move as a pair , while the termination factor requires that only A1492 is in the flipped-out state [20] , [21] . This may cause an acceptance of a non-cognate tRNA in place of a termination factor and lead to the read-through of a stop codon . Visualization of the G1491A and G1491U trajectories showed that the changes in base pairing and in the conformations of A1492 and A1493 made the A-site more condensed and compressed . We quantified these observations by calculating the distances between four atoms of residues 1407 , 1491 , 1492 , and 1493 that pointed to the inside of the binding cleft . To simplify the presentation of these results , we have grouped the trajectory conformations into five clusters ( see Methods , Table S1 and Figure S4 ) . Figure 4 shows the distances between these four atoms in the representative structure ( i . e . , the structure that comprises the center of the cluster ) of the most populated cluster . In general , the cleft in the simulations with the mutated 1491 base was more compact than in the NON_MUT , even though bases A1492 and A1493 moved to the flipped-in state in all three simulations ( NON_MUT , G1491A , and G1491U ) . We noticed that the change of the cleft shape was caused by the shift of the base pairing and the twisting of base 1491 ( see visualization in Figure S4 ) . Although distances 1 and 4 were large in one section of the RNA structure that was in the most populated cluster of the G1491A simulation ( Figure 4 and Figure S4b , left ) , the data derived from the entire trajectory for both A-sites show that the mutations of G1491 resulted in the same or smaller dimensions of the binding site compared to the non-mutated structure ( Figure S5 ) . Mutation of G1491 to adenine ( G1491A ) and to uracil ( G1491U ) allowed A1492 and A1493 to occupy the flipped-in state for up to 87% of the simulation time ( Table 2 ) . Therefore , the range of movement of A1492 and A1493 was reduced in these mutants ( Figure S6 ) . Especially the movement of the adenines in one of the A-sites of the G1491A mutant structure was more restricted to the flipped-in state when compared to the NON_MUT simulation ( see also Figure 2 , top ) . According to recent studies [17] , [37] , the decrease in movability is associated with a change in the accuracy of translation . In this case , the predominantly constant flipped-in position of A1492 and A1493 could result in a reduction in the number of cognate tRNAs accepted . Therefore , protein synthesis would be more prone to errors . On the other hand , it has been postulated that antibiotic binding occurs in a stochastic gating fashion [38] , and thus a mutated A-site should be more resistant to aminoglycosidic antibiotics , since the drug would have difficulty in “catching” the A-site in a conformation that had flipped-out adenines . A recent experimental study [39] on the reverse mutation in the yeast ribosome ( i . e . , with the A1491G mutation ) showed an analogous effect . The eukaryotic ribosome possessing a guanine in the 1491 position was less resistant to aminoglycosides . Moreover , there was a reduction in the frequency of translation error in the absence of the drug . In contrast , mutation of the U·U pseudo-pair ( i . e . , in the simulations U1495C and U1406C/U1495A ) caused A1492 and A1493 to occupy the outside of the RNA helix for the majority of the simulation time ( Table 2 and Figure S6 ) . We observed that mutations in the G1491 position resulted in a change of the base pairing pattern near the substituted base . In the starting conformation , base 1491 formed a hydrogen bond with the opposite base C1409 ( Figure 5a ) . These hydrogen bonds break several times in the G1491A and G1491U simulations . As a result , the C1409 base either pairs with A1492 ( Figure 5b ) or occupies the flipped-out state ( Figure 5c ) . This type of shift in base pairing is commonly found in tertiary RNA structures [40] , and it may contribute to bacterial resistance by changing the shape and volume of the binding site . A similar effect was also observed in some of our previous simulations of the wild-type prokaryotic A-site RNA fragment ( for details see Ref . [33] ) ; however , that shift was caused by a loss of stability by U1406·U1495 , which prevented the flipped-in conformations of bases A1492 and A1493 . In contrast , these two adenines were positioned inside the helix for the majority of the simulation time in the G1491A and G1491U simulations ( Table 2 ) . The 1491U∶C1409 and 1491A∶C1409 pairs , which contained mutant G1491 , were dynamic whenever they formed , and at times the C1409 base flipped out of the helix where it was stacked with either A1408 or A1410 ( Figure 5c ) . Nevertheless , the G1491A mutant structure was generally more conformationally stable than the G1491U mutant . The base pair formed in the MD simulation with adenine in the 1491 position lasted approximately two times longer than with uracil in the same position ( Table S2 ) . The U1406·U1495 pair ( Figure 1a ) is important for the structural stability of the A-site and for proper distribution of electrostatic potential inside the cleft [7] , [29] . Bound paromomycin forms one direct and one indirect hydrogen bond with the O4 oxygens of both uracils . Therefore , we monitored the behavior of these uridines in MD simulations to assess whether the mutations influence the base pairing and contacts with the drug . We found that the single mutants G1491U and G1491A did not affect the stability of the U1406·U1495 pair and that the pair was predominantly formed by two hydrogen bonds ( Figure 6a and Table S2 ) . In contrast , the resulting 1406C∶1495A pair from the U1406C/U1495A simulation formed one hydrogen bond and was only moderately stable ( Figures 6b , 7b , and Table S2 ) . Occasionally , 1406C was observed rotating to a position that was almost perpendicular to the base pair plane ( Figure 7c ) . Nevertheless , the 1406C∶1495A pair often adopted an experimentally observed pattern ( http://bps . rutgers . edu/atlas/bppattern/ac_5 [41]; Figure 7b ) . The geometry and the partial charge distribution in this region were completely altered in the double mutant . Figures 7a , 7b , and 7d show the difference in the atom types and their positions in the original U1406·U1495 pair after introduction of the mutations . In the original U·U pair , two oxygens , U1406 ( O4 ) and U1495 ( O4 ) , which were positioned inside the helix ( Figure 7a ) , formed one direct and one indirect hydrogen bond with the neamine core of paromomycin ( Rings I and II; Figure 1b ) . This moiety is present in every aminoglycoside and serves as an anchor for positioning the aminoglycosides in the A-site . By mutating the U·U pair to C∶A , the negative charge in the binding site that was provided by the uracil oxygens is deleted , which prevents the formation of important hydrogen bonds between the A-site and aminoglycosides . In addition , steric interactions can hinder the binding of the drug , since adenine is larger than uracil and occupies more space inside the binding cleft . The geometry-related changes could have been deduced from simple static structural modeling , however MD simulations describe the complicated dynamics of the hydrogen bonds that are formed between the nucleotides and between RNA and paromomycin . Mutating both uracils broke the U·U hydrogen bonding pattern and the adenines A1492 and A1493 were not able to adopt the flipped-in conformation for a longer period of time , making it easier for the aminoglycosides to bind to thus changed site . In the U1495C simulation , where only one of the uracils was mutated to cytosine , the newly formed U∶C pair was conformationally stable ( Table S2 ) , and at times even formed three hydrogen bonds . The U∶C pair adopted a well-known pattern , called 4-carbonyl-amino [42] or cis W . C . /W . C . [43] ( Figure 7d ) , although alternate periods of a transient , non-classical conformation were also observed ( Figure 7e ) . The mutated pair lacked one oxygen on the inner side of the base pair plane , and the uracil was often found situated deeper inside the helix than in the wild-type U·U conformation . This positioning of uracil may also make it more difficult for aminoglycosides to bind to a modified conformation . The visualization of trajectory revealed changes in the internal dynamics of the A-site/paromomycin complex , which was a result of mutations of the U1406·U1495 pair in the U1406C/U1495A_PAR simulation . These changes were also observed in the RMSF ( Figure 2 ) . Due to the mutations , paromomycin changed its conformation ( Figures 8a and 8b ) . Rings III and IV form the “tail” of paromomycin ( Figure 1b ) and are generally more mobile than the rest of the drug [32] , [44] . However , in the U1406C/U1495A_PAR simulation , the centers of mass of rings III and IV shifted as much as 3 Å ( Figure S8 ) . In one of the A-sites , PAR ( N2 ) of ring IV formed a new hydrogen bond with the G1489 ( O2P ) . In the second A-site , a new hydrogen bond was formed between PAR ( O3 ) of ring IV and the 1406C ( O2P ) . These were not observed for any other simulation . In addition , the position of the core of the antibiotic ( rings I and II ) was altered ( Figure S9 ) . Ring I moved away from the bulge and left room for A1492 and A1493 . In one A-site , the PAR ( N1 ) atom formed a hydrogen bond with 1406C ( O2 ) , which led to the disruption of the C∶A base pair ( Figure 9a ) . In the second A-site , the C∶A pair was formed with only one hydrogen bond , and the PAR ( O6 ) hydrogen bonded with 1406C ( N4 ) ( Figure 9b ) . In the non-mutated A-site [4] , [29] , PAR ( N1 ) forms a tight hydrogen bond with U1495 ( O4 ) ( with the distance of 2 . 82 Å and 2 . 72 Å for the two A-sites of the crystal structure [45] , respectively ) . In addition , the bond was maintained and had a mean distance of between the mentioned atoms in the MD simulation of the original complex . Another important hydrogen bond , which is mediated by a water molecule , is formed between PAR ( O6 ) and U1406 ( O4 ) ( distances in the X-ray structure are 2 . 62 Å between PAR ( O6 ) and the OW oxygen of water molecules W8 or W54; and 2 . 59 and 2 . 41 Å between W8 ( OW ) and W54 ( OW ) , respectively , and U1406 ( O4 ) ; numbering of atoms as in Figure 9d ) . The simulations of the wild-type structure showed that towards the end of the trajectory , a direct bond was formed between RNA and paromomycin: first the distance between PAR ( O6 ) and U1406 ( O4 ) was , and after ca . 11 and 14 ns for each symmetrical part of the structure , respectively , it decreased to . The U1406C∶U1495A mutation did not allow for the formation of the corresponding bonds ( Figure 9e ) and therefore could not support two very important contacts between paromomycin and RNA . These mutations almost completely prevented the proper binding of aminoglycosides in the mutated A-site , which has been shown in MIC experiments performed by Hobbie et al . [7] , [8] . Paromomycin was also dynamic in the G1491A_PAR simulation . In one A-site , we observed a shift of the entire antibiotic , and ring IV rotated around the bond that formed with ring III ( Figure 8c ) . However , this ring reorganization effect was less than in the U1406C/U1495A_PAR simulation , and the hydrogen bonds with the U1406·U1495 pair were preserved ( Figure 9c ) . In fact , paromomycin came closer to the RNA atoms in one of the A-sites ( Figures S7 and S8 ) . The distance between U1406 ( O4 ) and PAR ( O6 ) diminished during the simulation , like in the wild-type structure , which suggested that these atoms actually form a direct hydrogen bond . The clustering of the conformations of the A-site in complex with paromomycin provided additional evidence that the U1406C/U1495A substitution causes the antibiotic to be less conformationally stable in the binding site and even allows for A1492 and A1493 to move into the helix ( Figure S7 ) . Moreover , we also observed differences in the range of movements of the adenines between the NON_MUT_PAR and G1491A_PAR simulations . The G1491A mutation caused the range to widen , which indicated that the bound drug may be less effective [17] , [37] . We monitored the distribution of sodium ions and water molecules inside the binding site , since the electrostatic interactions [25] and indirect water-mediated bonding between paromomycin and RNA [4] , [45] are important for the structural stability of the complex . The analysis of the distribution of ions in the MD simulations without the antibiotic can show how the mutations change the electrostatic potential of the inner side of the RNA A-site helix . In a simulation of the wild-type prokaryotic A-site [33] the area of maximal sodium ion density ( more than 0 . 053 ion per ) was situated in the position of the ring II of the superimposed paromomycin . The locations of high ion density areas in the G1491U and G1491A simulations were roughly similar ( Figures 10a and 10b ) , however in comparison with the ion distribution around the wild-type structure , were shifted approximately 2 Å toward the phosphorous atom of A1493 . This shift indicates that only a minor change in the electrostatic potential occurred inside the RNA bulge , which was most likely caused by A1492 and A1493 predominantly occupying the flipped-in state . The MD simulation performed for the RNA helix with a double mutation of the U1406·U1495 pair showed larger deviations in the distribution of ions compared to the wild-type structure . Figures 10c and 10d show both of the structures in which these uracils were substituted . We noticed that the U1495C mutation introduced smaller changes than the U1406C/U1495A mutation . High sodium ion density areas in the U1495C simulation were shifted approximately 3 Å towards the major groove ( Figure 10c ) , while in the structure with the double mutation , they were located entirely outside of the core of the binding site ( Figure 10d ) . We noted that the ion density closest to ring II of paromomycin was not present , which provides further evidence that the U1406·U1495 pseudo-pair plays an important role in the recognition of the A-site site by aminoglycosidic antibiotics through electrostatic interactions . The analysis of water distribution inside the binding cleft showed that in general , the G1491U , G1491A , and U1495C were less hydrated than the wild-type structure . There were only a few dense areas observed , which can be explained by the change in the cleft shape , either by a shift of base pairing ( similar to the one observed in G1491A and G1491U ) or simply by A1492 and A1493 occupying the flipped-in state ( which was observed in all three simulations ) ( Figure S10a and Table S3 ) . Nevertheless , we observed an area of high water density between U1406 and U1495 in both A-sites in the G1491A and G1491U simulations ( in the X-ray structure this water is numbered W49; Table S3 ) . This indicated that the U1406·U1495 pair was correctly formed , since the hydrogen bonds between these uracils are mediated by a water molecule [45] . In our previous simulations of the original prokaryotic A-site [33] , these water density areas were also observed . In the U1495C simulation , where only one uracil was mutated to cytosine , there was a high water density area near U1406 . This was most likely due to the fact that U1406 was often shifted towards the inside of the helix , which left space for water molecules to gather near U1406 ( O2 ) ( see Figure 7d for atom numbering ) . Inside the structure with the double uracil mutation , we observed more areas of high water density ( Figure S10b ) ; however , none of these were between the mutated bases , and many were located in positions where atoms of paromomycin were found after superimposing the complex structure . These data suggest that although the shape of the binding cleft is not altered in the U1406C/U1495A simulation , there are still water molecules that the antibiotic has to expel upon binding . Additionally , less positions of crystal water molecules were reconstructed in the U1406C/U1495A_PAR simulation compared to the NON_MUT_PAR simulation ( 6 vs . 12; Table S4 ) . These results further confirm that paromomycin has weaker binding to the double mutated A-site .
The U1406C/U1495A mutation ( Figure 1a ) was found to have the biggest effect on the binding site of paromomycin , which is in agreement with previous experiments that have shown changes in the minimal inhibitory concentrations ( MIC ) of aminoglycosides that target bacteria with different mutations in the A-site [8] . This double mutation perturbs the electrostatic potential inside the RNA helix , which in our simulations resulted in disabling the formation of proper direct and indirect contacts between paromomycin and the mutated bases . Moreover , our simulations revealed that upon the change of base 1495 from uracil ( pyrimidine ) to adenine ( purine ) , the shape of the base pair was disrupted . During the simulation , the adenine is situated more inward than the uracil in the wild-type structure , which can possibly prevent paromomycin binding by steric hindrance . This apparent conformational change of the mutated base pair did not seem to affect the other base pairs' stability , and throughout the simulation , the A-site model retained its overall structure , which is in agreement with a previous study [46] . In all of the MD simulations of the complexes with the antibiotic , with the exception of U1406C/U1495A_PAR , paromomycin was firmly bound to RNA , and the complex was less conformationally dynamic than RNA alone . In contrast , the U1406C/U1495A_PAR simulation showed that the drug changed its conformation and slid out from the binding cleft , which indicated that the hydrogen bonds formed with the mutated structure were not stable . When comparing the simulations of structures with mutations of the U·U pair , we noticed that the U1495C substitution has a smaller overall effect than the U1495A substitution , which is in agreement with the experimental studies on affinities of paromomycin for ribosomal 30S subunits that possess different mutations in the A-site [6] . Our studies , together with other works where more uracil mutations have been tested [6] , [8] suggest that the negative electrostatic potential created by base 1495 may be more important for proper recognition of aminoglycosides than the geometry of this base pair . The double mutation in this study completely disrupted both features of the base pair , while the U1495C preserved the shape and one of the negatively charged moieties . The U1406C/U1495G mutation previously examined by Hobbie et al . maintained only the negative charge distribution on the 1495 base . The U1406C/U1495G substitution had almost no effect on the MIC value , which was elevated for the other two mutations ( U1406C/U1495A and U1495C ) . Mutation of the G1491 base also induced a significant effect on the A-site . Previous studies have shown that mutation of G1491 to U and A conferred high levels of resistance to paromomycin [8] , [11] . In the G1491U and G1491A simulations , we observed a shift in the base pairing , including the mutated base . This shift changed the internal dynamics of the binding site and enabled A1492 and A1493 to occupy the flipped-in state for a longer period of time , which could lead to steric clashes with paromomycin and preclude its accommodation in the A-site . Steric changes may influence the ability of aminoglycosides to bind , and may have an even larger effect than changes in the electrostatic potential [47] . Hobbie et al . has suggested that the intra-helical side of adenine is less nucleophilic than that of guanine , and therefore the G1491A substitution diminishes the strength of the hydrogen bonds formed with the drug [7] . Our results show that this mutation significantly changes the shape of the cleft to a point where paromomycin may have difficulty fitting into the binding site . A previous study of the eukaryotic yeast A-site showed that the A1491G substitution only caused a slight decrease in translation error frequency [39]; however , it was shown to increase 10-fold in the presence of the antibiotic . Therefore , the reverse mutation in bacteria can reduce the effect of the bound aminoglycoside at the expense of a slight increase in the translation error rate in the absence of the drug . Our G1491A_PAR simulation showed that A1492 and A1493 acquire conformations close to the flipped-in state , which corresponds to the decreased effectiveness of the antibiotic [17] , [37] . Moreover , in the simulation without the drug ( G1491A ) , these adenines stayed in the flipped-in state for a longer period of time than in the wild-type structure , indicating a possible increase in translation errors , which could occur by rejecting too many tRNA molecules . The MD simulations presented in this study also suggested a cause for the increased probability of a stop codon read-through due to the G1491A mutation that was previously reported [36] . In comparison to the NON_MUT simulation , we noticed that A1492 and A1493 were almost never apart in the G1491A simulation . However , in order to correctly recognize the termination factor , A1493 must stay inside the A-site RNA helix and A1492 must be flipped-out to form the necessary contacts [20] , [21] . Therefore , changes in the movement of the adenines introduced by the G1491A substitution reduces the probability that the termination factor will be accepted . It has been hypothesized that the mutation G1491U is more evolutionary profitable than the G1491A substitution [48] . In this paper , we showed that the simulation G1491A brings more changes to the A-site model and that the flipped-in conformations of the adenines A1492 and A1493 are much more stable in this simulation than in the G1491U simulation and the wild-type structure . This may explain the worse “fitness” of bacteria possessing a G1491A substitution . Moreover , we observed that the complexes of paromomycin with either of the G1491-mutated structures are quite stable , suggesting that in this case , the resistance comes from the smaller percentage of binding-enabled conformations of the A-site in the dynamic ensemble . It has been previously shown with combined experimental and theoretical approaches [38] that in case of aminoglycosides and the ribosome , binding is achieved through so-called stochastic gating or conformational selection , and not an induced fit mechanism . Therefore , the drug simply has a much lower probability of finding a G1491A mutated A-site in a favorable conformation . We propose that an alteration of the substituent at the position of paromomycin ring IV ( Figure 1b ) may improve the binding in the A-site , even with mutations of the U1406·U1495 bases . Specifically , substitution of the group with the would allow the ring IV to interact with phosphate groups of both G1405 and U1490 or G1491 . In the G1491A_PAR and G1491U_PAR simulations a bond was formed between and O2P of the mutated base 1491 , or even with U1490 ( O2P ) . It existed either in place of or along with the hydrogen bond between of paromomycin and G1405 ( O2P ) . This was most likely due to the change in the shape of the binding cleft in these simulations , since we did not observe the former interaction in the other simulations , especially in the NON_MUT simulation . However , with the proposed extension of the paromomycin , ring IV may always be hydrogen bonded to both sides of the major groove , which would anchor the drug even more . Similarly , the 6-OH group of ring II , which forms a water-bridged hydrogen bond with U1406 ( O4 ) in the unmodified A-site [29] , [45] , could be substituted for group . Thus , it may form a direct hydrogen bond with the unmodified base U1406 . Moreover , if the amino group at position ( ring I ) was switched with the OH substituent at position , the bonds formed with the phosphate group of A1492 could be tighter , which would therefore anchor the neamine part of the drug more ( this interaction was weak in the G1491A and the U1406C/U1495A simulations ) . Important hydrogen bonds are also formed with the group ( ring II ) , but they are not stable in simulations of the structures with mutated U1406·U1495 bases . We have noticed that the distance between the nitrogen N3 of paromomycin and phosphorous atom of either G1494 or A1493 is quite big after the equilibration ( an increase from 3 . 9 Å up to 4 . 9 Å ) . Therefore , an extension at this position ( i . e . , instead of a simple amino group ) could improve binding , which may also diminish the effect of the double mutation U1406C/U1495A .
We used a 44-nucleotide RNA model containing two symmetrically positioned A-sites that were complexed with paromomycin as the starting structure ( Figure 1a depicts half of the sequence of the model , Figure 1b shows the structure of paromomycin; PDB code of the whole structure: 1J7T [45] , 2 . 5 Å-resolution ) . This rRNA region forms a helix with a bulge created by the following three adenines: A1408 , A1492 , and A1493 . The chosen model proved to be a good representative of the original binding site , which is a solvent-exposed region in the small ribosomal subunit [33] , [49] , [50] . Since the model is deprived of the influence of all the surrounding ribosomal RNA and proteins that exist in the complete ribosome assembly , it could be questioned whether the behavior of the nucleic bases , particularly of the two adenines A1492 and A1493 , can be reliably represented . Therefore , we have compared the solvent accessible surface area ( SASA ) of these adenines in different X-ray structures of the ribosome with the values from the simulation of the model ( Figure S11; values were calculated with VMD software [51] ) . The range of the values obtained from the simulation of the wild-type A-site model were within the range calculated for the experimental static structures . In this study we also investigate the geometry and dynamics of the U1406·U1495 base pair . In the whole 70S ribosome it is involved in some tertiary contacts ( A1919 of 23S rRNA and G1517 of 16S rRNA; see e . g . , structures 3I8F and 3I8G [52] ) , which we are not able to mimic in our model . Nevertheless , these bases form a stable pair in the wild-type structure [33] and we did not observe bulged-out conformations of either U1406 or U1495 . These results provided additional reassurance that the model in our simulations can reliably reproduce the shape and internal dynamics of the A-site inside the 30S ribosomal subunit . Mutations were chosen based on previous experimental studies [7] , [10] , [11] and were introduced using the Sybyl ( Tripos ) software . We believe that a well-established protocol that includes minimization followed by heating and equilibration of the whole system ( described below ) yields a valid starting structure for the further collection of the production phase data . We also performed simulations of the RNA models with and without the drug , in order to have a reference when seeking changes in the features of the binding site that resulted from the presence of the bound antibiotic . All of the types of MD simulations together with their abbreviations used in the text are listed in Table 1 . The system was neutralized by adding sodium ions around the molecule with the use of LEaP from the Amber9 package [53] . In this step 44 and 34 ions were added to the structures without and with paromomycin , respectively . The neutralized molecules were then submerged into boxes of TIP3P [54] water molecules , again with the use of the LEaP program . The dimensions of each system were 92×69×69 . Finally , random water molecules were substituted with 39 sodium ( , radius: 1 . 5 Å , mass: 22 . 99 a . u . ) and 39 chlorine ( , radius: 1 . 5 Å , mass: 35 . 45 a . u . ) ions , in order to obtain an ionic strength of approximately 150 mM . Sodium ions were chosen because they were better represented in the force field that was used than potassium ions , for example ( see ref . [29] , [55] ) . The Amber ff99 [56] force field was selected for the RNA . A newer version of this force field is available , called parmbsc0 [57] , however we did not use it since we wanted to compare the results with our previous simulations that utilized the ff99 . Moreover , recent studies showed that there is little difference between these types of parametrization in relation to RNA simulations [58] , [59] . Very recently , some improvements of RNA force field parameters were proposed [60] . Banaš et al . have shown that even in the parmbsc0 force field , the angle ( i . e . , the dihedral angle of the linkage between the ribose and the nucleic base ) may adopt some non-standard values , leading to a so-called “ladder-like” structure formation instead of a normal A-RNA helix . We are aware that the parametrization of the RNA force field is far from perfect; however , on short timescales ( similar to our 20-ns trajectories ) and for simple tertiary structures ( i . e . , helical RNA ) it has been proven through many simulations that the experimental fluctuations and the overall structure is maintained [15] , [58] . In addition , the angle in our simulations behaved well for all the RNA sequences and we did not observe high-anti conformations ( see Figure S12 ) . Moreover , our trajectories were not as long as those tested in ref . [60] , and therefore we believe that the conformational changes reported here do not result from an improper parametrization of RNA . The parameters for paromomycin were created with the use of antechamber program from Amber suite , using GAFF [61] force field and AM1BCC charges . Since this is an automatic approach , with no guarantee for yielding correct force field parameters , we further tested the force field parameters . We performed two 10-ns long MD simulations of paromomycin in water ( with different initial velocities ) and compared some conformational features to the existing NMR data [44] ( see Figures S13 and S14 ) . The work of Asensio et al . analyzed NMR spectra of an aminoglycoside neomycin , which differs from paromomycin in only one chemical group ( in place of ) . This did not seem to influence the flexibility of the drug , as our simulations showed a very good correlation with the NMR-derived data . The complete parameters are given in Supplementary Dataset S1 . The computational protocol was essentially the same as previously described [33] . Briefly , the energy minimization was carried out with the sander program of the Amber9 package . Afterwards , the simulations were performed with NAMD [62] under constant pressure ( using the Langevin piston method [63] ) and temperature ( controlled by Langevin thermostat [64] ) and with periodic boundary conditions . Electrostatic interactions were calculated using the Ewald Summation method ( PME [65] ) . The SHAKE [66] algorithm was used which allowed for a 2 fs simulation time step . Thermalization from 30–310 K was performed with constraints applied to all heavy atoms of the RNA and , if applicable , paromomycin . The constraint coefficient ( ) was equal to 50 for the first 85 ps of simulation and then 25 for another 35 ps . The constraints were then gradually weakened during first 300 ps of the equilibration stage . For the remaining 600 ps , the constraints were applied only to heavy atoms of the terminal nucleotides C1402 ( Figure 1a; , to atoms of C1498 ( , and to atoms of G1403 ( . These values were adjusted so as to obtain the fluctuations of the termini that corresponded to the crystallographic temperature factors . The MD production stage was performed under the same conditions as the second part of equilibration and lasted 20 ns . In general , the MD simulations performed under constant temperature only sample configurations that are close to the energetical minimum of the given biomolecule , and do not enable crossing larger energetical barriers . Therefore , the trajectory can be quite limited . In our study , each of the simulations was quite short ( 20 ns ) , but the system includes two symmetrical binding sites , which enlarge the conformational sampling space . In addition , the analysis was mainly comparative between the structures with different mutations . We did not specifically gather statistics on nucleobase flipping . We used standard measures to test the conformational stability of molecules , including the root mean square deviation ( RMSD ) of atomic positions from the initial structure and the root mean square fluctuations ( RMSF ) of each residue . These were calculated with the g_rmsf and g_rms programs from the GROMACS package [67]–[69] for all non-hydrogen atoms . 3DNA software [70] was used to monitor hydrogen bonds between the paired bases and the opening angle of the base pairs . The detailed description of the method can be found in the software manual , which is available at http://rutchem . rutgers . edu/~xiangjun/3DNA . For the description of A1492 and A1493 flipping , we define the pseudo-dihedral angle as the torsion angle between the lines connecting the four atoms G1494 ( N1 ) , G1494 ( P ) , A1492/3 ( P ) , and A1492/3 ( N1 ) ( Figure S3 ) . Base 1494 was stably positioned in all simulations and formed a pair with an opposite C1409 . Therefore , it served as a good reference for the flipped-in conformation . For this study , we defined the flipped-in state of A1492 and A1493 when and , respectively . All other values of point to the base being outside of the RNA helix . A similar measure has been employed in other studies to measure the conformational variation of bases in RNA [16] , [71] and DNA [72] , [73] . The clustering of conformations obtained from MD simulations was performed by the ptraj program of AmberTools ( version 1 . 3 , available at http://ambermd . org ) . Each trajectory was aligned with the first frame in order to eliminate translations and rotations of the structure . The terminal residues C1402 were not considered during clustering because of the applied constraints . The average linkage method was chosen with a maximum of five clusters , and the clustering of the structures was performed according to the RMS distance measure for all heavy atoms . Other maximal numbers of clusters were tested , however the former settings gave optimal results . The calculation of sodium ions and water distribution inside the binding cleft was performed with the use of MolDyAna software ( http://moldyana . icm . edu . pl/moldyana; see also Methods section in [33] ) . VMD [51] and The PyMol Molecular Graphics System ( Schrödinger , LLC . , http://www . pymol . org ) were used to visualize trajectories and R environment ( http://www . R-project . org ) in order to produce plots . | In hospitals throughout the world , aminoglycosidic antibiotics are used to combat even the most severe bacterial infections . However , the continuous emergence of resistant bacteria has created an urgent need to improve these antibiotics . Aminoglycosides bind to bacterial ribosomal RNA . Experiments have shown that specific point mutations in the RNA confer high resistance against aminoglycosides in bacteria . We performed molecular dynamics simulations of the aminoglycosidic binding site model after introducing various mutations . Here , we show that even single nucleotide substitutions can significantly change the physicochemical features of the binding site . In addition , we hypothesize why certain mutations result in bacterial resistance to aminoglycosides . | [
"Abstract",
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] | 2011 | Understanding the Origins of Bacterial Resistance to Aminoglycosides through Molecular Dynamics Mutational Study of the Ribosomal A-Site |
Strongyloides stercoralis infection , a neglected tropical disease , is widely distributed . Autochthonous cases have been described in Spain , probably infected long time ago . In recent years the number of diagnosed cases has increased due to the growing number of immigrants , travelers and refugees , but endemically acquired cases in Spain remains undetermined . We systematically searched the literature for references on endemic strongyloidiasis cases in Spain . The articles were required to describe Strongyloides stercoralis infection in at least one Spanish-born person without a history of travel to endemic areas and be published before 31st May 2018 . Epidemiological data from patients was collected and described individually as well as risk factors to acquisition of the infection , diagnostic technique that lead to the diagnosis , presence of eosinophilia and clinical symptoms at diagnosis . Thirty-six studies were included , describing a total of 1083 patients with an average age of 68 . 3 years diagnosed with endemic strongyloidiasis in Spain . The vast majority of the cases were described in the province of Valencia ( n = 1049 ) . Two hundred and eight of the 251 ( 82 . 9% ) patients in whom gender was reported were male , and most of them had current or past dedication to agriculture . Seventy percent had some kind of comorbidity . A decreasing trend in the diagnosed cases per year is observed from the end of last decade . However , there are still nefigw diagnoses of autochthonous cases of strongyloidiasis in Spain every year . With the data provided by this review it is likely that in Spain strongyloidiasis might have been underestimated . It is highly probable that the infection remains undiagnosed in many cases due to low clinical suspicion among Spanish population without recent travel history in which the contagion probably took place decades ago .
Strongyloidiasis is a disease caused by soil-transmitted helminths , mainly by the species Strongyloides stercoralis . This intestinal nematode infects an estimated 300 million people worldwide , although this is probably underestimated . It is one of the most neglected of the neglected tropical diseases ( NTD ) and is widely distributed [1–2] . Although it generally occurs in subtropical and tropical countries , transmission is also possible in countries with temperate climates . Autochthonous cases have been described in Spain , possibly infected long time ago . It remains uncertain whether S . stercoralis is currently endemic in Spain . Still , some authors consider this country and some other southern European countries as endemic [3] . The life cycle of S . stercoralis is complex and follows multiple routes , including a complete life cycle outside the human host . The most frequent mechanism of infection is percutaneous entry of the filariform larvae . In healthy people , most of the cases are asymptomatic , although it can cause intermittent symptoms that mainly affect the intestine , the lungs or the skin [4] . About criterion used to establish the diagnosis of strongyloidiasis is not homogenized among the centers . The diagnostic laboratory criterion of strongyloidiasis is the observation of larval stages . However , in chronic infection , larvae excretion may be low and fluctuating , and microscopic observation is not sensitive enough and multiple stool specimens should be analyzed to increase the sensitivity of the method . The clinical criterion is a patient with epidemiological antecedents and any of the associated clinical manifestations , especially if it is an immunosuppressed patient . These methods are laborious , time consuming , and in the case of fecal culture , requires well trained technicians in order to differentiate S . stercoralis . Several immunological tests have also been described ( ELISA , IFAT and Western blot ) with variable sensitivity and specificity depending on the population tested among other factors [1] . Alternative diagnostic methods , such as molecular biology techniques ( mostly polymerase chain reaction , PCR ) have been implemented . However , PCR might not be suitable for screening purpose , whereas it might have a role as a confirmatory test , since it still misses a relevant proportion of infected people [5] . Due to the subtle symptoms , low sensitivity of diagnostic techniques and the complex lifecycle that can cause asymptomatic autoinfection for decades , the prevalence of S . stercoralis is thought to be severely underestimated . Typically risk factors for severe infection include immunosuppression , certain malignancies , human T-cell lymphotropic virus type 1 infection , and alcoholism . Likewise , S . stercoralis has been associated with agricultural or mining activities . In Germany , it was recognized as a parasitic professional disease in miners [6] . S . stercoralis infection has also been linked to low socioeconomic factors and infrastructure , indicating that it as a disease of disadvantage [7–8] . In recent years the number of diagnosed cases has been increasing in high income countries due to the growing number of immigrants , travelers and refugees [9–10] . To provide information on this topic , a systematic review of the cases of endemic strongyloidiasis in Spain was carried out , as well as the description of the epidemiological characteristics of these patients .
Aiming to assemble all scientific articles based on endemic strongyloidiasis diagnosed in Spain , a systematic review was carried out . Relevant articles were retrieved from PubMed , EMBASE , Scielo , ISI Web of Knowledge , and Cochrane Library databases using combinations of the search terms adapted to each database . Additionally , Gray Literature in the form of communications presented at national congresses was performed , as well as OpenGrey . As a secondary source , Google Scholar and free internet search was used for non-indexed articles . The keywords were “Strongyloides stercoralis” , “soil-transmitted helminthiasis” , “endemic” , and “Spain” . The following combinations of MeSH were used in PubMed: ( Strongy* [MeSH] AND Spain ) , ( "Strongyloidiasis" [MeSH] AND Spain NOT "imported" NOT "immigrant" ) , and ( "Strongyloidiasis" [MeSH] AND "endemic" AND "Spain" ) . The selection criteria were articles published in any language until May 31st 2018 that contained the description of at least one human case of infection with S . stercoralis acquired in Spain without a history of travel to endemic areas . No restrictions were applied based on the study design or data collection . Human filter was applied . A manual search of the bibliographical references cited in the relevant articles was carried out . All potential articles were analyzed by two researchers to assess compliance with the selection criteria . In situations of missed information , the corresponding author of the paper was contacted to gather the information . If the author answered the required information to fulfill the inclusion criteria , those articles were considered . If not , they were excluded because they could not ensure the endemic acquisition . The exclusion criteria included: animal studies , cases in which endemic infection could not be assured , cases of foreigners from an endemic country for S . stercoralis , native people with trips to endemic or probably endemic countries in the past ( e . g . Italy , France or Portugal ) , transplanted people in which this contagion route could not be excluded , and duplicated cases . Based on these criteria the articles were reviewed in two stages . In the first stage , articles were selected by titles and abstracts according to selection criteria . In the second stage , the full text of the articles was analyzed . Finally , the articles that met the selection criteria were included in the study . From each study the following data was extracted: the study period , year of publication and number of endemic cases described . The following epidemiological data from patients described in the studies was collected: age , gender , geographical origin , medical comorbidities and concomitant treatments , occupation ( or hobbies if relevant ) , other risk factors , year of diagnosis , diagnostic technique used for diagnosis , presence of eosinophilia and clinical symptoms .
Thirty-six studies were included describing a total of 1083 patients with endemic strongyloidiasis in Spain ( see Tables 1 and 2 ) [11–46] . The average age of the described cases was 68 . 35 years , ranging from 17 to 100 years old . Two hundred and eight of the 251 ( 82 . 9% ) patients in whom gender was reported were male , and most of them had current or past dedication to agriculture . The province in whom most cases were described was Valencia , with 1049 people diagnosed . Alicante had 13 and Murcia 5 , eventually describing cases in provinces of coastal oceanic climate with abundant rainfall most of the year and temperatures below 22°C ( Cantabria , Asturias , and Pontevedra ) . See Fig 1 . Regarding the number of diagnosed cases per year , a decreasing trend is observed since the beginning of this decade . The year with higher number of diagnosed cases was 2003 , with 82 patients . Since 2011 , no more than 10 cases have been reported annually ( Fig 2 ) . The technique that led to the diagnosis of strongyloidiasis was described in 743 patients from twenty-six different articles . In some cases , different techniques were used for the same diagnosis . In 692 patients ( 93 . 1% ) , the technique used for the definitive diagnosis of strongyloidiasis was the fresh stool examination , specific fecal culture , the Baermann test , the Ritchie technique or the Harada Mori technique . In 39 patients ( 5 . 2% ) the diagnosis was made by the sputum or bronchoalveolar lavage examination . In 6 cases ( 0 . 8% ) the diagnosis was made by serological techniques and in another 6 cases ( 0 . 8% ) the diagnosis was made by histopathological analysis . In 26 of the 37 patients individually described , comorbidities were reported . Out of those , most frequent were diseases that associate the use of corticosteroids such as: chronic obstructive pulmonary disease ( COPD ) , asthma , and inflammatory bowel diseases or immunosuppressive conditions due to advanced HIV infection ( AIDS stage ) or malignancies . In all patients diagnosed with COPD , severity of airflow limitation ( FEV1 ) was according to the Global Initiative for Chronic Obstructive Lung Disease ( GOLD ) criteria at least moderate GOLD 2 ( 50% ≤ FEV1 < 80% predicted ) if not severe: GOLD 3 ( 30% ≤ FEV1 < 50% predicted ) . Overall , 70 . 3% of these patients had at least one comorbidity . In patients in whom blood test results were reported , 41 out of the 50 ( 82% ) exhibited eosinophilia . The median eosinophil count in patients with eosinophilia was 4 , 057 eosinophil/mm3; considering 24 individual reported counts .
Strongyloidiasis prevalence may be underestimated in many countries . With the data provided by this review it is likely that underestimation could have been a reality for the last five decades in Spain . The main cause would be the lack of clinical suspicion . But also the subtle symptoms , the decades-long persistence of infection in untreated hosts and the absence of a diagnostic test of choice with high sensitivity and specificity would ultimately contribute . An important finding of our work is that almost 97% of all published infections occurred in the province of Valencia . The fact that most cases diagnosed and published are in the province of Valencia , can respond to various reasons . Firstly , the area had the perfect combination of temperature and humidity , population exposed to S . stercoralis for occupational reasons such as rice farmers or irrigation ditch cleaners ( activities that were characteristically carried out barefoot ) and hygiene factors of rural areas during the 1960s ( lack of drinking water and toilets in some homes ) . It is noteworthy that no cases of strongyloidiasis have been reported in other areas with similar climatology and population equally dedicated to the cultivation of rice fields , such as the Delta del Ebro in Tarragona province . We consider highly probable that there has been transmission in other areas outside those described . Secondly , health care professionals in the area of Valencia probably had a greater awareness of the infection , with a higher suspicion and therefore a higher number of diagnoses . Although we concur that the estimated prevalence of S . stercoralis by one highly cited article is not representative of the entire country , we disagree that Spain should not be considered an endemic country [17] . However , autochthonous cases have been anecdotal in the last decade , as indicated by Martinez-Perez [47] . The results of the individuals diagnosed showed an average age close to 70 years old . Given the known characteristics of the disease the contagion probably took place decades before the diagnosis , coinciding with the postwar period where hygienic conditions and infrastructure were affected . On one hand , factors of unavoidable mention that directly affect the transmission of this helminth are the improvement in hygienic conditions and the mechanization of agricultural work . On the other hand , the increase of awareness by health care workers , especially from the most affected communities , may have led to the diagnosis of new cases in recent years . An overall higher incidence rate in male gender is described , which is consistent with previous studies [15 , 17 , 21 , 27] . This might be explained due to a gender biased; since some articles focus on screening high risk population ( farmers or smokers with COPD ) , traditionally associated with gender roles . Regarding the diagnostic techniques used , there is great heterogeneity among the different studies . The sensitivity of techniques based on microscopy is not good enough , particularly in chronic infections . Serology is a useful tool but could overestimate the prevalence of the disease due to cross-reactivity with other nematode infections and its difficulty distinguishing recent and past ( and cured ) infections . However , current serological tests are specific enough and negativization or a decrease in the titers could be observed 6–12 month after treatment , making this tool very useful [48] . There are some limitations that have to be mentioned . Inevitably there are cases of strongyloidiasis that have not been written for publication . In addition , ten articles had to be excluded due to lack of information about travel history or did not comply with the minimum information required . Therefore , it is highly probable that there were more than 1083 cases . Lastly , given the characteristics of this review , it is possible that there are some duplicate cases in multiple description articles and described individually by another researcher . In summary , there are still new diagnoses of autochthonous cases of strongyloidiasis in Spain every year , especially as occupational hazard in a specific Spanish region . Although the number of diagnoses is much lower than in the past decade , it is highly probable that the infection remains undiagnosed due to low clinical suspicion among Spanish population without recent travel history . Epidemiological studies in at risk areas based on serological techniques could give more information about the real situation of autochthonous cases of strongyloidiasis in Spain . | S . stercoralis is a soil transmitted helminth that is common in many subtropical and tropical countries , but is also found in other regions of the world . In this study we reviewed all published material on endemic infection of S . stercoralis acquired in Spain issued before 31st May 2018 . We collected data from these articles and reported clinical and epidemiological characteristics of patients . Our systematic review of the articles showed a clear geographical pattern; nearly 97% of the cases described had been acquired in the Valencia province . Most of them ( 82 . 9% ) were male , and most had current or past dedication to agriculture . Our results showed that 70 . 3% had at least one condition or treatment that could have made them more vulnerable to suffer a severe form of this helminthic disease . Our data suggests that S . stercoralis infection probably remains underdiagnosed in Spanish population . Due to the scarce information available about endemic strongyloidiasis in Spain until now , we believe that the present work will be relevant and the conclusions derived from it might raise awareness about underdiagnosis . Transmission risk factors described in the people diagnosed may be key for prevention and control strategies implementation . | [
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] | 2019 | Strongyloides stercoralis infection: A systematic review of endemic cases in Spain |
The optimization of snakebite management and the use of antivenom depend greatly on the knowledge of the venom's composition as well as its pharmacokinetics . To date , however , pharmacokinetic reports on cobra venoms and their toxins are still relatively limited . In the present study , we investigated the pharmacokinetics of Naja sumatrana ( Equatorial spitting cobra ) venom and its major toxins ( phospholipase A2 , neurotoxin and cardiotoxin ) , following intravenous and intramuscular administration into rabbits . The serum antigen concentration-time profile of the N . sumatrana venom and its major toxins injected intravenously fitted a two-compartment model of pharmacokinetics . The systemic clearance ( 91 . 3 ml/h ) , terminal phase half-life ( 13 . 6 h ) and systemic bioavailability ( 41 . 9% ) of N . sumatrana venom injected intramuscularly were similar to those of N . sputatrix venom determined in an earlier study . The venom neurotoxin and cardiotoxin reached their peak concentrations within 30 min following intramuscular injection , relatively faster than the phospholipase A2 and whole venom ( Tmax = 2 h and 1 h , respectively ) . Rapid absorption of the neurotoxin and cardiotoxin from the injection site into systemic circulation indicates fast onsets of action of these principal toxins that are responsible for the early systemic manifestation of envenoming . The more prominent role of the neurotoxin in N . sumatrana systemic envenoming is further supported by its significantly higher intramuscular bioavailability ( Fi . m . = 81 . 5% ) compared to that of the phospholipase A2 ( Fi . m . = 68 . 6% ) or cardiotoxin ( Fi . m . = 45 . 6% ) . The incomplete absorption of the phospholipase A2 and cardiotoxin may infer the toxins' affinities for tissues at the injection site and their pathological roles in local tissue damages through synergistic interactions . Our results suggest that the venom neurotoxin is absorbed very rapidly and has the highest bioavailability following intramuscular injection , supporting its role as the principal toxin in systemic envenoming .
Snake envenomation remains a neglected tropical disease prevalent in the Southeast Asia region , including Malaysia [1] , [2] . It affects not only the population in the rural area but also the suburban regions due to rapid urbanization , and the encroaching of human activities into the natural habitat of snakes [3]–[7] . In Malaysia , cobra bites appears to be one of the commonest causes of snake envenomation [4]–[6] . There are two species of common cobras in Malaysia: Naja kaouthia and Naja sumatrana , both classified as Category 1 medically important venomous snake [2] . Of these two Naja cobras , N . sumatrana is widely distributed in the Peninsula Malaysia ( including Singapore ) , and is also known as the Equatorial spitting cobra [8] , one of the venom-spitting species in Southeast Asia that are able to cause venom ophthalmia . Clinically , cobra bites produce systemic envenomation syndrome with the characteristic neuromuscular paralysis , and local toxicity manifested as severe tissue necrosis [2] , [6] , [9] . The characterizations of different cobra venoms , however , are necessary for the better understanding of cobra envenomation pathophysiology as the toxin compositions in cobra venoms vary from species to species [10] . Recent venom profiling with the use of ion-exchange high performance liquid chromatography has shown that the major toxins of N . sumatrana venom comprise high abundance of phospholipase A2 and three-finger toxins such as polypeptides of neurotoxins and cardiotoxins [10] . These are toxins with varied biological and physicochemical properties which make the characterizations of individual toxins warranted in order to gain better insights into the toxic effects of the whole venom . The optimization of snakebite management and the use of antivenom depend greatly on the knowledge of the venom's composition , pharmacological activities , as well as its disposition in the body ( i . e . pharmacokinetics ) . The pathophysiological and pharmacological effects of snake envenomation are related to the absorption and distribution kinetics of venom toxins into the systemic circulation . Indeed , it has been reported that the serum concentrations of venom antigens in snakebite victims are well correlated with the severity of systemic and local symptoms during the course of envenomation [11] . Although there have been some studies on the pharmacokinetics of snake venoms or toxins in animals [12]–[22] , the highly varied snake venom compositions , inconsistent animal models , different pharmacokinetic modelling make the convergence of the data equivocal to have the pharmacokinetic parameters generalized across all snake species . To date , even within the Naja genus of cobras , the pharmacokinetic studies on their venoms were limited to isolated toxins of Formosan cobra [12] , [21] , a few African cobra venoms and their alpha toxins [15] and N . sputatrix venom [22] . Information on the systemic bioavailability of cobra venoms and their toxins following envenomation is even scarcer in the literature . There is therefore a need to define the pharmacokinetic parameters of specific cobra venom and its toxins more meticulously for better clinical correlation . In the present study , the pharmacokinetics of N . sumatrana venom and its three major types of toxins ( neurotoxin , cardiotoxin and phospholipase A2 ) were investigated using double-sandwich ELISA . This is the first report where the pharmacokinetics of a cobra venom was investigated alongside the pharmacokinetics of all its major types of toxins . The results will make it possible to interpret the pharmacokinetics of the whole venom in the light of that of its major toxins , and to enable better understanding of the pathophysiological effects of the venom .
All experimental animals were handled in accordance to CIOMS guidelines on animal experimentation [23] . The experimental protocol on the animal study ( 2013-06-07/MOL/R/FSY ) was approved by the Institutional Animal Care and Use Committee , Faculty of Medicine , University of Malaya . The venom was a pooled sample obtained from three adult N . sumatrana captured in central Malaysia ( Negeri Sembilan ) and was supplied by Snake Valley ( Seremban , Malaysia ) . Resource S ion exchange column and HiTrap Protein A HP affinity column were purchased from GE Healthcare ( New Jersey , USA ) . Goat anti-rabbit IgG-horseradish peroxidase ( HRP ) conjugate was obtained from Abnova , Taipei , Taiwan . Lichrosphere WP 300 C18 reverse-phase column cartridge was purchased from Merck , New Jersey , USA . iBlot Gel Transfer stacks and iBlot blotting system were supplied by Invitrogen . Sephadex G-25 gel beads and all other reagents were purchased from Sigma – Aldrich ( St . Louis , USA ) or as stated in the methods . The animals used in this study ( New Zealand white rabbits ) were supplied by Chenur Supplier ( Selangor , Malaysia ) . The animals were housed in Laboratory Animal Centre , Faculty of Medicine , University of Malaya , and received water and food ad libitum . The major N . sumatrana venom toxins ( phospholipase A2 , neurotoxin and cardiotoxin ) were isolated from the venom by Resource S ion-exchange chromatography as described by Yap et al . , 2011 [10] . The isolated phospholipase A2 , neurotoxin and cardiotoxin ( corresponds to peak 5 , peak 7 and peak 8 , respectively as reported in Yap et al . , 2011 [10] ) were further purified by C18 reverse-phase high performance liquid chromatography ( HPLC ) to homogeneity on 12 . 5% sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) . The gel bands were subjected to in-gel tryptic digestion followed by protein identification using matrix assisted laser desorption/ionization-time of flight ( MALDI-TOF/TOF ) mass spectrometry , as described by Yap et al . , 2011 [10] . Pre-immune serum was collected and used as the control in ELISA . In the first immunization , N . sumatrana venom ( 10 µg ) or venom toxins ( neurotoxin , cardiotoxin and phospholipase A2 , respectively at 5 µg ) dissolved in PBS ( pH 7 . 2 ) and mixed with an equal volume of Freund's complete adjuvant , was injected intramuscularly into the thigh of the rabbits ( n = 3 for each group ) . For the subsequent immunizations , 20 µg of the venom or 10 µg of venom toxins were dissolved in PBS ( pH 7 . 2 ) , respectively , mixed with an equal volume of Freund's incomplete adjuvant and injected intramuscularly at multiple sites at the back of the rabbit fortnightly for 8 weeks . The immunogenicity and antibody titers of inocula were monitored using indirect ELISA as described by Yap et al . , 2011 [10] . The rabbits were bled by cardiac puncture 9 days after the final immunization as indicated by plateauing of antibody titer on indirect ELISA . Anti - N . sumatrana venom IgG and three anti-toxins IgG were isolated from rabbit sera ( upon completion of immunization scheme ) by Sephadex G-25 gel chromatography , followed by Protein A affinity chromatography [24] . The IgG-HRP conjugate was prepared as described by Wisdom , 1996 [25] . Double-sandwich ELISA was conducted as described previously [27] . It was used to monitor the serum venom antigen levels following experimental envenomation in rabbits ( n = 3 ) during pharmacokinetic studies . Briefly , ELISA immunoplates were coated overnight at 4°C with optimal coating concentration for venom and venom toxins , which has been optimized as stated above in the previous section ( double-sandwich ELISA for investigation of immunological cross-reactivity ) . This was followed by subsequent incubation with 100 µl of diluted rabbit serum samples ( 1∶20 ) collected at different time intervals , 100 µl of anti-N . sumatrana venom IgG-HRP conjugate and anti-toxins IgG-HRP conjugate ( dilution of 1∶400 ) for 2 h . Substrate o-phenylenediamine dihydrochloride ( 0 . 4 mg/ml ) was added for colorimetric development and the absorbance at 492 nm was then determined using Bio-Rad Model 690 microplate reader . A standard curve was constructed using varying dilutions of venom or the respective toxins in the spiked pre-envenomed sera . The pharmacokinetics of N . sumatrana venom or toxins was studied using rabbits ( n = 3 ) . A sub-lethal dose of the venom or toxins was administered intravenously ( i . v . , marginal ear vein ) or intramuscularly ( i . m . , quadriceps ) into rabbits . Doses administered were as follow: venom 0 . 5 mg/kg ( i . m . ) or 0 . 1 mg/kg ( i . v . ) ; phospholipase A2 0 . 1 mg/kg ( i . m . ) or 0 . 05 mg/kg ( i . v . ) ; neurotoxin 0 . 07 mg/kg ( i . m . ) or 0 . 05 mg/kg ( i . v . ) ; cardiotoxin 0 . 15 mg/kg ( i . m . ) or 0 . 05 mg/kg ( i . v . ) . Blood samples were collected from central ear artery before experimental envenomation and at specific time points ( 5 min , 10 min , 30 min , 1 h , 2 h , 3 h , 6 h and 24 h ) after venom injection . The collected blood samples were centrifuged at 3 , 500 g for 20 min to obtain the sera , which were kept at -20°C until further analysis . The serum antigen concentrations were measured by double-sandwich ELISA as described above using the pre-envenomed serum sample taken from the same animal as the control for baseline reading . A parallel series of experiments were conducted to investigate the pharmacokinetics of cardiotoxin in the whole venom when N . sumatrana venom was injected intravenously or intramuscularly into the rabbits ( n = 3 ) . The serum concentrations of cardiotoxin ( in the whole venom ) at specified sampling times were estimated using anti-CTX IgG on a double-sandwich ELISA , as described above . The equivalent amounts of cardiotoxin in the injected whole venom ( 0 . 1 mg/kg , i . v . or 0 . 5 mg/kg , i . m . ) were estimated to be 0 . 04 mg/kg or 0 . 2 mg/kg , respectively , based on a 40% ( by dry mass ) composition of the whole venom [10] . This additional study aimed to verify if the pharmacokinetics of cardiotoxin when injected alone would be significantly different from that when injected in its native environment ( the whole venom ) . The pharmacokinetic parameters of N . sumatrana venom and venom toxins were determined using the method of feathering [28] . The initial phase rate constant ( α ) and terminal phase rate constant ( β ) were determined from the slopes of the best-fit lines obtained for the initial phase and terminal phase , respectively , of the log plasma concentration versus time plot . The initial phase half-life ( T1/2α ) and terminal phase half-life ( T1/2 β ) were determined by formula T1/2α or T1/2β = 0 . 693/α or β . The area under the curve ( AUC ) was calculated from zero time to the last experimental time point by trapezoidal rule and extrapolated to infinity ( AUC0-∞ ) according to the formula: AUC0-∞ = AUC0-t+Ct/β , where t is the last experimental time point and Ct represents the last serum venom concentration determined at time t . The distribution rate constants for the transfer between central compartment ( designated as 1 ) and peripheral compartment ( designated as 2 ) were calculated from the equations: k21 = ( Aβ + Bα ) / ( A+B ) and k12 = α+ β − k21− ( αβ/k21 ) . The other important pharmacokinetic parameters were determined as follows: Systemic clearance , CL = dose ( F ) /AUC0-∞ Volume of distribution by area , Vd , area = CL/β Volume of central compartment , Vc = Dosei . v . / ( A + B ) Volume of peripheral compartment , Vp = k12/k21 ( Vc ) Fi . v . is the intravenous bioavailability which is 1 . Fi . m . is the intramuscular bioavailability , and was calculated as follows: All data are reported as the mean ± S . D . or mean ( 95% C . I . ) . Mann-Whitney U test was used to compare differences between two independent groups . Kruskal-Wallis H Test is the nonparametric test for the comparison of more than two independent groups . The level of significance was set at p<0 . 05 . The statistical analysis was conducted using SPSS 21 . 0 ( SPSS Inc . , Chicago , IL , USA ) .
The serum concentration-time profiles of whole N . sumatrana venom antigen following a single i . v . and i . m . administrations of venom into rabbits ( n = 3 ) are shown in Figure 1 . The i . v . serum concentration-time profile of N . sumatrana venom ( 0 . 1 mg/kg ) ( Figure 1 , dotted line ) showed a bi-exponential pattern which was best fitted to a two-compartment model of pharmacokinetics described by the equation Ct = Ae−αt + Be−βt: where Ct represents the concentration at time , t; A and B represent the venom concentrations at the zero time intercepts of the initial fast phase and terminal slow phase , respectively; while α and β represent the first-order disposition rate constants for the initial fast phase and the terminal phase , respectively . The venom antigen level declined rapidly within the first 1 h ( T1/2α = 0 . 8 ± 0 . 3 h ) during the initial phase followed by a much slower decline at the terminal phase ( T1/2β = 13 . 6±1 . 1 h ) . The volume of distribution by area ( Vd , area ) of the venom antigens in rabbits was 1 . 8±0 . 03 L , and the systemic clearance ( CL ) was 91 . 3±7 . 8 ml/h , and the AUC0-∞ was 2201 . 2±185 . 5 ng/ml . h . The distribution rate constant for transfer from central to peripheral compartment ( k12 = 0 . 4±0 . 2 h−1 ) was comparable to that from peripheral to central compartment ( k21 = 0 . 5±0 . 2 h−1; p>0 . 05 ) . Consequently , the volume of peripheral compartment ( 0 . 8±0 . 2 L ) calculated based on the ratio of k constants was comparable to that of central compartment ( 1 . 0±0 . 1 L ) . The intramuscular administration of whole N . sumatrana venom in rabbits yielded a serum concentration-time profile ( Figure 1 , solid line ) with the absorption and distribution phase appeared indistinguishable . The venom antigen level peaked within 1 h at a concentration ( Cmax ) of 391 . 7±48 . 5 ng/ml . The terminal half-life ( T1/2β = 12 . 5±0 . 9 h ) , volume of distribution by area ( Vd , area = 1 . 7±0 . 1 L ) and the systemic clearance ( CL = 94 . 8±12 . 7 ml/h ) of the venom antigen following i . m . injection were not significantly different from that of i . v . pharmacokinetic parameters ( p>0 . 05 ) ( Table 1 ) . The AUC0-∞ of N . sumatrana venom when injected intramuscularly ( 0 . 5 mg/kg ) was 4617 . 8±583 . 8 ng/ml . h . However , when adjusted to the intravenous venom dose ( 0 . 1 mg/kg ) , the normalized AUC0-∞ of the venom antigens following i . m . administration was 923 . 6±116 . 8 ng/ml . h , which was significantly lower than the i . v . AUC0-∞ value ( 2201 . 2±185 . 5 ng/ml . h; p<0 . 05 ) . The i . m . bioavailability ( Fi . m . ) calculated from the two AUC0-∞ values were 41 . 9±0 . 2% . The phospholipase A2 , neurotoxin and cardiotoxin were isolated and purified from N . sumatrana venom . The protein identity of each toxin was confirmed by MALDI-TOF/TOF and is shown in Table 2 . Indirect ELISA and double-sandwich ELISA demonstrated extensive cross-reactions between phospholipase A2 and neurotoxin ( >50% ) , but not between these two toxins and cardiotoxin ( Table 3 ) . These findings were supported by Western blot results ( Figure 2 ) : the anti-PLA2 IgG only reacted with the phospholipase A2 and neurotoxin , but not with cardiotoxin; and similarly , the anti-NTX IgG only reacted with the neurotoxin and phospholipase A2 , but not with cardiotoxin . Anti-CTX IgG reacted only with cardiotoxin , but neither with phospholipase A2 nor neurotoxin . The neurotoxin appears to migrate to a higher position than it should ( i . e . in the same position as phospholipase A2 ) ( Figure 2 ) . To further examine this phenomenon , we performed protein mass analysis and confirmed that the neurotoxin isolated is indeed short neurotoxin with a molecular mass of 6 . 5 kDa ( unpublished data ) . The mass increase of neurotoxin as observed from SDS-PAGE could be attributed to the oxidation of Trp or Met residues in the neurotoxin [29] . Similar observation of abnormally high molecular mass neurotoxin has also been reported from Ophiophagus hannah venom [30] . The serum concentration-time profiles of purified N . sumatrana venom phospholipase A2 , neurotoxin and cardiotoxin following single i . v . or i . m . administrations into rabbits ( n = 3 ) are shown in Figure 3A–C . All of the intravenous profiles showed a bi-exponential pattern which was best fitted to a two-compartment pharmacokinetic model represented by the following equation: Ct = Ae−αt + Be−βt . The antigen concentrations in general decreased rapidly within a distribution half-life ( T1/2α ) of 0 . 5–0 . 7 h during the initial phase and followed by a declining terminal phase with half-life ( T1/2β ) of 8–12 h . On intramuscular routes , it contrast to the multiple peaks ( Cmax ) in the case of whole venom , we observed a single peak for toxin absorption at 0 . 5 h ( Tmax for neurotoxin and cardiotoxin ) or 2 h ( Tmax for phospholipase A2 ) ( Figure 3A–C , solid line ) . The intramuscular profile subsequently followed that of intravenous profile with a linear declining curve , illustrating the terminal phase of the serum concentration-time course . The i . v . and i . m . pharmacokinetic parameters of all three major toxins were shown in Table 4 . Most of the key i . m . pharmacokinetic parameters of the toxins ( especially related to distribution and elimination processes ) were not significantly different from the corresponding i . v . pharmacokinetic parameters ( p>0 . 05 ) . The intramuscular bioavailability ( Fi . m . ) of the toxins were estimated by comparing the dose-adjusted intramuscular AUC0-∞ of toxin to the corresponding intravenous AUC0-∞ . The dotted-line curve in Figure 3D shows the serum cardiotoxin concentration following intravenous whole venom administration that declined in a bi-exponential manner; while the solid-line curve depicts its intramuscular absorption with a Tmax of 0 . 5 h and a terminal phase parallel to that of intravenous profile . The pharmacokinetic parameters of the “in-venom” cardiotoxin following the intravenous and intramuscular administrations are shown in Table 5 . The pharmacokinetic parameters of cardiotoxin when only the toxin was administered are also listed for comparison ( see Discussion ) . Most of the pharmacokinetic parameters of the in-venom cardiotoxin were comparable with values obtained when only purified cardiotoxin was administered , with the major exceptions of a longer elimination half-life ( T1/2β ) and a lower clearance ( CL ) for the in-venom cardiotoxin ( Table 5 ) .
Since snake venom is a mixture of hundreds of proteins and peptides , it is therefore virtually impossible to investigate the pharmacokinetics of each individual toxin when the whole venom was administered into rabbits . As such , in this study , we only selected three representative toxins of N . sumatrana venom ( neurotoxin , cardiotoxin and phospholipase A2 ) for pharmacokinetic investigations . These three toxins also represent the major types of toxins in the venom . It should be noted that accurate quantitative measurement of individual toxins in the serum of experimentally envenomed animal using ELISA assay is not always feasible because of the immunological cross-reactivities observed among the snake venom toxins [37] . Indeed , our immunological cross-reaction studies revealed extensive cross-reactivity between the phospholipase A2 and polypeptide neurotoxin purified from N . sumatrana venom , demonstrating that unrelated venom proteins of distinctive primary structures and biological functions may share common antigenic domains [27] , [38] . As such , in the present report the pharmacokinetics of N . sumatrana venom purified phospholipase A2 , neurotoxin and cardiotoxin was studied after intravenous or intramuscular injection of a sub-lethal dose of each toxin into rabbits . Double-sandwich ELISA was developed in which specific anti-toxin IgG's ( i . e . anti-PLA2 IgG , anti-NTX IgG , anti-CTX IgG ) were used to measure the serum toxin antigen levels following injections of the individual toxins into rabbits . The individual serum concentration-time profiles of the toxins , as with the whole venom , injected intravenously were also best fitted to an open two-compartment pharmacokinetic model , where the toxins distributed between central and peripheral compartments . Following intravenous administration , the individual toxins i . e . phospholipase A2 , neurotoxin and cardiotoxin demonstrated shorter distribution half-lives ( 0 . 56–0 . 66 h ) compared to the whole venom ( 0 . 93 h ) , reflecting a more rapid distribution of the purified toxins on entering the systemic circulation . On the other hand , unlike that observed for the whole venom , there was no fluctuation pattern during the absorption and/or distribution phase in the serum concentration-time profile of individual toxins administered intramuscularly . The significant differences in the absorption of the whole venom and toxins were also reflected by the time to reach peak concentration ( Tmax ) . The neurotoxin and cardiotoxin antigens reached their respective peak concentrations much faster than phospholipase A2 , indicating fast absorption of these two low molecular mass toxin molecules ( approx . 8 kDa ) from the injection site into the systemic circulation . These principal cobra toxins are known to directly target receptors and cellular membranes , inducing rather rapid tissue responses compared to some viperid toxins the actions of which involve intermediate steps to accomplish the toxic effect , for instance , coagulopathy secondary to defribrinogenation induced by thrombin-like enzymes [32] . The fast absorption of neurotoxin and cardiotoxin likely accounts for the rapid onset of the systemic effects upon cobra envenomation i . e . neuromuscular paralysis and cardiac complications [7] , [9] , [39] . In view of the rapid absorption of these major toxins , meticulous monitoring for early institution of antivenom when indicated becomes crucial in order to alleviate the severity of syndrome and to preempt fatal outcome . Furthermore , all the three toxins exhibited a large Vd , area ( 1 . 6–2 . 2 L ) which are >10 fold of the total blood volume of a rabbit , suggesting that the toxin antigens distributed extensively into the peripheral tissues . This finding is congruent with the large volume of distribution of the whole venom in rabbits as described above . Both the neurotoxin and cardiotoxin ( 2 . 0–2 . 2 L ) showed a larger Vd , area compared to the phospholipase A2 ( 1 . 4 L ) , and this may be because low molecular mass proteins like neurotoxin and cardiotoxin ( with molecular mass of approximately 7–8 kDa ) cross the capillary endothelium more easily than do the larger proteins [13] such as phospholipase A2 ( 16 kDa ) . In this study , the terminal half-lives ( T1/2β ) of neurotoxin and cardiotoxin were similar ( 8 . 6–8 . 8 h ) but shorter than that of phospholipases A2 ( 11 . 7 h ) . This finding is consistently reflected in the systemic clearance of the three toxins , where the clearance values of neurotoxin and cardiotoxin were significantly larger ( indicative of faster elimination ) than that of phospholipase A2 . Assuming that the elimination takes place primarily from the central compartment and probably via the renal excretion route , the faster clearance of neurotoxin and cardiotoxin can be explained by the higher vascular permeability of the two toxins as both are low molecular mass peptides . However , the T1/2β values for the neurotoxin and cardiotoxin determined in this study are substantially different from the terminal half-lives of African cobras' α-neurotoxin ( 15–29 h , in rabbits ) [15] and that of cytotoxin from Chinese cobra , Naja naja atra ( 3 . 5 h , in rabbits ) [12] , suggesting intrageneric variations in the pharmacokinetics of these cobra three-finger toxins . Among the three major toxins , N . sumatrana neurotoxin has the most complete systemic absorption from the injection site , as evidenced by its higher intramuscular bioavailability ( Fi . m . = 81 . 5% ) than that of phospholipase A2 ( 68 . 6% ) and cardiotoxin ( 45 . 6% ) . This is in agreement with the finding of Ismail et al . ( 1998 ) [16] , who reported a bioavailability of 88% for Walterinnesia aegyptia α-neurotoxin . Interestingly , the Fi . m . of cardiotoxin was only 45 . 6% , presumably due to the strong binding affinity of cardiotoxin to the tissues at the injection site resulting in a poor absorption of cardiotoxin into the systemic circulation . On the other hand , the Fi . m . of the phospholipase A2 was 68 . 6% , indicating that a substantial amount of the toxin remained at the injection site . Indeed , bites from N . sumatrana ( and most Naja cobras ) can produce local envenomation characterized by local tissue necrosis involving the cutaneous , muscular and connective tissue layers [2] , [9] , [34] , [40] , [41] . Cardiotoxin and phospholipase A2 have been reported to interact synergistically and possess potent cytolytic activity [42] , [43] , and their substantial unabsorbed amount at the injection site seem to suggest that their toxic effects play an important role in local envenoming , which consequences include tissue necrosis following cobra bites , as well as venom ophthalmia in venom-spitted victims [44] . Although the i . v . pharmacokinetic behavior of neurotoxin is similar to that of cardiotoxin ( particularly in having a rapid absorption with a short Tmax ) , their intramuscular bioavailabilities differed markedly . The relatively low bioavailability of cardiotoxin would suggest that the systemic effects of cardiotoxin may not be that prominent in cobra envenomation , even though the venom contains relatively large amount of cardiotoxins ( 40% of venom content [10] ) . Furthermore , the neurotoxin is known to be much more lethal than both the cardiotoxin and phospholipases A2 , with an approximate 10-fold lower LD50 in mice ( 0 . 1 µg/g , [34] ) . It belongs to α-neurotoxins with high intrinsic activity of inhibiting the motor endplate nicotinic receptors vis-à-vis that of cardiotoxins and phospolipases A2 , the target receptors of which are primarily different and their actions are not crucial in mediating neuromuscular paralysis - the central cobra envenoming feature that leads to rapid death [34] . This is consistent with clinical reports where rapid onset of neuromuscular paralysis ( caused by neurotoxins ) is the most common fatal manifestation of systemic cobra envenomation , where victims may succumb to respiratory failure and death ensues within minutes to hours [9] , [36] , [45] . The pharmacokinetic result in addition to the neuromuscular blockade activity of neurotoxin generally supports the hypothesis that the neurotoxin plays the principal role in systemic envenomation of N . sumatrana , and should be one of the most crucial toxins to be targeted by antivenom . Nevertheless , variations of neurotoxins across cobra species have been reported on their structures and activities , and the phenomenon is likely the clue to varied efficacies of commercially available antivenoms in the cross-neutralization of cobra venoms in the region [46] . The pharmacokinetic profiling method hence appears useful in validating the toxin's role from the pharmacokinetic aspect , and may be further utilized as a tool in assessing antivenom efficacy on the targeted toxin derived from different cobras . In view of the negligible immunological cross-reactivity between cardiotoxin with phospholipase A2 and neurotoxin , it is possible to accurately determine the serum concentration of cardiotoxin following intravenous or intramuscular administration of the whole N . sumatrana venom using the same double-sandwich ELISA developed . This study would help to shed light on whether the pharmacokinetics of an individual toxin could be altered by other venom constituents , and whether the information gathered from the pharmacokinetic study of individual toxins can be applied in situations where the whole venom was injected . The serum concentration-time profile of cardiotoxin when whole venom was injected was found to be similar to that when only purified cardiotoxin was injected ( Figure 3C and 3D ) . It is however noted that when whole venom was injected , cardiotoxin exhibited a longer T1/2β and a lower CL than when only cardiotoxin was injected . The results therefore suggest that the rate of elimination of cardiotoxin in the whole venom is likely affected by the presence of other venom components in the venom due to competition among various venom components for the elimination processes . The results reflect that in N . sumatrana envenomation , pharmacokinetic characteristics of individual major toxins can be largely applied to situations where the whole venom is injected , with the possible exception that the rate of elimination of the toxins determined may be higher than that of the whole venom . On the other hand , the intramuscular bioavailability ( Fi . m ) of cardiotoxin injected with whole venom ( 39 . 5% ) was similarly low , if not even lower , compared to the Fi . m . of cardiotoxin when only the toxin was administered ( 45 . 6% ) , consistent with the indication that cardiotoxin remained substantially unabsorbed at the injection tissue site . In general , the elimination half-life of the whole venom is determined by the toxic components with the longest T1/2β ( phospholipase A2 in the case of N . sumatrana venom ) , while its intramuscular bioavailability is influenced more by the toxic components that is present most abundantly in the venom ( cardiotoxin , in this case ) . In the present pharmacokinetic study of N . sumatrana venom and toxins , both the neurotoxin and cardiotoxin were rapidly absorbed intramuscularly in the rabbits , with neurotoxin achieving the highest systemic bioavailability , while the cardiotoxin and phospholipase A2 possess relatively lower bioavailabilities . These pharmacokinetic findings therefore suggest that the neurotoxin is the principal toxin in systemic envenomation ( fatal neuromuscular paralysis ) , while significant amounts of the cardiotoxin and phospholipase A2 remain bound to the injection site , causing local tissue destruction . Using toxin-specific ELISA , the study also shows that the cobra venom pharmacokinetics is likely an approximation of the pharmacokinetics of individual toxins except for parameters relating to elimination rate due to possible competition of various toxins for the process in vivo . | Naja sumatrana is a medically important cobra species in Southeast Asia . The optimization of snakebite management and the use of antivenom depend greatly on the knowledge of the venom's composition , its biological activities , as well as its pharmacokinetics . The present study on the pharmacokinetics of N . sumatrana venom shows that the systemic bioavailability of this venom in experimental envenomation is similar to N . sputatrix venom determined in an earlier study . The neurotoxin and cardiotoxin exhibited a more rapid absorption and elimination compared to the phospholipase A2 and the whole venom . The venom neurotoxin produced a higher systemic bioavailability than the cardiotoxin and phospholipase A2 , suggesting that the neurotoxin plays the major toxic role in cobra bites . | [
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] | 2014 | Pharmacokinetics of Naja sumatrana (Equatorial Spitting Cobra) Venom and Its Major Toxins in Experimentally Envenomed Rabbits |
The regulation of cleavage plane orientation is one of the key mechanisms driving epithelial morphogenesis . Still , many aspects of the relationship between local cleavage patterns and tissue-level properties remain poorly understood . Here we develop a topological model that simulates the dynamics of a 2D proliferating epithelium from generation to generation , enabling the exploration of a wide variety of biologically plausible cleavage patterns . We investigate a spectrum of models that incorporate the spatial impact of neighboring cells and the temporal influence of parent cells on the choice of cleavage plane . Our findings show that cleavage patterns generate “signature” equilibrium distributions of polygonal cell shapes . These signatures enable the inference of local cleavage parameters such as neighbor impact , maternal influence , and division symmetry from global observations of the distribution of cell shape . Applying these insights to the proliferating epithelia of five diverse organisms , we find that strong division symmetry and moderate neighbor/maternal influence are required to reproduce the predominance of hexagonal cells and low variability in cell shape seen empirically . Furthermore , we present two distinct cleavage pattern models , one stochastic and one deterministic , that can reproduce the empirical distribution of cell shapes . Although the proliferating epithelia of the five diverse organisms show a highly conserved cell shape distribution , there are multiple plausible cleavage patterns that can generate this distribution , and experimental evidence suggests that indeed plants and fruitflies use distinct division mechanisms .
The spatial and temporal regulation of cell shape and cell proliferation are key mechanisms that direct tissue morphogenesis during development . Much of our knowledge of tissue morphogenesis comes from the study of simple epithelial monolayers , 2D planar sheets of strongly adhering cells in which division occurs in the plane of the epithelium . The strong structural constraints and developmental importance of epithelia have inspired a multitude of theoretical and computational models since the early 20th century [1]–[6] . Of these , an important class is topological models , where an epithelium is represented as a planar network ( topology ) . The topology of an epithelium is defined as the network of connectivity between cells ( Figure 1A and 1B ) . Some important topological properties include a cell's polygonal shape , defined as its number of neighbors , and the overall distribution of cell shapes within an epithelium . There are several reasons for considering these properties . First , empirical evidence from our recent work [5] shows that the distribution of cell shapes is conserved in the proliferating epithelia of several diverse organisms , including the Drosophila larval wing disc and the Xenopus tadpole tail epidermis ( Figure 1C and Table S1 ) . Second , polygonal cell shape is linearly correlated with cell surface area ( Figure 1D ) , a longstanding empirical observation known as Lewis' Law [2] , [3] . Third , important developmental processes such as cell division , migration , and intercalation fundamentally alter topology by creating and breaking connections between cells . For these reasons , topological models have been useful both experimentally and theoretically in understanding proliferating epithelia [4]–[8] and other non-biological lattices [9] , [10] . As early as the 1920s , F . T . Lewis documented the connection between cell proliferation and tissue topology , arguing that spatial control of cell divisions could affect the overall distribution of polygonal cell shapes [2] , [3] . Since that time , the relationships between cell shape , proliferation and epithelial topology have been further investigated using both topological models [4] , [5] , [9] , [10] and mechanical models [11]–[13] , exploring a wide variety of phenomena including differential rates of division , adhesion forces , and stochastic divisions . However , due to unknown parameters and simplifying approximations , the specific mechanisms by which global tissue morphology emerges from the local control of cell divisions in epithelial monolayers still remains poorly understood . To better understand proliferation within the larval wing disc of Drosophila melanogaster , we recently developed a stochastic topological model of cell division [5] . Our model mathematically predicts the emergence of a specific equilibrium distribution of polygonal cell shapes ( p* ) , revealing how local stochastic cellular processes can give rise to predictable global tissue properties . The predicted distribution p* was empirically confirmed in the larval imaginal wing disc of Drosophila melanogaster , but also closely matched in the tadpole tail epidermis of Xenopus laevis , the outer epidermis of the cnidarian Hydra vulgaris , and also Lewis's cucumber epidermis ( Figure 1C ) . A common characteristic across these diverse examples is that the epithelia-like tissue undergoes rapid proliferation with minimal cell rearrangement . The apparent conservation of p* across these systems is surprising . Is p* the consequence of a conserved process of cell division across these proliferating 2D epithelia ? Or is it possible that distinct processes of cell division converge upon the same final distribution of shapes ? More generally , how do widely varying division strategies impact global epithelial organization ? Despite much experimental and theoretical progress , previous models have limitations that make it difficult to address these questions . The major difficulty lies in modeling the neighborhood and lineage dependence in cleavage plane choice . For example , our previous model encodes a mean-field approximation that ignores the variability in the number of neighbors gained via the division of neighboring cells [5] . The mathematical models of Cowan et al . [9] do not account for neighboring divisions at all: a cell never gains sides from its dividing neighbors . These models cannot be used to study modes of cell division with any spatial or temporal dependence , both of which are biologically relevant . For example , cleavage patterns with mother-daughter or neighbor-neighbor correlations in cleavage plane choice are common [14] . To explore and characterize the space of plausible cleavage patterns , a more expressive model is required . Here we present a computational model of cell division that enables us to explore a much larger class of biologically plausible division models by directly simulating the topology of a proliferating epithelium from generation to generation . This includes division schemes with spatial and temporal dependence between neighboring cells and mother-daughter cells . Given a division model , we can compute the equilibrium distribution of polygonal cell shapes , along with other tissue-level topological properties . Our findings show that the fraction of hexagons and the variability in cell shape are both important global indicators of local division parameters , and we propose that it may be possible to infer these parameters from empirical data . Furthermore , we describe several division schemes that can reproduce with high accuracy the cell shape distribution seen in five diverse organisms . We use this modeling framework to formulate and explore some of the central theoretical and empirical questions regarding the local-to-global regulation of cell shape in proliferating epithelia .
This model describes a generic proliferating epithelium with no/minimal cell rearrangement . The assumptions are based on experimental evidence from the larval stage wing disc of Drosophila melanogaster , where the absence of cell rearrangement , roughly uniform cell division rates , and cleavage plane restrictions , appear to hold [5] . These assumptions also appear to be approximately valid for the other proliferating epithelia presented in Figure 1 , for example in plants , where rearrangement does not occur [2] , [3] . However in some cases , rearrangement may occur more frequently and there may be a higher occurrence of tetravalent vertices and three-sided cells; for those systems the model can be modified to include those aspects , although this is beyond the scope of the current paper .
The wide spectrum of shape distributions produced by different CPMs raises the intriguing possibility of inferring the CPM based solely on empirical observations of global epithelial topology . For example , a hypothesis for a cell division strategy in a given epithelium might be rejected simply by comparing the empirical cell shape distribution with the one predicted by the CPM . Here we present the results of comparing our simulated CPMs to cell shape distribution data from natural proliferating epithelia in a diverse array of organisms . We use data , collected and published previously by our group [5] , on Drosophila melanogaster ( larval wing disc , arthropod ) , Xenopus laevis ( tadpole tail epidermis , vertebrate ) , and Hydra ( adult outer epidermis , cnidarian ) . In addition , we have included previously published data from two plants , Cucumis ( cucumber epidermis ) from the paper by F . T . Lewis [3] and Anagallis arvensis ( meristem ) courtesy of J . Dumais [16] . These natural epithelia show a strongly conserved cell shape distribution with between 42–48% hexagons and a standard deviation of 0 . 83–0 . 98 sides ( Figure 1C and Table S1 ) . | Cell division is one of the key mechanisms driving organismal growth and morphogenesis . Yet many aspects of the relationship between local cell division ( how a cell chooses an orientation to divide ) and global tissue architecture ( e . g . , regular versus irregular cells ) remain poorly understood . We present a computational framework for studying topological networks that are created by cell division; this framework reveals how certain tissue statistics can be used to infer properties of the cell division model . Recently it has been observed that five diverse organisms show almost identical cell shape distributions in their proliferating epithelial tissues , yet how this conservation arises is not understood . Using our model we show that the low variation observed in nature requires a strong correlation between how neighboring cells divide and that although the statistics of plants and fruitflies are almost identical , it is likely that they have evolved distinct cell division methods . | [
"Abstract",
"Introduction",
"Model",
"Results/Discussion"
] | [
"developmental",
"biology/morphogenesis",
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"biology",
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] | 2009 | Modeling and Inferring Cleavage Patterns in Proliferating Epithelia |
Transcription factor ( TF ) binding is determined by the presence of specific sequence motifs ( SM ) and chromatin accessibility , where the latter is influenced by both chromatin state ( CS ) and DNA structure ( DS ) properties . Although SM , CS , and DS have been used to predict TF binding sites , a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs . Using budding yeast as model , we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers . In addition , simultaneously considering CS and DS further improves the accuracy of the TF binding predictions , indicating the highly complementary nature of these two properties . The contributions of SM , CS , and DS features to binding site predictions differ greatly between TFs , allowing TF-specific predictions and potentially reflecting different TF binding mechanisms . In addition , a "TF-agnostic" predictive model based on three DNA “intrinsic properties” ( in silico predicted nucleosome occupancy , major groove geometry , and dinucleotide free energy ) that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data . This intrinsic property model allows prediction of binding regions not only across TFs , but also across DNA-binding domain families with distinct structural folds . Furthermore , these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression . Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families , it is TF agnostic and likely describes general binding potential of TFs . Thus , our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome .
Transcription factors ( TFs ) regulate expression of target genes by interacting with specific TF binding sites ( TFBSs ) . Because knowledge of TFBSs is crucial for understanding regulatory relationships between TFs and their target genes , a number of in silico TFBS identification approaches have been developed to complement expensive and time-consuming experiments [1] . However , TFBSs are usually short and highly degenerate , making them difficult to be identified [2] . In particular , TFBS identification based solely on the occurrence of specific sequence motifs is typically accompanied by a high false positive rate [3] , and only a small subset of the large number of predicted sites are actually bound by TFs [4] . This high error rate in TFBS prediction is due to the fact that , in addition to sequence motifs , accessibility to the chromatin significantly influences whether a TF can interact with its binding sites [5 , 6] . For example , a large portion of predicted motif sites that are not bound by TFs tend to be located in chromatin inaccessible to nucleases [7 , 8] . Conversely , location of accessible chromatin tends to be correlated with experimentally defined TF binding regions [8–12] . Moreover , there is increasing evidence for association between chromatin accessibility and TF binding [13–16] . Chromatin state ( CS ) , i . e . histone modifications and nucleosome occupancy [17 , 18] , is a major determinant of chromatin accessibility and TF binding [19] . For example , histone acetylation weakens charge-dependent interactions between histones and DNA , thus increasing the chromatin accessibility for the transcription machinery [20] . Currently , chromatin accessibility mainly evaluated based on DNase I hypersensitivity has been used to distinguish genomic regions preferable for TF binding from regions that are not bound [9 , 21–23] . However , because chromatin accessibility is controlled by chromatin remodelers and histone modifications , it is expected that considerations in addition to DNase I hypersensitivity will be important to capture the influence of chromatin accessibility on TF binding . In addition to CS , TF binding affinity is affected by 3D molecular structure properties of naked DNA that directly interact with TFs [24] as well as genomic regions flanking the bound sites [25–27] . Some TFs recognize unique patterns of DNA structure independent of the specific sequences [28 , 29] . Therefore , most of the state-of-the-art TFBS identification methods supplement sequence motif models with either CS [7 , 30–35] or DNA structure ( DS ) [28 , 29 , 36–38] features . But no methods incorporate both CS and DS for TFBS prediction . Thus , the relative contribution of these features to binding of different TFs and how combining CS and DS may further improve TFBS predictions remains unclear . To investigate the impact of jointly considering CS and DS properties on the computational identification of TFBS , we systematically assessed how well 11 CS and 10 DS features were correlated with bound and unbound regions using a set of yeast chromatin immunoprecipitation ( ChIP ) data [39] . We next applied a machine-learning framework to evaluate the contributions of sequence motif , CS , and DS to TF binding prediction . Based on the prediction model , we further defined the most informative features required for identifying binding regions for each TF to determine how CS and DS may differentially influence TF binding . Importantly , some aspects of chromatin states ( e . g . nucleosome occupancy [40] ) and DNA structure properties [41] can be predicted with genomic sequences alone . These features can be considered “intrinsic properties” of DNA , and in this study we show that the “intrinsic property model” has a comparable performance compared to the full model incorporating experimental data . It was recently proposed that , by integrating multiple datasets including the degree of conservation , transcript annotation , and histone modifications , a TF agnostic binding prediction model can be generated without using any TF-specific sequence motif information [42] . This raises the question whether a TF agnostic model can also be constructed by considering intrinsic properties . To evaluate the generality of the intrinsic property model , we applied the model to predict binding regions across TF domain families with divergent structural folds and binding mechanisms . Our findings indicate that it is possible to establish a TF agnostic model of regulatory region identification that works across TF families . Finally , to further demonstrate that the regions predicted to be TF bound by the intrinsic property model are biologically relevant , we tested whether target genes predicted to be bound by the same TFs tend to have more similar expression profiles , which would suggest co-regulation . Our finding is consistent with this expectation indicating that the predicted bound regions are relevant to transcriptional regulation and are relevant biologically .
In addition to sequence motif ( SM ) , chromatin state ( CS ) [7 , 30–33] and DNA structure ( DS ) [28 , 29 , 36–38] features have been shown to be informative for TFBS identification . However , earlier studies focused on either SM and CS features or SM and DS features . Thus the relative contributions of SM , CS , and DS features , both singly and in combination , towards TF binding prediction remains unclear . It is also not known if there are significant differences in how these factors influence prediction performance among different TFs . Knowledge of these differences has the potential to reveal varying molecular mechanisms of TF and TFBS interactions . To address these questions , we first examined the relationships between TF binding regions determined in genome-wide ChIP analysis of 40 TFs [39] and 23 features including two SM [43] , 11 CS [44] , and 10 DS [41] features ( Table 1 ) . When considering binding regions of all the TFs jointly , 16 of the 23 analyzed features showed significant differences between bound and unbound regions ( two-sided Wilcoxon rank-sum test , p-values were adjusted by false discovery rate control for multiple testing , black rectangles in Fig 1A ) . Among SM features , the minimum p-values of motif-based predictions ( ScerTFpvalue ) are significantly smaller in bound compared to unbound regions ( subsequently examined by one-sided Wilcoxon rank-sum test , p = 4 . 6×10−11 ) . However , SM features have consistently higher p-values compared to all but one CS and three of the 10 DS features , indicating that CS and DS in general are better predictors of TF binding . For example , histone H3 ( H3 ) occupancy , a proxy for nucleosome occupancy , was significantly lower in bound than unbound regions ( subsequently examined by one-sided Wilcoxon rank-sum test , p = 1 . 2×10−130 ) , consistent with the fact that bound regions tend to be exposed on the nucleosome surface [45] or tend to have lower nucleosome occupancy [46] . Our findings also agree with the notion that histone modifications can influence TF activities [47] as all histone mark related features have p values ranging from 4 . 6×10−5 ( H3K4me2 ) to 5 . 6×10−217 ( H3K4me1 ) . Compared to CS features , the p values of test statistics of DS features tend to be higher , indicating DS features may play a less significant role in discerning TFBS . However , the differences in test statistics can be influenced by how CS and DS features were measured . Thus we cannot conclude whether CS is more important than DS or vice versa . Nonetheless , values of the DNA major and minor groove geometry related features ( ΔPC1 and PC4 ) and average dinucleotide free energy ( ΔPC2 ) are significantly different between bound and unbound regions ( two-sided Wilcoxon rank-sum test , highest p = 4 . 7×10−8; Fig 1A ) . Our findings are consistent with studies indicating that DS features such as the geometrical properties of the DNA major groove ( PC1 ) and minor groove ( PC4 ) , and stability ( i . e . free energy ) ( PC2 ) of the DNA helix could influence TF binding [48–50] . We next investigated the 23 features for each of the 40 TF individually ( Fig 1A and P-values in S1 Fig ) . TFs differ greatly in which features are significantly different between bound and unbound regions . Taking RAP1 as an example , values for most of the CS features are significantly different between bound and unbound regions , yet none of the DS features is significant ( Fig 1C ) . In contrast , for ZAP1 , many DS but few CS-related features have significant test statistics ( Fig 1D ) . However , some TFs , such as MSN2 , REI1 , SPT23 , and SWI5 , show significant differences both in CS and DS features ( Fig 1A ) . Interestingly , values of five features ( ScerTFhit , PC1 , PC2 , ΔPC4 , and ΔPC5 ) , which appear uninformative when considering binding regions of all TFs together , are significantly different between bound and unbound regions for a number of TFs . For example , the numbers of motif occurrences in regions of interest ( ScerTFhit ) are significantly higher in bound regions of INO4 , PUT3 , RAP1 , RPH1 , and SWI5 than unbound regions ( all five TFs with p-values < 10−3 , S1 Fig ) , indicating these TFs tend to bind to homotypic TFBS clusters [51 , 52] . In addition , the values of ΔPC4 ( DNA minor groove geometry ) and ΔPC5 ( tilt and roll angles of dinucleotides ) are significantly higher in bound than unbound regions for ZAP1 ( one-sided Wilcoxon rank-sum test , p = 1 . 0×10−4 ) and REI1 ( one-sided Wilcoxon rank-sum test , p = 9 . 1×10−5 ) . These results suggest that , for these TFs , the differences in DNA minor groove geometry and tilt and roll angles of dinucleotides are likely important factors in binding site recognition . In addition to comparing properties of bound and unbound regions , we were interested in determining how well may SM , CS , and DS features may allow us to distinguish bound regions of a TF vs . bound regions of other TFs . To this end , SM , CS , and DS feature value distributions between bound regions of a TF and bound regions of the remaining TFs were compared ( Fig 1B , P-values in S2 Fig ) . We found that TFs differed the most in ScerTFpvalue indicating that differences in bound sequences is a dominant factor differentiating specific TF binding . Nonetheless , most CS features and one DS feature ( PC2 ) are also significantly different between regions bound by different TFs . Taken together , our findings indicate that sequence specificity conferred by motifs , chromatin modifications , and/or DNA 3D structures may play distinct roles in determining binding specificity among these TFs , regardless of whether we compared bound vs . unbound regions or bound regions of one TF vs . the rest . In addition , CS and DS may be considered separately to identify binding regions of some TFs . But for others , these two feature sets should be simultaneously incorporated into a prediction model . Multiple SM , CS , and DS features are significantly distinct between bound and unbound regions of all TFs ( Fig 1 ) , indicating that they may be informative features for TFBS prediction . Earlier studies have shown that CS [7 , 30–33] and DS [28 , 29 , 36–38] can be incorporated for TFBS prediction , but they have not been considered together . To assess the utility of jointly considering CS and DS in TF binding prediction , we applied a machine-learning framework based on random forest [53] using these features to predict a genomic region would be bound by a specific TF or not . Random forest was chosen because: 1 ) it runs efficiently on large databases; 2 ) it systematically interprets the importance of each feature as well as their underlying relationships; 3 ) it is one of the most accurate learning algorithms currently available [53] . First we compared the performances of two random forest classification models: the first using only the two SM features ( SM-only ) and the second using all 23 features ( SM+CS+DS ) to determine the contribution of CS and DS features in binding region prediction . The performance of each classification model was measured using the averaged F-measure in ten independent runs , each with 10-fold cross-validation ( Figs 2 and S3; other measurements including the area under Receiver Operating Characteristic curve ( auROC ) , specificity , and accuracy are shown in S1 Table ) . F-measure is a value representing both precision ( the proportion of predicted regions that are true binding regions ) and recall ( the proportion of true binding regions that are predicted ) . A perfect binding region classification model will have F-measure of 1 , and a random model ( with both classes , bound and unbound , equally likely ) based on our dataset will have an F-measure of ~0 . 5 ( for the relationship between F-measures and auROC values , see S4 Fig ) . Among TFs , SM+CS+DS model F-measures ( average = 0 . 73 ) are significantly higher than those of the SM-only model ( average = 0 . 57; one-sided Kolmogorov–Smirnov test , p = 5 . 0×10−14 ) , demonstrating that , overall , the prediction performance is significantly better when taking CS and DS into consideration ( Fig 2A ) . In addition , considering CS and DS led to significantly better binding region predictions for every single TF ( Figs 2 and S3 ) . Nonetheless , there is a fairly large variance in the degree of improvement . For INO4 and SNF1 , the F-measures increased 208% and 113% , respectively . On the other hand , for BAS1 and PHO2 , the improvements were marginal ( 6% and 7% , respectively , S3 Fig ) . One possible reason for the marginal improvement is because the SM features alone may allow relatively good predictions . Consistent with this possibility , we found that the degree of performance improvement by incorporating CS and DS is significantly negatively correlated with the performances of the SM-only model ( Pearson correlation coefficient ( r ) = −0 . 76 , p = 9 . 5×10−9 , Fig 2B ) . This finding further suggests that some TFs are likely CS and DS independent . However , in most cases TF binding is significantly influenced by CS and/or DS judging from the fact that incorporating CS/DS features improves binding region identification . Similar conclusions can also be reached when predicting TF-specific binding by comparing bound regions of a single TF to bound regions of the other TFs ( the one-TF-vs-rest model , Fig 2C and 2D ) . The average performance of the one-TF-vs-rest model when considering SM+CS+DS features is 0 . 73 , the same as the performance of models based on bound/unbound regions ( Fig 2 ) . The F-measures for models considering CS/DS features are better than or equal to those of SM-only models because all values are either above or right on the diagonal line ( Fig 2C ) . Similarly , there is a significantly negative correlation between the degrees of performance improvement when considering CS/DS features and the F-measures of the SM-only models ( Fig 2D ) . These findings indicate that CS/DS features are particularly useful when models based on SM alone do not perform well for predicting binding regions . For these reasons , CS/DS features complement SM features in TF-specific binding prediction . Therefore , the contribution of CS/DS features in the one-TF-vs-rest model cannot be ignored . However , the performance improvement of the one-TF-vs-rest model by considering CS+DS features is not as pronounced compared to the improvement in the model differentiating bound/unbound regions . Thus to further reveal the contributions of CS/DS features to binding region prediction , in subsequent sections we focus on the models differentiating bound/unbound regions for each TF . To assess the contributions of SM , CS , and DS individually , as well as their combinations , to binding region predictions , seven random forest classification models were generated for each TF ( Fig 3 ) . We found that the prediction performance of the CS-only and DS-only models was significantly better than models using SM-only ( Fig 3A , one-tailed KS test , p = 5 . 6×10−7 and p = 3 . 0×10−4 , respectively ) . In addition , consistent with our finding that TFs differ greatly in their CS and DS value distributions ( Fig 1 ) , the performance correlation between the CS-only and DS-only models is rather weak ( Fig 3B , r = 0 . 40 , p = 1 . 1×10−2 ) . This suggests that DS and CS features have significantly different contributions in the prediction of binding regions for different TFs . We also found that the performance of the CS+DS model is significantly better than CS-only or DS-only models ( Fig 3A , one-tailed KS test , p = 7 . 5×10−3 and p = 7 . 3×10−4 , respectively ) . Compared to the union of predictions by the CS-only and the DS-only models ( 32 , 274 sites correctly predicted by either the CS-only model , the DS-only model , or both ) , the CS+DS model correctly predicts only 528 more cases ( S5 Fig ) . Thus it appears that , at least in the framework that we used , the effects of CS and DS are additive without a significant interaction term . Based on the performance of the seven random forest classification models in predicting binding sites , the 40 TFs can be clustered into three groups ( Fig 3C ) . For all groups , the best performing model considers both CS and DS feature sets . The main difference between these groups is which individual feature set is more important . Group 1 consists of TFs whose binding regions can be predicted by considering either CS or DS , whereas DS and CS are better predictors for Group 2 and Group 3 TFs , respectively . Although DS dominated in Group 2 , the prediction performance was enhanced when CS features were included . However , CS features themselves contribute little to predicting binding regions of TFs in Group 2 . In contrast , CS features dominated in Group 3 , but jointly considering DS led to better predictions . However , in this case , DS features themselves were insufficient to predict Group 3 TF binding regions . In most cases , adding SM does not increase prediction performance . Our findings suggest that Group 2 TFs may rely on variations within the DNA structure ( indirect readout ) rather than sequence base-specific recognition ( direct readout ) to recognize binding sites [54] . Furthermore , because CS features alone are not informative in predicting Group 2 TFs , their binding may not be regulated in a predominant fashion by chromatin state . Consistent with this notion , none of the TFs in Group 2 had reported interactions with histone modifiers [18] . In contrast , eight of the 14 TFs in Group 3 ( ASH1 , CIN5 , FKH2 , GCN4 , REB1 , SKN7 , SWI4 , and UME6 ) interact with histone modifiers [18] , consistent with our findings that binding regions of Group 3 TFs are best predicted with CS features . Another line of evidence indicating the importance of histone modification in Group 3 TF binding region prediction is that six TFs in Group 3 ( ASH1 , CIN5 , FKH1 , FKH2 , SKN7 , SWI4 ) have strong histone modification signals around their TFBSs [30] . Experimental evidence also showed that one Group 3 TF , RAP1 , competes with nucleosomes for DNA binding [17] . The observation of different TF subclasses echoes a recent study that classified TFs into pioneer , settler , and migrant classes based on DNase I footprints in bound regions [55] . Binding of settler TFs can be determined solely based on chromatin accessibility , whereas binding of migrant TFs is dependent on specific cofactor interactions . The pioneer TFs , on the other hand , can bind closed chromatin . Although the three TF groups ( Fig 3 ) we defined differ in how chromatin state influences TF binding , it remains unclear how these groups may correspond to the TF classes Sherwood et al . [55] defined . This is because the groups we defined are based on relative contributions of various chromatin state features with no information on the mechanisms . But the Sherwood et al . [55] classes are defined based on mechanistic details of the interactions between TFs and chromatin . Taken together , our findings highlight the importance of simultaneously considering CS and DS in a TF binding region prediction model and that the contributions of CS and DS features in predicting binding regions are highly TF-specific . Our findings indicate that DS and CS feature sets contain informative signals that can be used for TF binding region identification . Given that some of the 11 CS , 10 DS , and two SM features are better correlated with bound regions than others ( Fig 1 ) , a major question is what the relative contributions of each of the 23 features are to the binding region prediction models of each TF . To address this , an importance analysis was conducted to assess the relative importance of each feature . Importance is defined as the mean decrease in accuracy when the feature in question is removed . We found that the proxy for nucleosome occupancy ( H3 ) , DNA major groove geometry ( PC1 ) , and dinucleotide free energy ( PC2 ) were the three most important properties for predicting binding for most TFs ( arrows , Fig 4 ) . The importance of nucleosome occupancy agrees with the observation that TF binding regions tend to be located in nucleosome depleted regions [45 , 46] . DNA major groove geometry ( PC1 ) and dinucleotide free energy ( PC2 ) are significantly correlated with DNA accessibility and stability , respectively , and both are known to affect TF binding [48–50] . Nonetheless , there is substantial variation in the relative importance of features among subsets of TFs . For example , H3K4me1 was particularly important for INO2 , INO4 , GCR1 , CHA4 , GAL4 , and GCN4 . In addition , the geometrical characteristics of DNA minor groove ( PC4 ) were especially important for CHA4 , SNF1 , STP1 , and FHL1 . Considering that H3 , PC1 , and PC2 are particularly important for binding region predictions ( Fig 4 ) , we asked whether our binding region prediction model could be simplified by using these three major features alone . Remarkably , the performance of the simplified ( 3 features , average F-measure = 0 . 69 ) and the full ( 23 features , average F-measure = 0 . 71 ) models is not significantly different ( two-sided KS test , p = 0 . 10; S6 Fig ) . In addition , the correlation between the performance of the two models is significant ( r = 0 . 81 , p = 1 . 7×10−10 ) . Our findings indicate that nucleosome occupancy and the two DNA structure properties provide nearly sufficient information for identifying regulatory regions in a genome . It is important to note that the DNA structure properties used in the simplified model above are predicted from genomic sequences . On the other hand , the nucleosome occupancy data used above is experimentally derived . Given that nucleosome occupancy can be predicted with reasonable accuracy based solely on DNA sequence [40] , next we asked if the in silico predicted occupancy can replace the experimental histone H3 occupancy data in TF binding prediction . The performance of the simplified 3-feature model with predicted nucleosome occupancy is comparable to the performance based on the same model but with experimentally derived nucleosome occupancy ( S6 Fig , two-sided Wilcoxon rank-sum test , p = 0 . 35 ) . We should note that the 3-feature model has reduced performance compared to the original 23-features model ( S6 Fig , two-tailed KS test , p = 1 . 3×10−2 ) . Thus some of the features excluded in the simplified model are clearly relevant . Nonetheless , considering that the simplified model use only features that can be predicted with genomic sequences alone , it performs surprisingly well . Given that the features used in the 3-feature model can be determined based solely on genomic sequences , they are referred to as “intrinsic properties” , and the simplified 3-feature model incorporating predicted nucleosome occupancy data is referred to as the “intrinsic property model” . Although the intrinsic property model does not perform as well as the full model , the intrinsic property model performs similarly as the experimental data-based 3-feature model and out-performs the SM-based model . The significance of our finding is that the three intrinsic property features can be calculated based on DNA sequences alone , highlighting the possibility of predicting binding regions simply using DNA sequences . In addition , the three intrinsic properties do not include SM-related features which traditionally have been used extensively for identifying binding sites using DNA sequence . It is intriguing that the model based on three purely computationally derived features significantly outperforms the traditional , SM-based model . To extend our finding beyond the 40 yeast TFs analyzed thus far , we used the intrinsic property model to predict binding regions for 161 yeast TFs that have ChIP data [39] but not sequence motifs in ScerTF [43] . We found that the intrinsic property model ( average F-measure = 0 . 68 ) significantly outperformed a random predictor ( average F-measure = 0 . 50 , bootstrapping p < 1×10−4 ) . The result indicates that our binding prediction model can be applied to TFs without relying on known binding sequence motifs . But we should note that the predictions we made here are based on the intrinsic property model that predicts general rather than specific TF binding . There are at least two explanations for our ability to construct a predictive model based on a subset of TFs . The first is that the training set contains TFs from multiple DNA binding domain families . Thus the model can be used to predict binding regions of the test set TFs in the same domain family . Alternatively , the intrinsic property model may be universal . That is , the model is general enough that knowledge of CS and DS feature measurements for some TFs is sufficient for predicting TFs with distinct structural folds and binding mechanisms . To distinguish between these two possibilities , we conducted a cross-DNA-binding domain ( DBD ) study . First a model was trained with binding sites of TFs with a particular DBD . The DBD-specific model was then used to predict the binding regions of TFs containing another DBD . Five common types of DBDs were analyzed: helix-turn-helix , zinc finger , leucine zipper , winged helix and helix-loop-helix . The results showed that cross-DBD predictions have F-measures ranging from 0 . 58 to 0 . 77 , which are all significantly better than random predictions ( bootstrapping p-value < 0 . 0001 ) ( Fig 5 ) . Moreover , the performances of cross-DBD predictions are comparable to self-DBD predictions , and in three families cross-DBD predictions are actually better ( Fig 5 ) . Taken together , the intrinsic property model allows the prediction of TF binding regions in a non-DBD-specific manner , consistent with the interpretation that the intrinsic property model is sufficiently general to make reasonable predictions of TF binding regions for the additional 161 yeast TFs . One question is that , if the contributions of SM , CS , and DS features to binding regions predictions differ greatly between TFs ( Figs 3 and 4 ) , how binding region prediction of a TF is feasible without using the binding data of the TF in question . There are two explanations . First , as shown in Fig 3C , TFs could be classified into three groups based on the contribution of SM , CS , and DS features in predicting TF binding regions . These groupings are feasible because some TFs apparently have similar SM/CS/DS feature values . This indicates that the binding regions of a TF , X , can be predicted based on consideration of other TFs with similar SM/CS/DS feature values ( i . e . TFs in the same group as TF X ) . Second , one advantage of the random forest approach is in integrating multiple features and identify relevant combinations of decision cutoffs for various features . This approach is feasible even when each feature has only weak contribution to accurate prediction of binding regions of a TF . In our case , there are some similarities in SM/CS/DS features among subsets of TFs ( which may or may not be in the same group , Fig 3C ) and these similarities , although far from perfect , can be captured by the random forest method as indicated by the reasonable performance of our models ( Fig 5 ) . Our result also suggests that a significant component of TF binding depends on simply these three intrinsic properties of genome sequence: in silico predicted nucleosome occupancy , DNA major groove geometry , and dinucleotide free energy . Thus , provided the genome sequence is available , it is feasible to establish a TF agnostic regulatory region identification model . Although the TF binding profile is known to be dictated in part by non TF-specific features [9 , 16 , 21] , our intrinsic property model is the first computational model for describing general binding properties of TFs by integrating CS and DS features that are derived from solely genomic sequence . Notably , the intrinsic property model , trained by current data in the unicellular model budding yeast , was independent of cellular state . On one hand , this indicates that the intrinsic property model is sufficiently general and its prediction is not influenced by how the experiments were performed . On the other hand , it remains to be determined to what extent this intrinsic property model is applicable to predict binding in different cellular contexts . The binding data we have analyzed so far are from ChIP-chip experiments that do not cover the entire yeast genome , although Venters et al . [39 , 56] indicated that the genome-wide regulatory maps derived from their ChIP-chip dataset ( one of the dataset used in this study ) are similar to those obtained from the high-density ChIP Affymetrix microarrays . To assess whether our findings may be influenced by the limitations in microarray resolution and genome coverage , we collected ChIP-seq data for five yeast TFs that overlap with our original 40 TFs with ChIP-chip data and where experiments were conducted under similar conditions [57–60] . For each TF , an intrinsic property model was trained by ChIP-chip data and then applied to distinguish between ChIP-seq peaks and random sequences . We found that the averaged performance is similar between models predicting ChIP-chip binding probes and those predicting ChIP-seq signal peaks ( S2 Table and S8 Fig ) . Most importantly , results from one platform are not necessarily better than the other . Although there is still room for improvement , the ChIP-chip data are useful and our conclusions are not significantly affected by the type of data . Identification of functional TFBSs that are bound by TFs and consequently impact gene expression has been one of the critical challenges in gene regulation studies [61] . Recently , several studies have addressed this challenge by integrating chromatin accessibility data [11 , 62 , 63] . However these associations between TFBS and chromatin accessibility have not yet been considered in predictive models of gene expression . In the predictive framework outlined in the previous section , we demonstrated the feasibility of using only intrinsic properties that can be calculated from genomic sequence to establish a model identifying functional TFBSs among sequence motif sites within the yeast genome . Next , we examined whether these TF binding regions predicted by the intrinsic property model are relevant in regulating the expression of target genes . The rationale is that genes containing regions predicted to be bound by the same TF would have a higher probability of being co-regulated by that TF and thus would display more highly correlated expression patterns under specific conditions compared to genes that are not predicted to be bound by the same TF . To determine if this is the case , time course expression data from four different “conditions” ( cell cycle [64] , galactose [65] , mating [66] , and nutrient deprivation [67] ) were analyzed . Here the intrinsic property model provides a TF agnostic binding profile , which we used as a filter to distinguish functional TFBSs from the vast number of motif occurrences . We use only the intrinsic property model trained by the 40 TFs instead of the individual model for each TF; therefore , the binding data for each TF tested is unnecessary . TFs regulating gene expression under each of these four conditions ( referred to as condition-specific TFs ) have been defined [68] , and their Position Weight Matrices ( PWMs ) are available [43] . We first asked which genes contained motif sequences matching the PWM of each condition-specific TF in their promoter regions . Here the TFs analyzed do not include the initial 40 TFs used to establish the binding region prediction models . Note that the presence of a motif within a promoter is not definitive evidence of TF binding . Next , we classified each gene containing motif sequences of the TF in question into two gene sets . In the “bound” ( or accessible ) set , all positions of the motif sequences are within regions predicted to be bound by the TF based on the intrinsic property model ( see Methods ) . In the “unbound” set , none of the motif sequence positions overlap with predicted bound regions . If the predicted bound regions are biologically relevant , we expect that genes in the bound set will have higher expression correlation under each condition . Consistent with this expectation , the gene expression patterns of bound sets tend to be more coherent than unbound sets ( Table 2 and S7 Fig ) . These observations support our hypothesis that motif occurrences within predicted bound regions are more strongly correlated with gene expression than those within predicted unbound regions , demonstrating the feasibility of identifying authentic TFBSs using the intrinsic property model . Our results also show the advantage of using the intrinsic property model to identify functional TFBSs ( i . e . it can remove unbound motif occurrences which are false positives ) . TF binding must have both a TF-specific as well as TF-agnostic components where the specific component dictates how different sites are bound by different TFs and the agnostic component is describing the general tendency of protein-DNA interactions . We found that , through integrating SM , CS , and DS features which have not previously been studied in combination , binding region prediction models can be established for both TF-specific and TF agnostic predictions . Our results demonstrate that models considering CS and DS features outperform a model considering SM features and highlight the importance of simultaneously considering CS and DS in yeast TF binding identification . Nonetheless , TFs differ greatly in which SM/CS/DS features allow prediction of their binding regions and can be classified into three distinct groups based on whether these features and/or combination of features are informative . The implication is that the specific primary sequence , chromatin accessibility , and 3D DNA structure contribute differently to the binding of these TFs to DNA . Thus , in addition to SM features , CS and DS features can also contribute to predictions of TF-specific binding . We also show that nucleosome occupancy , DNA major groove geometry , and dinucleotide free energy are particularly relevant features for TF binding prediction . Because these three features can either be predicted or calculated based on genome sequence alone , we refer to them as ( DNA ) intrinsic properties . Our analyses demonstrated the comparable performance of the intrinsic property model based on these three features to the model using all 23 SM , CS , and DS features . Most importantly , the intrinsic property model can be applied not only between TFs but across TF DBD families . Given that the DBD families analyzed are found in evolutionarily divergent eukaryotic lineages , our finding suggests that it is possible to construct a TF agnostic regulatory region prediction model that can potentially be applied to sequenced species . This is consistent with an early suggestion that a general TF binding model can be constructed by considering conservation , transcript annotation , and histone modifications [42] . Taking this one step further , our findings suggest that a reasonable TF binding region prediction is feasible based on properties that can be inferred directly from genomic sequences alone . Although there is room for further refinement , the intrinsic property model can serve to provide first pass prediction of potential TF-bound regions . These results not only highlight the importance of considering the features that can be extracted from genomic sequences in modeling binding in a TF agnostic fashion , but also highlight the contributions of both CS and DS features in predicting general TF binding . We expect that such a model can significantly contribute to a better understanding of transcriptional regulation , particularly in species with little or no regulatory genomic resources . This study focuses on the unicellular model budding yeast , and thus cell-type specific binding , which is an important question and challenge in multicellular eukaryotes , should be addressed in future studies .
The features used in this study are shown in Table 1 . For each yeast TF , the PWM was downloaded from ScerTF [43] . We scanned the DNA sequence with PWMs using Matrix-scan , a program in the RSAT package [69] , to identify putative binding sites ( p < 0 . 001 ) . Two sequence motif features were then extracted for each PWM: 1 ) the minimum p-value of sites matching the PWM and 2 ) the number of motif occurrences ( p < 0 . 001 ) . Chromatin state features included histone H3 occupancy , binding data of two acetyltransferases ( ESA1 and GCN5 ) , and the signal levels of seven histone modifications ( H4K5ac8ac12ac16ac , H3K9ac , H3K14ac , H3K4me1 , H3K4me2 , H3K4me3 , H3K36me3 , H3K79me3 ) , with all experiments performed on YPD grown budding yeast [44] . The value of each feature used in our analysis was the average signal intensity of the ChIP-chip probes covering more than half of each analyzed region ( 60 bp bound or unbound genomic region for a TF according to TF binding ChIP data , see next section Random forest classification ) . All the chromatin state feature values were normalized to the level of histone H3 occupancy [44] . The DNA structure features include 125 conformational and thermodynamic dinucleotide properties collected from DiProDB [41] as of Aug . 2013 . We first applied principal component analysis to reduce the data size and to consolidate overly similar DNA structure properties . The top five principal components ( PC1 to PC5 , S3 Table ) that explain 83 . 3% of variance in DiProDB properties were used for subsequent analyses . The biological meaning of each PC was interpreted from top 10 DiProDB properties having highest PCA loading coefficients ( i . e . the weight by which the variable should be multiplied to obtain the component score , indicating the correlation between a DiProDB property and a principal component ) . For example , PC1 is annotated as DNA major groove geometry because seven of the top 10 DiProDB properties are geometrical characteristics of DNA major groove ( such as DNA major groove distance , DNA major groove width , and DNA major groove depth ) . We calculated two feature values for each DNA structure PC: ( 1 ) the average of the PC values of dinucleotides ( step size of one base pair ) for each analyzed genome region ( PCx ) , and ( 2 ) the difference of the average PC values between each analyzed region and both its 5’ and 3’ flanking 30 bp regions ( ΔPCx ) . The prediction models of TF binding were generated by the random forest classification model [53] using the sequence motif , chromatin state , and DNA structure features . The training and testing binding data were from a genome-wide ChIP-chip study of 201 regulatory proteins in S . cerevisiae grown at 25°C in rich media [39] . For each TF , the bound probes on the tiling microarray ( at a 5% FDR threshold ) were used as the positive dataset and the same number of unbound probes ( with the lowest signals ) as the negative dataset . Because 75–300 bp DNA fragments were hybridized on the tiling microarray [39] , only parts of the positive probe regions are actual binding sites . To further refine the regions used as the positive set , we searched from 240 bp upstream to 240 bp downstream of each probe region to obtain a narrower 60 bp region that contained the best hit of the sequence motif for the TF in question in its center . In addition , we filtered out any probe with missing values for any analyzed feature . TFs with less than 30 bound probes were not considered for random forest classification to avoid over fitting . Because we are interested in dissecting the relative contributions of SM , CS , and DS features , we could only focus on 40 TFs with annotated sequence motif information in both the ScerTF database [43] and in the ChIP-chip dataset [39] . An additional 161 TFs for which sequence motifs are not available ( i . e . not in ScerTF or less than 30 bound probes in the ChIP-chip dataset ) were used only for testing the performance of the developed model , and the testing data were simply defined as the bound probe regions at a 5% FDR threshold and the same number of unbound probes with the lowest signals . We adopted a 10-fold cross-validation procedure repeated ten times to examine the performance and robustness of the random forest models . The two parameters of random forest , ntree = 500 ( the number of built trees ) and mtry = p1/2 ( the number of randomly sampled features for each tree , in which p is the number of features ) , were used following the suggestion of Liaw et al . [70] . To evaluate the performance , we calculated F-measure , the harmonic mean of precision ( True Positive / ( True Positive+False Positive ) ) and recall ( True Positive / ( True Positive+False Negative ) ) . In addition , auROC was calculated for comparison using the ROCR package in R [71] . We also measured the importance of each feature X as the reduction in accuracy after dropping feature X in the model . All the random forest analyses were conducted in R using the randomForest package [70] . To further predict TF-specific binding , the one-TF-vs-rest model was developed for each TF similar to the bound-unbound model except that the negative data was the regions bound by the other 39 TFs . For each TF , a one-TF-vs-rest model was evaluated by comparing the TF in question to each of the rest 39 TFs . For each TF , the 39 performance measures were then averaged and reported in Fig 2 . Because the numbers of bound regions among TFs are similar , the above scheme allows us to avoid the extreme unequal sample size problem if we compared the bound regions of a TF ( a small number ) to the regions bound by any of the other 39 TFs ( a large number ) . To evaluate the influence of the ChIP data platform type , we collected five yeast TFs with peaks identified in experiments conducted under similar conditions [57–60] . The positive dataset contains all 60 bp regions with ChIP peaks in its center . The negative dataset contains randomly chosen 60 bp regions without any ChIP peak . We analyzed time-series mRNA expression data for four “conditions” including cell cycle [64] , galactose [65] , mating [66] , and nutrient deprivation [67] . For each condition , we first identified binding sites of previously defined condition-specific TFs [68] ( TFs which overlapped with the initial 40 TFs used to establish the binding region prediction models were excluded ) by scanning PWMs from ScerTF database [43] with the tool Matrix-scan[69] . For each condition-specific TF , a gene containing its TFBSs ( motif mapping p < 0 . 001 ) in its promoter region ( defined as ≤1000 bp of intergenic region upstream of the transcriptional start site ) was classified into one of the following three gene sets: 1 ) all TFBSs are within regions predicted by the intrinsic property model to be bound by the TF in question , 2 ) no TFBS is within predicted bound regions , and 3 ) some TFBSs are within predicted bound regions . We define the first two groups as bound and unbound gene sets respectively , and exclude the last group in the following analyses due to its ambiguity . For each condition , only TFs that have at least ten genes in both the bound and unbound sets are considered in this study . To test the hypothesis that the within-group expression correlation of genes in the bound set is significantly higher than that of genes in the unbound set , we calculated the maximal single time-lagged ( i . e . shifting the expression profile of one gene forward or backward one time point relative to the other gene ) Spearman correlation [72] for each pair of genes within each set . Absolute values were taken to consider active and repressed effects simultaneously . Subsequently , we test if this distribution of correlation coefficients was significantly larger for bound set than unbound set by one-sided Kolmogorov-Smirnov test . | Identification of transcription factor binding sites based on sequence motifs is typically accompanied by a high false positive rate . Increasing evidence suggests that there are many other factors besides DNA sequence that may affect the binding and interaction of TFs with DNA . Through the integration of sequence motif , chromatin state , and DNA structure properties , we show that TF binding can be better predicted . Moreover , considering chromatin state and DNA structure properties simultaneously yields a significant improvement . While the binding of some TFs can be readily predicted using either chromatin state information or DNA structure , other TFs need both . Thus , our findings provide insights on how different histone modifications and DNA structure properties may influence the binding of a particular TF and thus how TFs regulate gene expression . These features are referred to as sequence “intrinsic properties” because they can be predicted from sequences alone . These intrinsic properties can be used to build a TF binding prediction model that has a similar performance to considering all features . Moreover , the intrinsic property model allows TFBS predictions not only across TFs , but also across DNA-binding domain families that are present in most eukaryotes , suggesting that the model likely can be used across species . | [
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] | [] | 2015 | Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast |
Species B human adenoviruses ( Ads ) are increasingly associated with outbreaks of acute respiratory disease in U . S . military personnel and civil population . The initial interaction of Ads with cellular attachment receptors on host cells is via Ad fiber knob protein . Our previous studies showed that one species B Ad receptor is the complement receptor CD46 that is used by serotypes 11 , 16 , 21 , 35 , and 50 but not by serotypes 3 , 7 , and 14 . In this study , we attempted to identify yet-unknown species B cellular receptors . For this purpose we used recombinant Ad3 and Ad35 fiber knobs in high-throughput receptor screening methods including mass spectrometry analysis and glycan arrays . Surprisingly , we found that the main interacting surface molecules of Ad3 fiber knob are cellular heparan sulfate proteoglycans ( HSPGs ) . We subsequently found that HSPGs acted as low-affinity co-receptors for Ad3 but did not represent the main receptor of this serotype . Our study also revealed a new CD46-independent infection pathway of Ad35 . This Ad35 infection mechanism is mediated by cellular HSPGs . The interaction of Ad35 with HSPGs is not via fiber knob , whereas Ad3 interacts with HSPGs via fiber knob . Both Ad3 and Ad35 interacted specifically with the sulfated regions within HSPGs that have also been implicated in binding physiologic ligands . In conclusion , our findings show that Ad3 and Ad35 directly utilize HSPGs as co-receptors for infection . Our data suggest that adenoviruses evolved to simulate the presence of physiologic HSPG ligands in order to increase infection .
Human adenoviruses ( Ads ) have been classified into six species ( A to F ) currently containing 51 serotypes . Most Ad serotypes utilize the coxsackie-adenovirus-receptor , CAR , as a primary attachment receptor [1] . However , this is not the case for species B Ad serotypes [1] . Species B Ads form two genetic clusters , B1 ( Ad3 , Ad7 , Ad16 , Ad21 , and Ad50 ) and B2 ( Ad11p , Ad14 , Ad34 , and Ad35 ) [2] . This classification of species B partially correlates with tissue tropism but does not indicate receptor usage . Recently , we have suggested a new grouping of species B Ads based on their receptor usage [3] . Group 1: ( Ad16 , 21 , 35 , 50 ) nearly exclusively utilize CD46 as a receptor; Group 2: ( Ad3 , Ad7 , 14 ) share the same non-identified receptor/s which we refer to as receptor X; Group 3: ( Ad11p ) preferentially interacts with CD46 , but also utilizes receptor X if CD46 is blocked [3] . Importantly , our previous study showed that receptor X is identical for Ad3 , 7 , 11p and 14 [3] . This novel receptor-usage based grouping system is supported by studies from others and us that also found CD46-usage for Ad serotype 11p , 16 , 21 , 35 and 50 but not for serotype 3 and 7 [4]–[7] . The finding that Ad11p is the only species B Ad family member that evolved to efficiently use both CD46 and receptor X has also been indicated by other previous studies from Gustafsson et al . and Marttila et al . [4] , [7] . Marttila et al . confirmed that CD46 blockade on human cells did not affect Ad3 and Ad7 infection , only partially inhibited Ad11p infection and completely abolished infection by serotype 16 , 21 , 35 and 50 [4] . For Ad14 , Marttila et al . suggested that infection of this serotype might partially depend on CD46 , however this finding was apparently not significant as indicated by the margin of error in the Ad14 infection assay of this study [4] . Thus , together with the findings of our studies , it appears that Ad16 , 21 , 35 and 50 nearly exclusively use CD46 , Ad11p uses both CD46 and receptor X , while Ad3 , 7 and 14 only utilize receptor X as attachment receptors for cellular infection . Several groups recently attempted to identify receptor X , and various candidates such as CD46 , CD80 , and/or CD86 were suggested [8]–[11] . However , we and others were so far not able to independently verify that one of these surface molecules represent receptor X . Furthermore studies from others and us ( this study included ) actually provide contrary evidence that CD46 , CD80 and CD86 are not receptor X [3] , [4] , [6] , [7] , [12] . Ads cause continuous outbreaks of acute respiratory disease ( ARD ) in US military training facilities . Studies conducted between 1999 and 2002 revealed that >95% of Ads isolated from recruits were serotype Ad4 . Based on this , the US army reinstated an Ad4 vaccination program . The dominance of Ad4 continued through 2005 , followed by a simultaneous emergence of diverse species B serotypes at the majority of sites . This included the group 1 serotypes 21 and the group 2 serotypes 3 , 7 , and 14 [13] , [14] . Ad14 outbreaks also occurred in the civil population . During March–June 2007 , a total of 140 cases of confirmed Ad14 respiratory illness were identified in clusters of patients in Oregon , Washington and Texas . Thirty eight percent of these patients were hospitalized , including 17% who were admitted to intensive care units ( ICUs ) ; 5% of patients died [15] . Furthermore , ARD caused by outbreaks of Ad35 were reported in the past [16] , [17] . Species B-derived , replication-deficient vectors ( in particular Ad5/3 and Ad5/35 capsid/fiber chimeric vectors ) have recently shown promises as vehicles for gene transfer into multiple human cell types including cancer cells and tissue stem cells [18] , [19] . In contrast to most human Ads , the infection mechanism and cellular attachment receptor/s of several B species serotypes , in particular Ad3 , 7 , and 14 , have been elusive so far . Considering the emergence of diverse species B Ads as a critical pathogen and the potential practical importance of species B based vectors for gene therapy , we attempted to identify the cellular receptors that are used by species B Ads in addition to CD46 . We focused in this study on species B serotypes 3 and 35 that are representative for group 1 and group 2 Ads . The outer protein capsid of Ads consists of 240 trimeric hexon capsomers , 12 pentameric penton base capsomers and 12 trimeric fibers projecting from the vertices of the icosahedral capsid and ending with a C-terminal fiber knob domain ( knob ) . The knob domain has been identified as a major determinant for the initial cellular attachment of Ads to host cells . We therefore set out to discover yet-unknown Ad3 and Ad35 receptors using the corresponding recombinant fiber knobs . We identified cellular heparan sulfate proteoglycans ( HSPGs ) as the main ligand of Ad3 knob but not Ad35 knob . HSPGs were , however , not the main high-affinity receptor for Ad3 ( receptor X ) . Ad3 interacted in a low-affinity manner via fiber knob with HSPGs in order to increase interaction with receptor X ( Ad3 co-receptor function of HSPGs ) . Additionally , we identified a new HSPG-dependent mechanism of Ad35 infection , which was not mediated by the Ad35 fiber knob and was independent of CD46 ( Ad35 receptor function of HSPGs ) . Together , this study shows that both serotypes evolved to utilize HSPGs as co/receptors for infection . HSPGs typically consist of long polyanionic heparan sulfate ( HS ) chains ( repeating disaccharide units of N-acetylglucoseamine and glucoronic/iduronic acid ) , which are covalently linked to a protein core ( mostly membrane proteins , in particular glypicans , syndecans and CD44v3 ) [20] . During HSPG biosynthesis successive modification via N-deacetylation-N-sulphatation , epimerization , 2-O-sulphation , 6-O-sulphation and 3-O-sulfation result in a high structural variety in HS-chains . This allows HSPGs to bind to a wide range of proteins ( including FGF and TGF family members ) [20]–[22] . The classic view of HSPGs is that they serve as co-receptors that bind via their HS chain to various ligands and promote interaction and subsequent signaling via the cognate membrane localized ligand receptors [21] . One example is fibronectin , which binds with different domains to HS-chains of syndecans and to integrins to induce cell spreading and focal adhesion formation [23] . Another example is FGF that requires binding to both , HS-chains and FGF receptor to efficiently induce signaling and endocytosis . HS-chains typically show regions with high , intermediate and low sulfation [24] . In particular , highly sulfated HSPG regions have been shown to participate in the binding of physiologic ligands [20] , [22] , [25] . HSPGs are also exploited as co/receptors by a wide spectrum of viruses and other parasites . Within the family of human adenoviruses , HSPG interaction has been described for two other serotypes ( Ad2 and Ad5 ) so far [26] . HSPG-Ad5 interaction is via fiber knob and has been proposed to trigger macropinocytosis and subsequent uptake into natural target cells , in particular , lacrimal acini cells [27] and similar observations have been made for the uptake of Ad2 into epithelial cells [28] . Importantly , Ad3 has recently also been shown to utilize macropinocytosis as an uptake mechanism into host cells [29] . These findings , together with the data reported in the present study suggest a general role of HSPGs in Ad infection .
For protein receptor identification we used recombinant trimeric Ad3 and Ad35 knobs for pull down assays using purified HeLa membrane proteins as described before [12] . ( HeLa cells express both CD46 and receptor X at high levels [3] ) . Mass spectrometry analysis of pulled down protein revealed CD46 as an interacting membrane protein for Ad35 knob , which is in agreement with our earlier study [12] . However , for Ad3 knob no valid interacting membrane protein/s could be identified ( data not shown ) . We therefore tested the functionality of purified Ad3 and Ad35 knob via competition for cellular attachment with the corresponding viruses . Pre-incubation of HeLa and 293 cells with the recombinant knobs blocked attachment of the corresponding viruses ( Figure 1A and Figure S1 ) . This indicated that the knobs of both viruses are major determinants for attachment of the corresponding viruses . Overall , Ad3 knob showed different blocking properties as compared to Ad35 knob: ( i ) Ad35 knob reduced binding of Ad35 virus particles by ∼90% ( 293 cells ) and 95% ( HeLa cells ) at relatively low concentrations ( 10 ng Ad35 knob/105 cells = 9 . 4×104 Ad35 knob trimers per cell ) ; and ( ii ) Ad3 knob reduced binding of Ad3 virus particles only 63% ( 293 cells ) and 76% ( HeLa cells ) and 50-fold higher concentrations ( 500 ng Ad3 knob/105 cells = 4 . 7×106 Ad3 knob trimers per cell ) were required for this effect . Next , we incubated HeLa cells with an increasing amount of Ad3 and Ad35 knob and detected the amount of bound knob via flow cytometry . In contrast to Ad35 knob an approximately 50-fold higher concentration of Ad3 knob was necessary to reach a similar amount of knob binding to HeLa cells and no saturation point of Ad3 knob binding was observed , whereas Ad35 knob reached a saturation of binding to HeLa cells at 40 ng knob/105 cells ( Figure 1B and Figure S2 ) . In summary , both knobs bound to human cells and competed the binding of the corresponding viruses . However , in contrast to Ad35 knob the Ad3 knob could not be used to identify an interacting membrane protein using mass spectrometry analysis . Since we did not identify a valid Ad3 knob interacting membrane protein via mass spectrometry analysis we next hypothesized that Ad3 knob might interact with carbohydrates . To test this hypothesis we utilized human and non-human cells . Ad3 knob significantly bound to Chinese hamster ovarian cells ( CHO-K1 ) , whereas Ad35 knob only bound to these cells when they were transformed to express human CD46 ( CHO-C2; Figure 1C ) . Removal of sialic acids from the cellular surface of HeLa cells did not reduce Ad3 or Ad35 knob binding , whereas FITC-labeled wheat germ agglutinin ( that specifically interacts with sialic acid ) showed significantly reduced binding ( ∼50% , Figure S3 ) . Next we tested whether pre-incubation of knobs with Heparin might abrogate their attachment to cells . Ad3 knob binding was completely blocked , whereas Ad35 knob binding only minimally affected by Heparin ( Figure 1D ) . Heparin is similarly structured to the heparan sulfate ( HS ) side chains of heparan sulfate proteoglycans ( HSPGs ) , but generally displays higher levels of sulfation as compared to HSPGs [22] . Therefore , we then pretreated HeLa cells with Heparinase I in order to test whether cellular HSPGs might interact with Ad3 knob . Heparinase I reduced HSPG levels on HeLa , CHO-C2 and Y79 cells ( 72% , 79% and 75% decreased HSPG levels , respectively ( data not shown ) . Importantly , Ad3 knob binding was also reduced upon this treatment to similar extends ( HeLa , 61%; CHO-C2 , 102%; Y79 , 66% decreased Ad3 knob binding , respectively ) ( Figure 1E ) . In contrast , for Ad35 knob increased levels of binding were detected on all cell lines upon HSPG removal from the cellular surface . Together these data indicated that Ad3 knob binds to HSPGs on human and hamster cells whereas HSPGs had an inhibitory effect on Ad35 knob attachment to cells . To further investigate the possibility of HSPGs being a receptor for Ad3 knob we utilized CHO cells that are specifically HSPG-negative due to Xylosyltransferase deficiency ( CHO-pgsA-745 [30] ) . These cells did not bind anti-HSPG antibody , whereas native CHO-K1 cells show high levels of anti-HSPG antibody staining . Intriguingly , CHO-pgsA-745 cells did not bind any Ad3 knob at all , which is in stark contrast to native HSPG-positive CHO-K1 cells ( Figure 1F ) . In addition , Ramos cells ( which totally lack HSPG expression , Figure S4 ) do not bind Ad3 knob or Ad3 virus particles ( Figure 1G ) . Interestingly , Ramos cells expressed CD46 and CD86 ( Figure S4 ) and efficiently bound Ad35 knob and virus particles ( Figure 1G ) . Finally , we used 1 µg of each fiber knob and soluble CD46 and Heparin in a highly sensitive western blot assay . Ad35 knob efficiently bound to soluble CD46 , whereas Ad3 knob did not show any binding of soluble CD46 at all ( Figure 1H ) . However , Ad3 knob showed efficient binding to Heparin . To our surprise Ad35 also bound to Heparin in the western blot assay , although to a lesser degree as compared to Ad3 knob . However , the biologic relevance of the detected Ad35 knob interaction with Heparin might be questionable since the cell-based assays did not show any receptor function of HSPGs for Ad35 knob , in particular ( i ) HSPG removal from cells actually increased attachment of Ad35 knob ( probably due to better access to the high-affinity ligand CD46 after HSPG removal ( Figure 1C ) ) and ( ii ) Ad35 knob ( in contrast to Ad3 knob ) did not efficiently attach to CHO-K1 cells ( these cells have high HSPG-levels ( Figure 2D ) but lack the high-affinity ligand CD46 ( Figure 1C ) ) . Overall the western blot assay confirmed our finding that Ad3 knob interacts with Heparin/HSPGs . Together these data indicated that Ad3 knob binds to HSPGs on cells in a specific and low-affinity manner . In contrast , Ad35 knob interacted with CD46 in a high-affinity manner and HSPGs had an inhibitory effect on Ad35 knob binding to CD46 . Since Ad3 knob directly interacted with HSPGs , we used a glycan array in order to screen for further carbohydrates that might be used by the Ad3 or Ad35 knobs . The glycan array currently consists of 320 natural and synthesized glycans that are linked to a glass slide . After Ad3 and Ad35 knob incubation on the glass slides and detection of the relative amount of bound knob via primary and secondary AlexaFluor488-labeled antibody , glycan#26 showed the highest level of binding for Ad3 knob ( Figure 2A ) . Ad35 knob also bound to this glycan , although to overall lower levels . Importantly , glycan#26 is the only glycan in the array that has a total of 3 sulfate groups . All other glycans in the array have less or no sulfates ( Table 1 ) . Disaccharides that were structured identical , with the only difference being reduced sulfation , bound significantly less Ad3 knob ( e . g . glycan#35 , 45 , 288 , 287 , 286 , etc . ; Figure 2B and Table 1 ) . There was a direct correlation of reduced sulfation status ( absence of one , two or all sulfate groups ) and reduced Ad3 knob binding ( Figure 2B and Table 1 ) . Together the array data strongly indicated that the sulfation status of glycans is crucial for Ad3 knob binding . It is important to mention that the array also contained glycans with sialic acid and that these glycans did not show any significant Ad3 or Ad35 knob binding , which argues against a charge-mediated interaction of Ad3 knob with HSPGs ( both , sialic acid and HSPGs are negatively charged at neutral pH ) . To test the possibility that Ad3 knob uses highly sulfated regions within cellular HSPGs , we utilized CHO cells that are Heparan sulfate N-sulfotransferase deficient ( CHO-pgsE-606 , [31] ) . These cells express HSPGs that are grossly non-sulfated . Incubation of these cells with a primary mAb against HS that reacts with an HS epitope that is destroyed by N-desulfation [32] showed ∼75% reduced binding as compared to native CHO-K1 cells ( Figure 2D ) . Ad3 knob binding to CHO-pgsE-606 cells was also greatly reduced ( ∼90% ) as compared to CHO-K1 cells ( Figure 2C ) , which confirmed our finding on the glycan array that Ad3 knob specifically interacts with sulfated HSPGs but not with non-sulfated HSPGs . Ad35 knob attached with no quantitative difference and at nearly non-detectable levels to both CHO-K1 and CHO-pgsE-606 cells . Together these data indicated that Ad3 knob , but not Ad35 knob , binds to highly sulfated regions within cellular HSPGs . Since we identified sulfated HSPGs as a major cellular receptor for Ad3 knob , we next tested whether this interaction would also be required for the attachment of the corresponding Ad3 virus particles . Our earlier studies showed that Ad3 virus particles interact with receptor X in a trypsin and cation-dependent ( EDTA-sensitive ) manner [3] . We therefore hypothesized that binding of the Ad3 knob to human cells would also be ablated by these agents . Indeed , trypsin pretreatment of HeLa cells reduced both HSPG levels ( data not shown ) and binding of Ad3 knob and Ad3 viral particles by ∼80% ( Figure 3A and 3B ) . However EDTA-pretreatment of cells had no inhibitory effect on Ad3 knob binding ( Figure 3A ) . This is in contrast to binding of Ad3 virus particles , which was reduced 78% by the same EDTA concentration ( Figure 3B; similar observation on A549 cells , data not shown ) . We next tested whether Heparin pre-incubation of Ad3 virus particles might decrease virus attachment to HeLa cells . However , the same concentration that completely ablated Ad3 knob binding ( Figure 1D ) reduced Ad3 virus particle binding only 27% ( Figure 3C ) . Ad35 virus particle binding was even reduced to a greater extent ( 57% ) via pre-incubation with the same Heparin concentration ( Figure 3C ) . Next , we tested whether Heparinase I pre-treatment of cells would have an impact on Ad3 virus particle binding . In contrast to the Ad3 knob ( Figure 1E ) , Heparinase I pretreatment did not reduce Ad3 virus particle attachment to HeLa or Y79 cells , but slightly increased the number of Ad3 virus particles attached per cell ( HeLa: 13% increase , P = 0 . 072; Y79: 27% increase , P = 0 . 017; Figure 3D ) . Ad35 virus particle attachment showed an even higher increase upon Heparinase I treatment of cells ( HeLa: 34% increase , P = 0 . 057; Y79: 45% increase , P = 0 . 0054; Figure 3D ) . Altogether , this indicated that HSPGs have an inhibitory effect on Ad3 and Ad35 virus particle attachment to cells . To further study the role of HSPGs in Ad3 and Ad35 virus particle attachment we next used CHO-pgsA-745 and CHO-pgsE-606 cells that are grossly deficient for HSPG expression and HSPG sulfation , respectively . Overall , Ad3 and Ad35 virus both attached at comparatively low levels to CHO cells ( e . g . ∼15-fold lower as compared to HeLa cells ) ( Figure 3E ) . Ad3 attached to CHO cells in an EDTA-sensitive manner , which was not observed for Ad35 ( Figure 3E ) indicating that both serotypes utilize different mechanisms for attachment . For Ad3 the highest binding levels were observed for CHO-pgsA-745 cells followed by CHO-K1 and CHO-pgsE-606 cells ( CHO-pgsE-606 versus CHO-pgsA-745: 34% increase , P = 0 . 025 ) . For Ad35 virus the highest attachment levels were observed for CHO-K1 cells as compared to CHO-pgsA-745 and CHO-pgsE-606 cells ( CHO-pgsA-745 versus CHO-K1: 46% increase , P = 0 . 012; CHO-pgsE-606 versus CHO-K1: 44% increase , P = 0 . 019 ) . In summary , these data show that cellular HSPGs were not essential for Ad3 or Ad35 attachment to cells . After studying the role of HSPGs in Ad3 and Ad35 knob and virus particle attachment we next investigated the role of HSPGs in infection by these serotypes . We did not observe any difference in Ad3 and Ad35 induced CPE formation between Heparinase I and the mock pre-treated HeLa or A549 cells as determined via crystal violet and MTT assay ( data not shown ) . However , this result was not surprising since HSPGs have a relatively short half-life on the cellular surface ( 3–8 h ) , and are either ( i ) shed by the action of proteases or specific phospholipases for GPI-linked HSPGs or ( ii ) taken up by endocytosis and recycle back to the surface or can be degraded in the lysosomes , which altogether results in a continuous renewal of cell surface located HSPGs ( a process that is facilitated in infection assays at 37°C , but inhibited in attachment assays at 4°C ) [22] , [25] . Altogether , we conclude that in contrast to attachment assays , in infection assays Heparinase I pre-treatment is not a sufficient model . Overall these data show that partial removal of HSPGs via Heparinase I had no effect on Ad3 and Ad35 infection . Since Heparinase I pretreatment did not affect adenovirus infection , we next employed native CHO-K1 cells ( HSPG expressing ) , Xylosyltransferase deficient CHO cells ( pgsA-745 , HSPG-expression deficient ) and Heparan sulfate N-sulfotransferase deficient CHO cells ( pgsE-606 , HSPG-sulfation deficient ) . A general advantage of these CHO mutants is that they are grossly deficient in HSPG expression ( pgsA-745 ) or HSPG sulfation ( pgsE-606 ) due to enzymatic defects ( as compared to their native counterpart CHO-K1 ) and therefore represent a clear-cut model for investigating the effect of HSPG deficiency . A general disadvantage is that these cells do not express the primary attachment receptor of Ad35 ( CD46 ) and only low levels of receptor X . Low-level expression of receptor X was indicated by Ad3 virus attached to CHO cells , which is less efficient ( as compared to human HeLa cells ) but also EDTA-sensitive ( Figure 3B and 3E ) . In addition , we observed for Ad serotype 3 and 35 that relatively high MOIs were required to induce CPE in CHO-K1 cells , which is not surprising since human adenoviruses replicate generally less efficient in non-human cells . CPE formation correlated with nuclear Ad hexon staining in Ad3 and Ad35 infected CHO-K1 cells ( determined 3 days post-infection ) . Ad3 induced CPE formation and positive nuclear hexon staining at a minimum MOI of 512 plaque-forming-units ( pfu ) /cell in CHO cells ( Figure 4A and 4C ) . However , a ∼5-fold higher MOI ( 2560 pfu/cell ) of Ad35 was required for the same effect in CHO-K1 cells ( Figure 5A and 5C ) . This result indicated that CHO-K1 cells were more susceptible towards Ad3 infection . We next investigated the effect of HSPG-expression deficiency in infection by both serotypes using CHO-pgsA-745 cells . As readout for Ad infection we used CPE formation ( defined as described in Material and Methods; Figures 4C and 5C ) . As readout for Ad-induced cell death we used a MTT assay ( mitochondrial activity of cells; Figures 4B and 5B ) . When compared to native CHO-K1 cells , CHO-pgsA-745 cells were markedly more susceptible towards Ad3 infection . In contrast , CHO-pgsA-745 cells were more resistant towards Ad35 infection , when compared to native CHO-K1 cells . Next we tested the effect of HSPG-sulfation deficiency in infection by both serotypes in CHO-pgsE-606 cells . For Ad35 a similar inhibitory effect on infection was observed as seen in HSPG-expression deficiency . Interestingly , for Ad3 an opposite effect of HSPG-sulfation deficiency was observed as compared to HSPG-expression deficiency . In particular , CHO-pgsE-606 cells were more resistant towards Ad3 infection as compared to CHO-K1 cells . We next tested whether the differences in CPE formation were due to different efficacy of viral uptake or viral replication in these CHO cell lines . First we investigated Ad3 and Ad35 viral particle internalization ( Figure S5 ) . Ad3 showed highest internalization levels in CHO-pgsA-745 cells and lowest internalization levels in CHO- pgsE-606 cells . Ad35 showed highest internalization levels in CHO-K1 cells and lower levels of internalization in both CHO-pgsA-745 and CHO-pgsE-606 cells ( Figure S5 ) . These data correlates with the CPE ( Figures 4C and 5C ) , MTT ( Figures 4B and 5B ) and viral attachment data ( Figure 3E ) and further supports the finding that the susceptibility of CHO cells towards Ad3 and Ad35 infection is directly influenced by the HSPG-expression and -sulfation status . We next analyzed viral replication of Ad3 and Ad35 in CHO cells . In contrast to human A549 cells , CHO cells did not support production of progeny viruses as indicated by decreased numbers of Ad3 and Ad35 plaque forming units 5 days after infection compared to input pfu ( Figure S6 ) . However , we found that CHO cells supported replication of Ad3 and Ad35 viral genomes , although viral genome amplification was at least one order of magnitude less efficient as compared to A549 cells ( Figures 4D and 5D ) . Overall , levels of genomic replication correlated with virus attachment and internalization efficacy ( Figures 3E and S5 ) and virus-induced CPE ( Figures 4C and 5C ) and cell death ( Figures 4B and 5B ) in CHO cells: Specifically , for Ad3 the highest levels of genomic replication were observed in CHO-pgsA-745 cells ( 440% increase , P = 0 . 0017 ) , whereas lower replication levels were detected in CHO-K1 cells ( 153% increase , P = 0 . 0082 ) and no increase of viral genomes was observed in CHO-pgsE-606 cells ( 59% decrease , P = 0 . 0037 ) . For Ad35 only CHO-K1 cells showed viral genome amplification ( 115% increase , P = 0 . 0091 ) , whereas CHO-pgsA-745 cell ( 31% decrease , P = 0 . 073 ) and CHO-pgsE-606 cell ( 67% decrease , P = 0 . 0018 ) infection did not result in increased Ad35 genome levels . Together , from these data we conclude that the HSPG status of CHO cells influences their susceptibility to Ad3 and Ad35 attachment and internalization , which downstream causes quantitative differences in viral DNA replication , CPE formation and virus induced cell death for both serotypes . In summary , we show for Ad3 that HSPG expression deficiency increased and lack of HSPG sulfation decreased infection by this serotype . For Ad35 the data show that lack of HSPG expression and lack of HSPG sulfation both inhibited infection .
The aim of this study was to identify novel cellular receptors that are used by species B Ads . For this purpose we employed recombinant Ad3 and Ad35 knob . Screening assays ( affinity capture/mass spectrometry and glycan array ) indicated that ( i ) CD46 is a ligand of the Ad35 but not the Ad3 knob and ( ii ) cellular heparan sulfate proteoglycans ( HSPGs ) are ligands of the Ad3 but not the Ad35 knob . We subsequently confirmed that the Ad3 but not the Ad35 knob interacted with HSPGs on cells in a cation-independent , sulfation-dependent and low-affinity manner . In contrast to the knob , Ad3 virus particles mainly attached to cells in a cation-dependent , HSPG-independent and high-affinity manner . Therefore our data clearly indicated that HSPGs were not identical to the main Ad3 receptor X . An important conclusion from our data is therefore that the Ad3 knob protein apparently lacks a high-affinity receptor on cells and does not independently interact with the major Ad3 primary attachment receptor . These findings are surprising , since they are in contrast to other adenovirus serotypes , such as Ad2 , 5 , and 35 , for which the knob-interacting proteins have been found by us and others to be identical with the primary attachment receptors of the corresponding viruses [12] , [33]–[35] . Since our data indicates that Ad3 knob is not independently responsible for interaction with the main receptor of Ad3 virus , we currently attempt to identify this/these receptor/s X using whole Ad3 viral particles for pull-down assays and subsequent mass spectrometry analysis . We predict that viral particles are more likely to reveal the full spectrum of Ad3 interacting cell surface molecules , as compared to recombinant Ad3 knob . Overall , for Ad3 our study provides strong evidence that sulfated HSPGs act as co-receptors for this serotype: ( i ) High-throughput screening on a glycan array revealed sulfated glycans as the only significant Ad3 knob ligands; ( ii ) Removal of HSPGs ( via Heparinase I pre-treatment of cells ) inhibited Ad3 knob attachment to human cells; ( iii ) HSPG-expression deficiency ( CHO-pgsA-745 ) ablated Ad3 knob attachment; ( iv ) HSPG-sulfation deficiency ( CHO-pgsE-606 ) ablated Ad3 knob attachment; ( v ) HSPG-sulfation deficiency ( CHO-pgsE-606 ) inhibited Ad3 virus attachment and infection as compared to native CHO-K1 cells; ( vi ) Pre-incubation of human cells with Ad3 knob reduced attachment of Ad3 virus ( most likely because of competitive inhibition for sulfated binding sites on cellular HSPGs ) ; ( vii ) The only human cell line ( Ramos ) that did not express HSPGs was the only human cell line that did not bind any Ad3 knob ( all other human cell lines expressed HSPGs and bound Ad3 knob ) ; ( viii ) Pre-incubation of Ad3 knob with Heparin ablated binding of Ad3 knob to cells; ( ix ) Pre-incubation of Ad3 virus with Heparin partially inhibited Ad3 virus attachment to cells; and finally ( x ) In a western blot assay high levels of Heparin ( but no soluble CD46 ) were bound by Ad3 knob . Together these data indicated that Ad3 virus interacts via fiber knob with sulfated HSPGs in order to increase cellular attachment and infection ( Ad3 co-receptor function; summarized in Figure 6 ) . Although HSPGs apparently acted as Ad3 co-receptors , part of our data indicated that HSPGs also functioned as a barrier for Ad3 attachment and infection ( Figure 6 ) : ( i ) Removal of HSPGs ( via Heparinase I pre-treatment of cells ) did not decrease but slightly increased Ad3 virus particle attachment to human cells , and ( ii ) HSPG expression deficiency ( CHO-pgsA-745 ) markedly increased Ad3 infection as compared to native CHO-K1 cells . In summary , we conclude that our data points towards a dual role of HSPGs in Ad3 infection . We propose that Ad3 evolved to interact with HSPGs via fiber knob ( in a sulfation-dependent and low-affinity manner ) in order to partially overcome the barrier function of these abundantly expressed cell surface molecules and enhance access to the main receptor/s X ( Figure 6 ) . Several candidate attachment receptors for Ad3 have been recently suggested , including CD46 [8] , [11] , CD80 and CD86 [9] , [10] . However , the data of this study and other previous studies strongly argue against these molecules being identical with the main Ad3 receptor X . Some of these data include: ( i ) Ramos cells expressed CD46 and CD86 and bound Ad35 knob and virus particles but did not bind Ad3 knob or virus particles at all ( this study ) ; ( ii ) CHO cells did not express CD46 , CD80 or CD86 but did bind Ad3 knob and virus particles ( this study ) ; ( iii ) Ad35 knob , but not Ad3 knob , bound to soluble and membrane localized CD46 ( this study ) ; ( iv ) Ad3 virus particles efficiently attached to and infected multiple human cancer cells that did not express CD80 and CD86 and received CD46 blockade [3] , [4]; ( v ) CD46 siRNA reduced Ad35 , but not Ad3 attachment to cells [3]; ( vi ) Soluble CD46 inhibited Ad35 but not Ad3 virus particle attachment to cells [3] , [4] . ( vii ) CD80 and CD86 are co-stimulatory ligands for CD28-mediated T cell activation and are expressed in immune cells ( in particular professional antigen-presenting cells upon activation ) [36] or certain leukemia cells ( e . g . Ramos and K562 cells ) but not by epithelial cells that are the natural target of Ad3 infection ( this study ) . There are , however , several possibilities that could reconcile the findings from other groups that CD46 , CD80 and CD86 are utilized as attachment receptors by Ad3 with the contrary data from us and others: ( i ) One possibility would be that Ad3 indeed interacts with CD46 , CD86 and/or CD80 but only with a very low affinity , so that only when very high ectopic receptor expression levels are used in re-expression models ( like BHK-CD46 , CHO-CD86 , CHO-CD80 cells ) a measurable increase of Ad3 interaction with the cell occurs . Indeed , in the studies on CD46 by Fleischli et al . and on CD80/86 by Short et al . , very high ( and arguable non-physiologic ) expression levels of these molecules were used on CHO/BHK cells [8] , [9] . Notably , while Short et al . , reported significant CD80 and CD86 expression on HeLa cells , we were unable to detect these molecules on HeLa cells using flow cytometry . Furthermore , in the study by Fleischli et al . it was reported that a 100-fold higher concentration of soluble CD46 was required to achieve detectable interaction of Ad3 and Ad7 viral particles with this molecule ( when compared to Ad11 viral particles ) [8] . This indicated that although Ad3 might interact with CD46 , the affinity of this interaction might be several orders of magnitude lower as compared to that of Ad11 to CD46 . Importantly , we previously found that Ad3 viral particles have a similar ( and not a several logs reduced ) affinity as compared to Ad11 and Ad35 viral particles to human cells ( Ad3 VP Ka: 3 . 6e9 M−1; Ad11 VP Ka: 4 . 3e9 M−1; Ad35 VP Ka: 6 . 5e9 M−1 ) [3] . In recent studies from us and others the interactions of purified Ad11 and Ad35 knobs with CD46 were found to be of high affinity ( Ad11 knob KD: 2 nM; Ad35 knob KD: 15 . 5 nM ) [35] , [37] . We also tried to determine the Ad3 knob affinity to CD46 in the same study [35] and found that it was ( if existent ) below the sensitivity of the SPR assay ( data not shown ) . The apparently low ( if existent ) affinity of Ad3 to CD46 together with the general absence of CD80 and CD86 expression on human epithelial cells therefore provide further evidence that a yet-unknown attachment receptor X , and not CD46 , CD80 and/or CD86 , mainly mediates cellular attachment and infection of Ad3 . ( ii ) A second possibility would be that forced over-expression of CD46 , CD80 or CD86 on non-human cells could indirectly increase HSPG and/or receptor X levels ( e . g . due to formation of stable complexes or , in case of HS-chains , direct linking ) ; and finally ( iii ) a third possibility could be that CD46 , CD80 and/or CD86 might not be independent attachment receptors but co-receptors for Ad3 . For Ad35 we identified a novel CD46-independent infection mechanism , which is dependent on sulfated HSPGs ( Ad35 receptor function of HSPGs; summarized in Figure 6 ) . The following findings for Ad35 supported this conclusion: ( i ) Pre-incubation of Ad35 virus particles with Heparin markedly reduced Ad35 virus particle attachment to cells; ( ii ) HSPG-expression deficiency ( CHO-pgsA-745 ) reduced attachment and infection of Ad35 as compared to native CHO-K1 cells; and ( iii ) Lack of HSPG-sulfation ( CHO-pgsE-606 ) reduced attachment and infection of Ad35 as compared to native CHO-K1 cells . Intriguingly , recombinant Ad35 knob exclusively interacted with CD46 and not with cellular HSPGs , which indicated that the observed Ad35-HSPG interaction is not mediated by the Ad35 fiber knob but via other viral proteins . This conclusion is supported by the following findings for the Ad35 knob: ( i ) Pre-incubation of Ad35 fiber knob with Heparin reduced only minimally Ad35 knob attachment to cells; ( ii ) Ad35 knob did not bind to CD46-negative cells that express HSPGs; ( iii ) Ad35 knob showed only low binding to sulfated glycans in a glycan array; and ( iv ) Ad35 knob bound strongly to soluble CD46 but only minimally to Heparin in a western blot assay . Altogether , the cell-based assays clearly showed the absence of an Ad35 knob interaction with cellular HSPGs . Our results also showed that , similar to Ad3 , HSPGs can act as a physical barrier for Ad35 attachment to cells , in particular pre-incubation of HeLa cells with Heparinase I strongly increased Ad35 knob and Ad35 virus particle attachment . In contrast , on CD46-negative CHO cells sulfated HSPGs acted as alternative receptors ( Figure 6 ) . In summary our data indicated that HSPG-dependent ( and CD46-independent ) Ad35 infection has in general a lower efficacy . In our experiments , the CD46-Ad35 interaction was the dominant mechanism as compared to HSPG-Ad35 interaction ( when both CD46 and HSPGs were expressed on the cell ) . The HSPG-Ad35 interaction therefore had apparently a lower affinity as compared to the CD46-Ad35 interaction . Ramos cells ( which expressed CD46 , but not HSPGs ) bound very efficiently Ad35 knob and viral particles , which showed that HSPGs are not required for Ad35 attachment to CD46 expressing cells . In summary , the data support a dual role of HSPGs in Ad35 infection: They act as alternative low-affinity receptors for CD46-independent infection ( in the absence of CD46 expression; summarized in Figure 6 ) but they also represent a physical barrier between Ad35 and CD46 ( in the presence of CD46 expression ) . Since sulfated HSPGs can act as Ad35 receptors , the barrier function of HSPGs towards CD46 is unlikely to be due to electrostatic repulsion of the Ad35 capsid and we therefore speculate that HSPGs are more likely to physically block access of Ad35 to CD46 . Because CD46 is expressed on all nucleated cells in humans , the question about the relevance of Ad35 binding studies on cells that lack CD46 arises . Considering our conclusion that HSPGs are a barrier to Ad35-CD46 interaction as well as our recent finding that , in primary polarized epithelial cells , CD46 is trapped in tight junctions ( Robert Strauss , et al . , in preparation ) , one could speculate that CD46 is not accessible on epithelial tissue in vivo . This scenario is not new for adenoviruses . On lung epithelial tissue , CAR , the receptor for most adenoviruses including Ad2 and Ad5 , is expressed only on the basolateral surface and access to CAR is blocked by the glycocalix [38] , [39] . Interestingly , Ad2 and Ad5 also interact with HSPG with low affinity [26] . We therefore hypothesize that adenoviruses , in general , have evolved to interact with the ubiquitinously present HSPGs to gain access to a high affinity receptor . Another focus of our future studies is therefore to study cellular signaling upon Ad-HSPG interaction in vitro and in vivo .
293 ( Microbix , Toronto , Ontario , Canada ) , A549 , K562 and HeLa ( American Type Culture Collection , ATCC ) were cultured in Dulbecco modified Eagle medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) . Y79 and Ramos ( ATCC ) cells were maintained in RPMI 1640 medium supplemented with 20% FBS , 1 mM sodium pyruvate , and 10 mM HEPES . CHO-K1 , CHO-pgsA-745 , CHO-pgsE-606 ( ATCC ) and CHO-C2 cells ( provided by John Atkinson , Washington University , St . Louis , MO ) were cultured in minimal essential medium ( MEM ) supplemented with 10% FBS , 200 µM asparagine , and 200 µM proline . All of the media described above were additionally supplemented with 2 mM L-glutamine , 100 U penicillin/ml , and 100 µg streptomycin/ml ( Pen-Strep ) . Ad3 ( GB strain ) and Ad35 ( Holden strain ) were obtained from the ATCC . Ad3 was also generously provided by Dr . Silvio Hemmi ( Institute of Molecular Biology , University of Zürich , Switzerland ) and found to be identical with the GB strain from the ATCC as determined by sequencing of the viral genomes . Ads were propagated in 293 cells , methyl-3H thymidine-labeled , purified , dialyzed and stored in aliquots as described elsewhere [40] , [41] . Wild-type Ad particle ( viral particle , VP ) concentrations were determined spectrophotometrically by measuring the optical density at 260 nm ( OD260 ) and plaque titering ( plaque forming units , pfu ) was performed using 293 cells as described elsewhere [40] . The pfu∶VP ratios for Ad3 and Ad35 were both 1∶15 . Multiplicities of infection ( MOIs ) are stated as pfu per cell for CPE and MTT assays and as VP per cell for internalization and attachment assays . Monoclonal antibodies ( mAbs ) directed against CD46 ( clone MEM-258; Serotec ) , CD80 ( L307 . 4; PE-labeled; BD Pharmingen , San Jose , CA ) , CD86 ( clone 2331; PE-labeled; BD Pharmingen ) , and HSPG ( clone F58-10E4; Seikagu ) were used for flow cytometry . The knob domains of Ad3 and Ad35 fibers were produced in E . coli with N-terminal tags of six consecutive histidine residues ( 6-HIS ) , using the pQE30 expression vector ( Qiagen , Valencia , CA ) and purified by Ni-NTA agarose chromatography as described elsewhere [35] . The fiber knob proteins were dialyzed against 5 mM KCl , 17% glycerol , and 10 mM MgCl2 . Soluble CD46 was produced in 293 cells stably transfected with soluble CD46 expression plasmid as described elsewhere [35] . FITC-labeled wheat germ agglutinin was purchased from Vector Laboratories ( Burlingame , CA ) . Recombinant Ad3 and Ad35 knobs ( 1 µg respectively ) were separated by polyacrylamide gel electrophoresis and then transferred onto nitrocellulose membranes . Protein samples were loaded in loading buffer ( 50 mM Tris-HCl , pH6 . 8 , 100 mM dithiothreitol , 2% sodium dodecyl sulfate , 10% glycerol , 0 . 2% bromophenol blue ) without boiling . To detect whether recombinant Ad3 and Ad35 knobs bind to CD46 , the blot was incubated with sCD46 in TBS ( 10 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl ) and 3% blotting grade milk ( BIO-RAD , Hercules , CA ) for 1 h at room temperature ( RT ) and then washed three times for 10 min in TBS-0 . 05% Tween20 ( TBS-T ) buffer . The blot was then incubated with anti-CD46 antibody ( clone J4 . 48; Fitzgerald , Concord , MA ) ( 1∶50 ) in TBS and 3% milk for 1 h at RT and then washed three times for 10 min in TBS-T buffer . To visualize binding , the blot was incubated with goat anti-mouse immunoglobulin G ( IgG ) -horseradish peroxidase ( HRP ) ( BD Pharmingen ) ( 1∶1000 ) in TBS and 3% blotting grade milk for 1 h at RT . To detect whether recombinant Ad3 and Ad35 knobs bind to Heparin , the blots were incubated with Heparin-biotin ( Sigma ) ( 1∶1000 ) in TBS for 1 h at RT and then washed three times for 10 min in TBS-T . The blot was then incubated with Streptavidin-HRP ( eBioscience , San Diego , CA ) ( 1∶250 ) in TBS and 3% milk for 1 h at RT and then washed three times for 10 min in TBS-T buffer . Finally , blots were subjected to enhanced chemiluminescence substrate ( Pierce , Rockford , IL ) . Adherent cells were detached by treatment with Versene ( Gibco ) . After being washed , cells were resuspended in 120 µl of wash buffer ( WB; phosphate-buffered saline-1% fetal bovine serum ) and incubated for 45 min at 4°C with mAbs ( final concentration , 1 µg/ml ) . Subsequently , cells were washed with WB twice . For CD46 and HSPG staining cells were subsequently incubated with Alexa Fluor 488 goat anti-mouse antibody ( Molecular Probes , Invitrogen Corporation , Carlsbad , CA ) for 30 min at 4°C . After incubation with the secondary antibody , cells were washed two times with WB . Control samples for CD46 and HSPG staining were incubated with the isotype control as a primary antibody ( final concentration , 1 µg/ml ) and Alexa Fluor 488 goat anti-mouse as a secondary antibody . Control samples for CD80 and CD86 staining were incubated with PE-labeled isotype control ( final concentration , 1 µg/ml ) . Geometric mean fluorescence intensities were determined via flow cytometry using 104 cells per sample and a FACS scan machine ( BD ) . All knob and virus attachment assays were carried out in a final volume of 100 µl in ice-cold adhesion buffer ( DMEM supplemented with 2 mM MgCl2 , 1% FBS , and 20 mM HEPES ) containing 105 cells . 5×104 CHO cells per well were seeded in 24 well plates and 24 h later 3H-labeled Ad was added at multiplicities of infection ( MOIs ) of 15 , 360 VP/cell ( Ad3 ) or 153 , 600 VP/cell ( Ad35 ) . Five days post-infection cells were detached and surface bound viral particles were removed using incubation with Trypsin-0 . 1%EDTA ( Gibco ) for 10 min . Cells were washed twice with 0 . 5 ml of ice-cold WB . After the last wash , the supernatant was removed and the cell-associated radioactivity was determined with a scintillation counter . The number of VP internalized per cell was calculated by using the virion specific radioactivity and the number of cells . Background scintillation was determined using cells that were not incubated with 3H-labeled Ad . Background scintillation was subtracted from scintillation of 3H-labeled Ad incubated samples . 5×104 CHO or A549 cells were seeded in one ml medium per well in 24 well plates and 24 h later infected with multiplicities of infection ( MOIs ) of 100 pfu/cell ( Ad3 and Ad35 , A549 cells ) , 1024 pfu/cell ( Ad3 , CHO cells ) , and 10240 pfu/cell ( Ad35 , CHO cells ) . Cells ( adherent and floating ) and supernatants were collected at time points 0 h and 5 days post-infection for quantification of Ad3 and Ad35 genomes and plaque forming units in order to test for viral replication . 1 . 25×104 CHO-K1 cells were seeded per well in Lab-Tek 8-well chamber glass slides ( Nalge Nunc International , Rochester , NY ) . 24 h later cells were infected with various MOIs of Ad3 ( 0 , 64 , 128 , 256 , 512 , 1024 pfu/cell ) or Ad35 ( 0 , 640 , 1280 , 2560 , 5120 , 10240 pfu/cell ) . Three days post-infection , cells were fixed with Acetone/Methanol and washed twice with PBS . Slides were blocked for 20 min at RT using PBS-5% blotting grade milk ( BIO-RAD , Hercules , CA ) followed by incubation with Cy3-labeled mouse anti-hexon antibody ( concentration 1∶100; clone 20/11; Chemicon International ) and FITC-labeled mouse anti-E-cadherin antibody ( concentration 1∶100; clone 36/E-Cadherin , BD Pharmingen ) in PBS for 1 h at RT . Slides were washed twice with PBS , mounted with Mounting Medium for Fluorescence ( with DAPI; Vector Laboratories ) and then analyzed using a fluorescence microscope . 1 . 25×104 CHO-K1 , CHO-pgsA-745 or CHO-pgsE-606 cells were seeded per well in 100 µl medium in 96 well plates and 24 h later infected with MOIs ranging from 64–1024 pfu/cell ( Ad3 ) or 640–10240 pfu/cell ( Ad35 ) . 7 days post-infection 20 µl of MTT stock solution ( stock concentration 5 mg/ml in PBS ) was added in each well and cells were incubated for 2 h at 37°C . Medium was removed and cells were washed twice with PBS and then air-dried . 100 µl of DMSO/well was added and incubated for 30 min at RT in order to dissolve crystals . Absorbance was measured in plate reader at 546 nm . We used a high-throughput glycan array developed by cores D and H of the Consortium for Functional Glycomics ( CFG; an NIH National Institute of General Medical Sciences initiative ) for identifying specific carbohydrate binding partners for proteins . The glycan binding specificities of Ad3 and Ad35 recombinant fiber knob were screened . The printed array ( Version 3 . 0 ) contained 320 different natural and synthetic glycans ( including sialylated sugars with different linkages and modifications , for example , sulfation , but not heparin sulfate; http://www . functionalglycomics . org/glycomics/publicdata/microarray . jsp ) . The method used for generating the printed array is detailed in a publication by Blixt et al . [42] . Briefly , the array is created using a robotic printing technology that uses amine coupling to covalently link amine-functionalized glycans or glycanconjugates to amine-reactive N-hydroxysuccinimide-activated glass slides . The slides contain six addresses per glycan or glycoconjugate . A printed slide was incubated with Ad3 or Ad35 knob ( 100 µg/ml ) , and then an anti-Penta-His monoclonal antibody ( Qiagen ) ( 1 µg/ml ) was overlaid on the bound knobs followed by a goat anti-mouse AlexaFluor488-labeled secondary antibody ( Molecular Probes ) ( 1 µg/ml ) . The fluorescence intensity was detected using a ScanArray 5000 ( Perkin-Elmer Inc . ) confocal scanner . The image was analyzed using the IMAGENE image analysis software ( BioDiscovery , El Segundo , CA ) . The data were plotted using the Microsoft EXCEL software . Statistical significance was calculated by two-sided Student's t-test . P-values <0 . 05 were considered statistically significant . | In this study , we attempted to identify binding receptors that are used by the two human adenovirus ( Ad ) serotypes 3 and 35 . Ad3 uses yet-unknown receptors and is one of the most common Ads causing epidemic conjunctivitis , and respiratory and gastrointestinal diseases . Ad35 uses the complement receptor CD46 as an attachment receptor and mainly causes infections of the kidney and urinary tract . We utilized novel high-throughput techniques in combination with the recombinant viral proteins ( fiber knobs ) , which mediate the initial interaction of Ads with host cells . We found that both serotypes interacted with cellular heparan sulfate proteoglycans ( HSPGs ) . In subsequent assays , we show that HSPGs were not major receptors , but acted as low-affinity co-receptors for both Ad3 and Ad35 . Ad3 and Ad35 used different viral proteins in order to interact with HSPGs . Both serotypes , however , used the same regions within HSPGs that show high levels of sulfation and are important for binding of extracellular located physiologic ligands . In summary , we show that Ad3 and Ad35 evolved to “highjack” yet another class of cellular surface molecules that are essential for the function of the target host cells and are ubiquitously expressed . This provides new insights into the emerging picture of the infection mechanism of Ad3 and Ad35 . | [
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"and",
"Methods"
] | [] | 2008 | Role of Cellular Heparan Sulfate Proteoglycans in Infection of Human Adenovirus Serotype 3 and 35 |
Small ubiquitin-like modifier ( SUMO ) modification of chromatin has profound effects on transcription regulation . By using Kaposi’s sarcoma associated herpesvirus ( KSHV ) as a model , we recently demonstrated that epigenetic modification of viral chromatin by SUMO-2/3 is involved in regulating gene expression and viral reactivation . However , how this modification orchestrates transcription reprogramming through targeting histone modifying enzymes remains largely unknown . Here we show that JMJD2A , the first identified Jumonji C domain-containing histone demethylase , is the histone demethylase responsible for SUMO-2/3 enrichment on the KSHV genome during viral reactivation . Using in vitro and in vivo SUMOylation assays , we found that JMJD2A is SUMOylated on lysine 471 by KSHV K-bZIP , a viral SUMO-2/3-specific E3 ligase , in a SUMO-interacting motif ( SIM ) -dependent manner . SUMOylation is required for stabilizing chromatin association and gene transactivation by JMJD2A . These finding suggest that SUMO-2/3 modification plays an essential role in the epigenetic regulatory function of JMJD2A . Consistently , hierarchical clustering analysis of RNA-seq data showed that a SUMO-deficient mutant of JMJD2A was more closely related to JMJD2A knockdown than to wild-type . Our previous report demonstrated that JMJD2A coated and maintained the “ready to activate” status of the viral genome . Consistent with our previous report , a SUMO-deficient mutant of JMJD2A reduced viral gene expression and virion production . Importantly , JMJD2A has been implicated as an oncogene in various cancers by regulating proliferation . We therefore further analyzed the role of SUMO modification of JMJD2A in regulating cell proliferation . Interestingly , the SUMO-deficient mutant of JMJD2A failed to rescue the proliferation defect of JMJD2A knockdown cells . Emerging specific inhibitors of JMJD2A have been generated for evaluation in cancer studies . Our results revealed that SUMO conjugation mediates an epigenetic regulatory function of JMJD2A and suggests that inhibiting JMJD2A SUMOylation may be a novel avenue for anti-cancer therapy .
Epigenetics connects genotype to phenotype and disease . Histone modifications are important epigenetic events in the regulation of chromatin structure and gene transcription . Different from acetylation , histone lysine methylation was long considered to be irreversible and therefore garnered little attention in the epigenetics field until the discovery of first histone lysine demethylase ( KDM ) , LSD1/KDM1A [1] . LSD1 catalyzes a flavin-dependent amine oxidation reaction that can only demethylate mono- and di-methylated forms of modified histone lysine residues . Removal of trimethyl groups from histone lysines was first evidenced by the discovery of Jumonji C ( JmjC ) domain-containing histone demethylase 2A ( JMJD2A ) in 2006 [2] . This enzyme catalyzes a Fe ( II ) and α-ketoglutarate-dependent dioxygenation reaction that demethylates the tri-methyl group on modified histone lysine residues . The realization that all histone lysine methylation states are completely reversible established histone methylation as a novel component of the “histone code” for epigenetic regulation . It is therefore not surprising that intensive studies have focused on the role of JMJD2A in epigenetics and disease regulation . JMJD2A ( also known as KDM4A and JHDM3A ) is a histone 3 lysine 9 ( H3K9 ) and lysine 36 ( H3K36 ) trimethyl-specific KDM [2] with specificity towards trimethyl H3K9 ( H3K9me3 ) [3] . Similar to all KDMs , an intact JmjC domain is required for demethylation activity of JMJD2A and a single amino acid mutation on histidine 188 ( H188 ) within JmjC domain completely abolishes demethylation activity [2] . In addition , JMJD2A possess a double tudor domain at its C-terminus [4] . This domain binds to H3K4me3 and H4K20me2/3 and functions as a chromatin recruitment binding module [4 , 5] . However , the target specificity of JMJD2A also depends on its interacting partners . For example , interaction with HP1 enhances H3K36me3 demethylation activity of JMJD2A [6 , 7] . Although considerable progress has been made in understanding the specificity of JMJD2A in binding to and demethylation of histone marks , regulation of JMJD2A-chromatin interactions are largely unknown and requires further analysis . Post-translational modifications ( PTMs ) play important roles in determining protein function . Increasing evidence points to PTM regulation of the functional specificity of KDMs . For example , phosphorylation of JmjC domain-containing histone demethylase PHD finger protein 2 ( PHF2 ) enables PHF2 activation and subsequent formation of a histone demethylase complex that specifically binds to its target promoters and demethylates H3K9me2 [8] . Similarly , phosphorylation of KDM3A determined its specific binding to target genes [9] . In contrast , phosphorylation of LSD1 dissociates corepressors from its histone demethylase complex and consequently impairs the repressive activity of LSD1 [10] . In addition to phosphorylation , modification of KDMs by other PTMs , such as ubiquitination and SUMOylation , has also been reported . Ubiquitination of JMJD2A , KDM4B , KDM5C and PHF8 invariably mediates proteasome-dependent degradation of these KDMs [11–14] . SUMOylation of KDM5B , a transcription repressor that demethylates the activation mark H3K4me3 , prevents its occupancy at target genes [15] . However , little is known about small ubiquitin-like modifier ( SUMO ) modification in regulating other KDMs . SUMO was initially identified as a reversible PTM that regulates protein stability and signal transduction [16–18] . The growing list of SUMO modified DNA binding proteins or transcription factors reveals the importance of SUMOylation in chromatin remodeling complex formation and transcription regulation [16] . Genome-wide studies have shown that SUMO modification may associate with either positive regulation [19 , 20] or negative restraint of gene transcription [20–22] . By using Kaposi’s sarcoma associated herpesvirus ( KSHV ) as a model , we recently showed that SUMO-2/3 specific chromatin modification restrains the transactivation of genes in the KSHV genomic euchromatin region [21 , 23] . This suggests that SUMO-paralog specific chromatin modifications may be involved in the observed variation in the role of SUMO in epigenetic regulation of transcription . However , the SUMO-2/3 target proteins on the KSHV euchromatin region have never been identified . Uncovering the target proteins will not only largely improve our knowledge of SUMO modification in epigenetic regulation but also open an avenue for developing specific inhibitors for therapeutic use . Following our previous report showing the JMJD2A binding profile in the euchromatin region of KSHV latent genomes [24] , we first examined the essentialness of JMJD2A for SUMO-2/3 enrichment in the KSHV genome euchromatin regions upon reactivation . Our results revealed a striking reduction of SUMO-2/3 enrichment in JMJD2A knockdown cells when compared with control cells . We found that JMJD2A is modified by SUMO-2/3 at K471 and that this modification is important for JMJD2A binding on viral chromatin and for viral gene transactivation during KSHV reactivation . Moreover , we identified that KSHV lytic protein K-bZIP , a viral SUMO E3 ligase with specificity towards SUMO-2/3 [25] , enhances SUMOylation of JMJD2A . Emerging evidence has underscored the association of JMJD2A activity with various cancers ( reviewed in [26] ) . Interestingly , we found that wild-type ( WT ) but not SUMO-deficient mutant of JMJD2A was capable of rescuing the proliferation of JMJD2A knockdown cells . Cellular genes that are highly upregulated in JMJD2A-WT during KSHV reactivation are enriched in cancer-related pathways when compared against the SUMO deficient mutant . We also show that JMJD2A binds at the promoter region of the oncogenic TBX3 and that SUMOylation of JMJD2A is required for its full TBX3 induction . Oncogenic viruses have served as important experimental models to identify oncogenes and study the molecular mechanisms underlying oncogenesis . These findings provide some clues to understand potential mechanisms underlying tumorigenesis mediated by JMJD2A . Therapeutic inhibition of JMJD2A has been implicated as a potential target in cancer therapy . Since SUMOylation is essential for JMJD2A binding to target gene promoters and executing its epigenetic function , inhibiting JMJD2A SUMOylation could be a new strategy for cancer therapy .
We have previously reported a global SUMO-2/3 enrichment on KSHV genome euchromatin regions upon viral reactivation [23] . In this study we sought to identify potential SUMO targets residing on viral chromatin . The negative correlation between SUMO-2/3 enrichment and the heterochromatin mark H3K9me3 in the KSHV lytic genome [23] is reminiscent of the inverse correlation between H3K9me3 with JMJD2A in latent viral chromatin that we reported in 2011 [24] . Moreover , in the same report , we demonstrated that the KSHV SUMO E3 ligase K-bZIP interacts with JMJD2A and inhibits its demethylase activity . Together , these results suggest that JMJD2A may be a potential SUMO target on viral chromatin . To study this , we first performed a chromatin immunoprecipitation ( ChIP ) experiment of JMJD2A using chromatin prepared from TREx-F3H3-K-Rta BCBL-1 cells after doxycycline ( Dox ) induction for 12 , 24 , and 36 hours ( S1 Fig ) . A KSHV tiling array [24] was then used to measure the binding of JMJD2A on viral lytic chromatin at 12 hours post induction . The ChIP-on-chip result revealed a comparable binding pattern of JMJD2A throughout the KSHV lytic ( Fig 1A ) and latent ( data published in Fig 4A of J . Virol . , 2011 [24] ) genome . Pearson correlation showed a strong positive relationship between JMJD2A binding on the KSHV latent and lytic genome ( r = 0 . 83 ) as expected . However , Pearson's analysis showed no statistically significant correlation between global SUMO-2/3 enrichment ( Yang et al . 2015 ) and JMJD2A binding ( r = 0 . 21 ) on the KSHV genome at 12 hours post induction . These data suggest that instead of being a genome-wide target for SUMO conjugation , JMJD2A may function in a locus-specific manner . To identify potential JMJD2A binding loci that may responsible for SUMO-2/3 enrichment on KSHV genome during reactivation , we aligned the ChIP-seq data of SUMO-2/3 [23] and ChIP-on-chip data of JMJD2A ( Fig 1A ) on the KSHV lytic genome . We noticed that two viral genome regions , which contain high levels of JMJD2A binding , also displayed a significant increase of SUMO-2/3 ( Fig 1A , blue boxes ) . This finding indicates that JMJD2A may be the SUMO target in these two KSHV genome regions and responsible for the SUMO-2/3 enrichment during viral reactivation . If this is true , loss of JMJD2A will abolish the SUMO-2/3 enrichment . To study this , we performed another ChIP assay of SUMO-2/3 and JMJD2A using chromatin prepared from JMJD2A knockdown TREx-MH-K-Rta-shJMJD2A BCBL-1 and its control cells ( TREx-MH-K-Rta-shCtrl BCBL-1 ) ( Fig 1B ) . KSHV K6 and PAN in the first region and K-bZIP and Orf52 in the second region were chosen for quantitative PCR ( qPCR ) analysis . ChIP-qPCR results showed that JMJD2A knockdown significantly reduced but did not completely abolish SUMO-2/3 enrichment on the promoter regions of KSHV genes in both regions ( Fig 1C , upper panel ) . Orf23 and Orf25 which reside in a low SUMO enrichment region were used as negative controls . The significant decrease of JMJD2A ( Fig 1C , lower panel ) , which correlates with reduced SUMO-2/3 enrichment by knockdown of JMJD2A implies that the SUMO-2/3 enrichment could be due to JMJD2A SUMO-2/3 conjugation at the indicated KSHV genome regions . To examine whether JMJD2A is post-translationally modified by SUMO , we first carried out a cell-free in vitro SUMOylation assay using recombinant and purified SUMOylation E1 and E2 enzymes , the substrate JMJD2A and SUMO paralogs . A higher molecular weight band representing SUMO-modified JMJD2A was observed in reactions containing SUMO-2 and/or SUMO-3 with the highest level of SUMO modification in the presence of SUMO-2/3 ( Fig 2B ) . SUMO modification of JMJD2A was further evaluated in vivo . To this end , we transiently transfected plasmids expressing Flag-tagged JMJD2A together with T7-tagged SUMO-1 or SUMO-2 and -3 in 293T cells and immunoprecipitated ( IP’d ) the total cell lysates ( TCLs ) with M2 affinity beads . Western blot analysis of TCLs using SUMO-1 and SUMO-2/3 antibodies showed the successful overexpression and conjugation of SUMO-1 and SUMO-2/3 in 293T cells ( Fig 2C , right panel ) . Immunoblotting of the precipitated JMJD2A using anti-SUMO-1 and anti-SUMO-2/3 antibodies revealed that it was conjugated by both SUMO-1 and SUMO-2/3 ( Fig 2C , left panel ) . Immunoblotting using anti-JMJD2A antibodies showed a lower level of conjugation with SUMO-1 ( compare lanes 2 and 3 in Fig 2C , left panel ) . Following our discovery of JMJD2A SUMOylation , SUMOsp2 . 0 and SUMOplot Analysis Programs were used to predict potential SUMOylation site ( s ) on JMJD2A . By overlapping the sites predicted by both algorithms , we narrowed down the putative SUMO sites in JMJD2A to three lysines ( K463 , K471 , and K1036 ) ( Fig 2A ) . To determine whether JMJD2A is SUMOylated at these three putative SUMOylation sites , we generated a set of single- ( K463R , K471R , and K1036R ) , double- ( K463R/K1036R ) , and triple- ( K463R/K471R/K1036R ) site mutants of JMJD2A in which the lysine residues of the putative SUMOylation sites were replaced by arginine ( R ) . in vitro SUMOylation assays using purified wild-type ( WT ) and mutant JMJD2A proteins showed the disappearance of the main JMJD2A-SUMO band in the K471R and triple mutants ( Fig 2D ) , indicating that K471 may contribute to the SUMO conjugation of JMJD2A . To confirm this , we further evaluated the capability of the WT and K471R mutant JMJD2A proteins to be modified by SUMO in vivo . Consistently , SUMO modification of K471R mutant was undetectable ( Fig 2E , compare lanes 1–3 versus lanes 4–6 , respectively ) , confirming that K471 in JMJD2A is involved in the conjugation of SUMO to JMJD2A . Moreover , we transiently transfected plasmids expressing Flag-tagged JMJD2A-K463R/K1036R mutant together with T7-tagged SUMO-1 or SUMO-2 and -3 in 293T cells and immunoprecipitated ( IP’d ) the TCLs with M2 affinity beads . Western blot analysis of TCLs showed the successful overexpression and conjugation of SUMO-1 and SUMO-2/3 in 293T cells ( S2A Fig ) . Immunoblotting of the precipitated JMJD2A using anti-JMJD2A , anti-SUMO-1 and anti-SUMO-2/3 antibodies revealed that JMJD2A-K463R/K1036R mutant was conjugated by both SUMO-1 and SUMO-2/3 ( S2B Fig ) . These results provide the first evidence for JMJD2A as a new SUMO target protein . Following our previous report showing that JMJD2A colocalizes and interacts with K-bZIP [24] , a SUMO-2/3 specific viral E3 ligase encoded by KSHV lytic Orf K8 [25] , we hypothesized that JMJD2A is a SUMOylation target of K-bZIP . To determine whether K-bZIP might enhance SUMOylation of JMJD2A , we first performed an in vitro SUMOylation assays using purified K-bZIP . Immunoblotting using anti-SUMO-1 , anti-SUMO-2/3 and anti-JMJD2A antibodies revealed that JMJD2A was SUMOylated by K-bZIP ( Fig 3A ) . When SUMO E3 ligase dead mutant ( L75A ) of K-bZIP that we generated in our previous work [25] was included , we detected SUMOylated JMJD2A bands in WT but not the ligase activity dead mutant of K-bZIP ( S2C Fig ) . This result indicates that K-bZIP promoted the SUMOylation of JMJD2A in an E3 ligase activity dependent manner . However , in vitro SUMO-1 modification of JMJD2A was also observed in the presence of K-bZIP . This might due to the high protein level of SUMO and E3 ligase in vitro that leads to the loss of SUMO paralog specificity . To confirm this , we further evaluated the K-bZIP-mediated SUMOylation of JMJD2A in vivo . We found that co-transfection of a plasmid expressing K-bZIP increased the SUMOylation of JMJD2A to a much higher level using SUMO-2/3 modification than with SUMO-1 ( Fig 3B ) . This indicates that the SUMO-2/3 specificity of JMJD2A persists in vivo . The E3 ligase activity-dependent SUMOylation of JMJD2A by K-bZIP was further confirmed in vivo . As shown in Fig 3C , a significant increase of SUMO-2/3 modification of JMJD2A ( left panel ) but not its SUMOylation deficient K471R mutant ( right panel ) was observed in cells overexpressing WT K-bZIP but not its L75A mutant . These data indicate that JMJD2A is a novel SUMO substrate of viral SUMO E3 ligase K-bZIP . To assess the functional significance of JMJD2A SUMOylation , we first generated rescue cell lines stably expressing WT and SUMO-deficient mutant ( K471R ) of JMJD2A in JMJD2A knockdown TREx-MH-K-Rta-shJMJD2A BCBL-1 cells . Immunoblotting confirmed the similar expression level of shRNA-resistant JMJD2A-WT and JMJD2A-K471R mutant to that of endogenous JMJD2A prior to knockdown ( S3A Fig ) . Given that JMJD2A is required for efficient SUMO-2/3 enrichment on viral promoters during reactivation ( Fig 1C ) , the essential contribution of K471 of JMJD2A in global SUMO-2/3 enrichment on the KSHV genome during reactivation was evaluated using ChIP-seq . The successful induction of K-Rta and expression of K-bZIP after Dox treatment for 12 hours were first confirmed by immunoblotting ( Fig 4A ) . The ChIP experiments were then performed using chromatin prepared from the Dox-induced ( lytic phase ) TREx-MH-K-Rta-shJMJD2A-Flag-JMJD2A-WT and -K471R BCBL-1 cells . High-throughput sequencing was carried out to measure the chromatin binding of SUMO-2/3 following a ChIP assay . ChIP-seq data showed similar SUMO-2/3 distribution in JMJD2A-WT rescue cells when compared with TREx-F3H3-K-Rta BCBL-1 cells [23] . As shown in Fig 4B , the ChIP-seq result showed that the SUMO-2/3 enrichment was reduced on most parts of high JMJD2A binding regions in cells containing the K471R mutant of JMJD2A at 12 hours after viral reactivation ( p = 2 . 27e-103 ) . However , it should be noted that no significant changes of SUMO-2/3 binding was observed in several gene loci within high JMJD2A binding regions . Again , this result further supports the notion that SUMOylation of JMJD2A may function in a locus-specific manner . To further verify the ChIP-seq results , SUMO-2/3 binding on promoter regions of K6 , PAN , K-bZIP and Orf52 was analyzed using real-time qPCR . Orf23 and Orf25 in a low SUMO enrichment region were used as negative controls . Consistent with the ChIP-seq results , qPCR data showed that the genes in high JMJD2A binding regions tested here displayed a significant reduction of SUMO-2/3 enrichment after viral reactivation in cells containing JMJD2A-K471R when compared with WT control ( Fig 4C ) . These results suggest that K471 is the primary SUMOylation site of JMJD2A that is responsible for at least part of SUMO-2/3 conjugation on KSHV chromatin during KSHV reactivation . Our previous report showed that SUMO-2/3 enrichment on the KSHV lytic genome restrains viral gene expression [23] . We therefore hypothesized that SUMOylation of K471 in JMJD2A may also participate in restraining viral lytic gene expression and that loss of SUMOylation in the K471R mutant may result in higher viral gene expression during reactivation . To explore this idea , an RNA-seq assay was performed using total RNA purified from JMJD2A knockdown BCBL-1 cells and rescue cell lines stably expressing WT and K471R of JMJD2A before and after K-Rta-induced reactivation . The expression of JMJD2A , K-Rta and K-bZIP before and after Dox treatment for 24 hours was shown by immunoblotting ( S3B Fig ) . Consistent with our previous report showing that JMJD2A is essential for maintaining the readiness of KSHV genes to be reactivated [24] , when compared with JMJD2A-WT rescue cells ( blue in Fig 5A ) , knockdown of JMJD2A reduced viral gene activation during reactivation ( green in Fig 5A ) . Surprisingly , instead of eliciting a higher induction of viral genes expression , the rescue cell line expressing JMJD2A-K471R showed a lower activation of viral genes at 24 hours after reactivation ( red in Fig 5A ) when compared with WT control ( blue in Fig 5A ) . To validate gene expression changes observed in RNA-seq analysis , expression of K6 , PAN , K-bZIP and Orf52 before and after viral reactivation was analyzed using real-time reverse transcription-qPCR ( RT-qPCR ) . Consistent with RNA-seq results , a slightly , but statistically significant , diminished expression of these viral genes expression was observed during lytic reactivation in JMJD2A-K471R ( Fig 5B ) . Interestingly , the data suggests that JMJD2A-K471R mimics the knockdown of JMJD2A . To verify this observation , we performed a hierarchical clustering analysis using the RNA-seq results of both cellular ( Fig 5C ) and viral ( Fig 5D ) datasets across the panel of JMJD2A knockdown , JMJD2A WT and K471R cells . The result showed that in both datasets the latent cells align at closer distances than lytic cells . For viral genes after reactivation , the aligned distances of WT were far from both knockdown and K471R of JMJD2A ( Fig 5D ) . This result supports the notion that JMJD2A-K471R may function similar to its knockdown . Since our previous report showed that knockdown of JMJD2A reduced virion production [24] , viral titers were determined in TREx-MH-K-Rta-shJMJD2A , -shJMJD2A-Flag-JMJD2A-WT and -K471R BCBL-1 cells at 48 hours with or without Dox treatment . Immunoblotting showed the successful induction of K-Rta and expression of KSHV lytic protein K-bZIP ( S3C Fig ) . KSHV virion-associated DNA was purified from viral particles in supernatants and determined by real-time qPCR as previously described [24] . Similar to the knockdown , the JMJD2A-K471R significantly reduced viral production when compared with JMJD2A-WT rescue cell lines ( Fig 5E ) . SUMOylation has been recognized as a PTM that regulates gene transcription through modulating DNA-binding of chromatin-associated factors [27] . However , functional responses vary among target proteins , as DNA-binding activity may be positively or negatively modulated by SUMO . Our data imply SUMOylation may help stabilize JMJD2A binding on chromatin . To study this , we performed a ChIP in TREx-MH-K-Rta-shJMJD2A-Flag-JMJD2A-WT and -K471R BCBL-1 cells using anti-JMJD2A antibody . Consistent with our hypothesis , JMJD2A-K471R significantly reduced its association with viral chromatin ( Fig 6A ) . In addition , ChIP analysis using anti-H3K9me3 antibody demonstrated significantly higher levels of H3K9me3 in JMJD2A-K471R at three ( K6 , PAN , K-bZIP ) out of four viral promoter regions analyzed ( Fig 6B ) . These findings suggest that SUMOylation at K471 is essential for the association of JMJD2A with chromatin and in mediating the demethylation of H3K9me3 . To further confirm that the histone demethylase activity of JMJD2A is regulated by SUMOylation , we examined H3K9me3 levels in 293T cells transiently transfected JMJD2A-WT or -K471R . The catalytically deficient JMJD2A-H188A mutant was also included as a negative control . Forty-eight hours after transfection , cells were fixed and co-stained with antibodies against JMJD2A and H3K9me3 . Consistent with previous reports , overexpression JMJD2A , but not its catalytically deficient mutant , diminished H3K9me3 levels ( Fig 6C , left and right panel , respectively ) . Immunostaining showed similar H3K9me3 intensity in K471R overexpressing cells versus non-transfected cells ( Fig 6C , middle panel ) . This was further confirmed by in vitro demethylation assay . Flag-tagged WT or K471R JMJD2A proteins purified from Sf9 cells ( S4A Fig ) or IP’d from 293T cells ( S4B Fig ) were incubated with histone proteins in demethylase buffer . Consistently , immunoblotting results showed that JMJD2A-K471R failed to reduce H3K9me3 levels ( S4 Fig ) . Taken together , these results imply that SUMOylation is important for chromatin binding and histone demethylation activity of JMJD2A . JMJD2A was recently proposed as an oncoprotein [28 , 29] . Targeting JMJD2A by a KSHV lytic protein may contribute to the essential role of lytic phase in KSHV oncogenesis . To gain a better understanding of JMJD2A SUMOylation in modulating the host response to KSHV , we first compared the proliferation rate of control and transient JMJD2A knock down SLK cells ( S6A Fig ) . Consistent with previous reports [30 , 31] , knockdown JMJD2A significantly reduced the cell proliferation ( S6B Fig ) . Only WT JMJD2A but not its K471R mutant could rescue the cell proliferation defect in JMJD2A knockdown SLK cells ( Fig 7A ) . Similar result was observed in JMJD2A knockdown and its WT and K471R mutant rescued BCBL-1 cell lines ( Fig 7B ) . These data indicate that SUMOylation is important for JMJD2A regulation of cell proliferation . Consistent with our earlier report showing that binding of JMJD2A on viral chromatin maintains an open state for rapid activation of KSHV genes [24] , our data here showed that SUMOylation of JMJD2A is required for maintaining its binding on the viral genome and an open chromatin structure for viral gene transactivation ( Figs 6 and 5 , respectively ) . Next , we asked whether this was also the case for the JMJD2A regulation of cellular genes . To this end , we first dissected the genes that are up-regulated in TREx-MH-K-Rta-shJMJD2A-Flag-JMJD2A-WT BCBL-1 cells during reactivation ( 628 genes ) into three categories: 1 . 5-fold higher up-regulated ( 253 genes ) , 1 . 5-fold less up-regulated ( 49 genes ) and no change ( 326 genes ) when compared with K471R mutant rescued cells . These data indicate that around half ( 52% ) of the cellular genes up-regulated during KSHV reactivation are independent of JMJD2A status . Consistent with our hypothesis that SUMOylation of JMJD2A maintains an open chromatin configuration for gene transactivation , JMJD2A SUMOylation is important for successful up-regulation of cellular genes ( 40% ) during KSHV reactivation . To identify functional pathways involved in SUMOylation-mediated JMJD2A upregulation of cellular genes during KSHV reactivation , we performed an ingenuity pathway analysis ( IPA ) using the 253 genes that are upregulated in TREx-MH-K-Rta-shJMJD2A-Flag-JMJD2A-WT BCBL-1 cell when compared with the K471R mutant . Gene ontology ( GO ) analysis identified nine significant pathways ( p<0 . 05 ) containing more than ten gene molecules ( Fig 7C ) . Consistent with emerging evidence showing that JMJD2A is involved in various cancers [26] , most of the pathways identified were cancer-related . To narrow down the genes for further validation , genes simultaneously present in more than five of the nine identified pathways were first selected ( Fig 7D ) . We then use ChIP-seq analysis to uncover the direct binding of JMJD2A on the promoter region ( 500 bp up- and downstream of the transcription start site ( TSS ) ) of the identified genes ( S5 Fig ) . Significant binding of JMJD2A was defined as any two peaks height of at least 3 reads per million ( RPM ) within each promoter region . By using this cut-off , we identified four of the twelve genes with JMJD2A-WT but not its K471R mutant binding on their promoter region ( Fig 7D ) . Interestingly , TBX3 is a T-box transcription factor that has been implicated in a wide range of carcinomas [32] ) and in regulation of proliferation [31 , 32] . Significant higher induction of TBX3 mRNA levels in JMJD2A-WT rescued cells in comparison with JMJD2A knockdown and K471R mutant cells during KSHV reactivation was confirmed by RT-qPCR ( Fig 7E ) . In addition , the direct binding of JMJD2A-WT but not K471R mutant on TBX3 promoter region was verified by ChIP-qPCR ( Fig 7F ) . Our data here indicate that TBX3 may be a novel JMJD2A target gene that is responsible for JMJD2A-mediated cell proliferation during KSHV reactivation . However , more detailed analysis is required to elucidate the pathological consequences of TBX3 up-regulation by JMJD2A .
SUMO modification has emerged as an important PTM that regulates protein subcellular localization , stability , protein-protein interaction , and DNA binding [33] . In recent years , increasing evidence showed that covalent modification of histone modification enzymes by SUMO regulates chromatin organization and gene expression . For histone deacetylases ( HDACs ) , SUMOylation increases their transcriptional repression and HDAC activities [34 , 35] . In contrast , SUMO modification of KDM5B reduced its DNA binding and transrepression activities [15] . Mammalian cells contain three protein-conjugating isoforms of SUMO: SUMO-1 , and highly related SUMO-2 and SUMO-3 ( SUMO-2/3 ) . The varied effects of SUMO partly result from the isoforms of SUMO proteins conjugated to a given substrate . In this study , we identified SUMO-2/3 as the modifier of JMJD2A and showed that JMJD2A SUMOylation is required for its DNA binding and transcription derepression activities . SUMO-2/3 is targeted to JMJD2A through a viral SUMO E3 ligase K-bZIP , an immediate-early protein encoded by KSHV K8 . This event is SUMO-interacting motif ( SIM ) -dependent SUMOylation ( S2C Fig ) and allows a specific SUMO-2/3 modification to JMJD2A ( Fig 3B and 3C ) . For E3 ligase-dependent SUMOylation , conjugation usually occurs at the lysine ( K ) residue within ψKxD/E consensus motif . Among the three lysine residues , K463 , K471 , and K1036 , located within the consensus motif of JMJD2A , K471 was identified as being SUMO modified as mutation of this lysine to arginine ( K471R ) significantly reduced JMJD2A SUMOylation ( Fig 2D and 2E ) . Consistent with our previous report showing that K-bZIP is a SUMO E3 ligase with specificity towards SUMO-2/3 [25] and required for SUMO-2/3 conjugation on KSHV genome euchromatin region during reactivation [23] , we showed here that JMJD2A is a new SUMO substrate of K-bZIP and is responsible for SUMO-2/3 enrichment on KSHV genome euchromatin regions during viral reactivation ( Fig 1C ) . However , since induction of K-bZIP is reduced in both JMJD2A knockdown ( Fig 1B ) and K471R mutant ( Fig 4A ) , the reduction of this viral SUMO E3 ligase may also responsible for lower SUMOylation in viral promoters ( Figs 1C and 4C ) . Moreover , since K471R mutant of JMJD2A significantly reduced but not completely abolished the SUMO-2/3 enrichment ( Fig 4C ) , this suggests that there might be still additional SUMO target protein ( s ) present on the KSHV genome . K-bZIP , the viral SUMO E3 ligase that can interact with SUMO protein [25] and is SUMOylated [36] may belong to one of these unknown proteins . Our recent report showed that SUMO-2/3 viral chromatin modification contributes to creation of a repressive environment that restrains viral gene expression during reactivation , as SUMO-2/3 knockdown increases the expression of KSHV lytic genes [23] . However , our data here demonstrated that the viral gene activation ( Fig 5A and 5B ) and virion production ( Fig 5E ) were decreased when the SUMOylation site on JMJD2A is mutated . This indicates that SUMOylation of JMJD2A activates instead of represses the viral gene expression during KSHV reactivation . SUMO modification of chromatin proteins provides a binding platform for protein complex formation , typically via a non-covalent SIM , and stabilizes their DNA binding as the case for the KAP-1 [37 , 38] . The inconsistency between these studies could be due to the essential role of SUMO modification in JMJD2A DNA binding , as our ChIP data show that JMJD2A-K471R loses its occupancy at promoter regions of KSHV genes ( Fig 6A ) . This loss of binding may also explain why the SUMOylation site mutation of JMJD2A resulted in increased H3K9 methylation ( Fig 6B and 6C ) . Loss of JMJD2A binding by the SUMO modification site mutant can therefore be viewed as loss of JMJD2A . This is further supported by hierarchical clustering analysis of the transcriptome data which showed that the aligned distance was closer in knockdown and K471R of JMJD2A ( Fig 5C and 5D ) . Following this concept , the data here are consistent with our earlier finding that JMJD2A is important for maintaining a favorable chromatin structure to facilitate KSHV reactivation , as knockdown of JMJD2A decreased virion production [24] . We hypothesized that though JMJD2A can be viewed as a platform for SUMO-2/3 binding on chromatin , JMJD2A-K471R may mimic the knockdown of JMJD2A , but not of SUMO-2/3 . JMJD2A SUMOylation is worth further investigation since this modification may have other regulatory functions . For example , the stability of JMJD2A is regulated by ubiquitination [11 , 28 , 39] . Analogous to ubiquitination , SUMOylation is a multistep enzymatic modification of proteins at lysine residues . However , in contrast with ubiquitination , SUMOylation does not usually trigger degradation . Interestingly , we observed a gradual reduction of JMJD2A along with KSHV reactivation ( S3B and S3C Fig and Fig 2B in [24] ) . Moreover , the JMJD2A-K471R was found to be degraded at an earlier time point following viral reactivation ( S3B Fig ) . Our data suggest that SUMO-2/3 modification of JMJD2A by K-bZIP during KSHV reactivation helps stabilize JMJD2A from degradation . This hypothesis is consistent with one recent report showing that modification of HDAC1 by SUMO-1 , but not by SUMO-2 , facilitates its degradation [40] . However , more detailed analysis is required to further our understating of JMJD2A protein stability and its regulation by paralog-specific SUMO modification . In addition , little is known about how SUMOylation of JMJD2A is regulated by cellular E3 SUMO ligases . Differing from the ubiquitin pathway enzyme cascade which contains hundreds of E3 ligases , only several E3 ligases ( PIAS family , RanBP2 , and Pc2 ) have been identified so far for the SUMOylation machinery . Moreover , a SUMO E3 ligase , while increasing SUMOylation , is sometimes a requirement for SUMO-paralog specific conjugation . In particular , PIASy has recently been reported to mediate SUMO-2/3 specific conjugation of HDAC1 [40] and poly ( ADP-ribose ) polymerase 1 ( PARP1 ) [41] . Our preliminary data showed that a member of the PIAS family , PIAS3 , acts as specific E3 ligase that promotes SUMO-2/3 modification of JMJD2A ( S7 Fig ) . However , we cannot exclude the possibility of the presence of other cellular SUMO E3 ligases that contribute to SUMOylation of JMJD2A . This question is worthwhile for detailed analysis in the future . It can be imagined that JMJD2A , the first discovered tri-methyl histone demethylase responsible for removing both heterochromatin mark H3K9me3 and active chromatin mark H3K36me3 [2] , should play important roles in various cellular functions , such as maintaining genome integrity and regulating gene transcription . Overexpression of JMJD2A induces copy gains on specific chromosomal domains [42] and degradation of JMJD2A is required for DNA repair by efficient recruitment of 53BP1 to DNA damage sites [43] . For transcription regulation , stabilized JMJD2A chromatin binding is a prerequisite for PPARγ-mediated transcription regulation of genes associated with lipogenesis [39] . In addition , JMJD2A is involved in regulating transcription of neural-specific genes and therefore required for neural stem cell differentiation [44] . Most interestingly , levels of JMJD2A were observed to change during cell cycle progression . Peaking in G1/S phase , JMJD2A antagonizes the occupancy of heterochromatin protein HP1γ , increases chromatin accessibility and consequently promotes S phase progression [13 , 45] . Due to the functional diversity of JMJD2A , it is not surprising that dysregulation of this histone modification “eraser” would contribute to tumor progression . In addition , virally induced JMJD2A SUMOylation appears to activate certain cellular oncogenic pathways , probably providing an explanation for the observation that viral lytic genes are always coexpressed with cellular cancer-associated pathways [46] . Indeed , accumulating studies have noted the overexpression of JMJD2A in various cancers ( reviewed in [26] ) . JMJD2A overexpression may promote transformation by blocking oncogene-induced senescence through transcriptional repression of CHD5 [28] . Direct transcriptional repression of Sp1 by JMJD2A also promotes metastasis of breast cancer [29] . In addition to direct transcriptional regulation , JMJD2A also interacts with transcription factors and functions as either a corepressor or a coactivator . JMJD2A regulates proliferation and apoptosis by functioning as a corepressor for p53 [47] . It also promotes tumor progression by interacting with E2Fs and enhancing the repression of tumor suppressor ARHI expression [48] . In contrast , when interacting with hormone receptors of estrogen ( ER ) [49] and androgen ( AR ) [50] , JMJD2A functions as a coactivator . Taken together , these reports suggest that JMJD2A appears to regulate cellular function by targeting specific sets of genes through interactions with distinct DNA binding proteins . However , little is known about how JMJD2A is regulated . One recent report showed that expression of JMJD2A is suppressed by tumor suppressor sirt2 [51] . Moreover , as mentioned above , the stability of JMJD2A is regulated by ubiquitination [11 , 13 , 39] . In this study , we showed that JMJD2A can be SUMOylated by K-bZIP and it is one of the factors responsible for global SUMO-2/3 enrichment on KSHV genome euchromatin region during viral reactivation . However , our data here demonstrated that SUMOylation is essential for chromatin binding of JMJD2A and established that JMJD2A-K471R may mimic the knockdown of JMJD2A . Consistently , the cell proliferation defect in JMJD2A knockdown SLK and BCBL-1 cells could only be rescued by WT JMJD2A but not its K471R mutant . As an epigenetic regulator capable of regulating genes associated with cancer progression , selective inhibitors for JMJD2A have been developed [52 , 53] . Discovery of paralog-specific SUMOylation of JMJD2A suggests that the development of SUMOylation specific inhibitors may be a novel avenue for anti-cancer therapy .
The doxycycline ( Dox ) -inducible TREx-BCBL-1 , with Myc-His-tagged K-Rta ( TREx-MH-K-Rta BCBL-1 ) , cell line was maintained in RPMI 1640 containing 15% FBS , 20 μg/ml blasticidin and 200 μg/ml hygromycin ( Invitrogen , Carlsbad , CA ) . Latently infected KSHV in TREx-MH-K-Rta BCBL-1 cells were induced with 0 . 2 μg/ml Dox for viral reactivation . The JMJD2A constitutive knockdown BCBL-1 cell line was generated in a previous study [24] . Cell line TREx-MH-K-Rta-shJMJD2A BCBL-1 was maintained as described for TREx-MH-K-Rta BCBL-1 cells and supplemented with 1 μg/ml puromycin ( Invitrogen ) . JMJD2A knockdown and K-Rta expression was confirmed by immunoblotting analysis . The JMJD2A and K471R overexpression cell lines were generated by transfection of plasmids expressing Flag-JMJD2A-WT shRNA resistant or Flag-JMJD2A-K471R shRNA resistant into TREx-MH-K-Rta-shJMJD2A BCBL-1 . Cells were selected for 30 days with 200 μg/ml G418 ( AMRESCO ) and purified by Ficoll . Expression of Flag-JMJD2A-WT and K471R was tested by Western blot using anti-JMJD2A antibody . 293T cells and SLK cells were maintained in DMEM containing 10% FBS . JMJD2A tagged with an N-terminal Flag was expressed from pcDNA3 vector . Mutation of specific lysine residues to arginine was introduced into JMJD2A by site-directed mutagenesis using specific primers listed in S1 Table . T7-SUMO-1 , T7-SUMO-2/3 , HA-K-bZIP-WT , HA-K-bZIP-L75A and HA-PIAS3 were also expressed from plasmid pcDNA3 . Flag-JMJD2A-WT shRNA resistant and Flag-JMJD2A-K471R shRNA resistant were cloned into pLenti4-CMV/TO vector . ChIP was performed according to the protocol from Dr . Farnham’s laboratory ( http://genomics . ucdavis . edu/farnham ) . Briefly , chromatin DNA from BCBL-1 cells was harvested after fixation with 1% formaldehyde . Chromatin DNA from 1 x 107 cells was used for each ChIP assay . Anti-JMJD2A rabbit polyclonal antibody , ChIP grade anti-SUMO-2/3 ( Abcam , ab81371 ) mouse monoclonal antibody , ChIP grade anti-H3K9me3 ( Abcam , ab8898 ) rabbit polyclonal antibody and rabbit non-immune serum IgG ( Alpha Diagnostic International ) were used for the ChIP assays . 50 ng of ChIP’d DNA eluted in 50 μl of ddH2O was used for ChIP-seq library preparation , according to the protocol from Illumina . DNA fragment libraries ( ~400 bp ) were analyzed for paired-end sequencing on Illumina HiSeq 2000 . The ChIP-Seq data was aligned with the KSHV genome and hg19 build by Partek Genomics Suite ( Partek Inc . USA ) . ChIP DNA was confirmed for successful IP using SYBR Green-Based real-time qPCR analysis by CFX connect real-time PCR detection system ( Bio-Rad , Richmond , CA ) . Specific primer sets were designed to amplify potential binding sites . Primer sequences are listed in S2 Table . Total RNA was prepared from TREx-MH-K-Rta-shJMJD2A , -shJMJD2A-Flag-JMJD2A-WT and -K471R BCBL-1 cell lines with 0 hour and 24 hours Dox treatment using TRIzol ( Invitrogen , Carlsbad , CA ) according to the manufacturer’s instructions . RNA-seq was performed at the Sequencing Core of National Research Program for Genomic Medicine at the National Yang-Ming University using Illumina HiSeq 2000 . Sequencing reads were processed as described previously [21] . In this study , the sequence reads that did not map to hg19 were aligned to the KSHV genome . The transcript frequency was determined in reads per kilobase of transcript per million mapped reads ( RPKM ) with transcriptome information obtained from Ensembl Release 75 by Partek Genomics Suite . Genes with RPKM > 0 . 05 were considered as expressed in cells and analyzed for further study . Differential expression of genes in KSHV reactivation at 24 hours verses latency was analyzed by comparing RPKM and calculated as fold change . To compare the expression profile similarity between TREx-MH-K-Rta-shJMJD2A , shJMJD2A-Flag-JMJD2A-WT and -K471R BCBL-1 cell lines , the RPKM in 0 hour and in 24 hours Dox treatment were analyzed for hierarchical clustering by dChip software . The genes used for dChip analysis were listed in S3 and S4 Tables . The biological functions of genes regulated by JMJD2A during KSHV reactivation were analyzed by Ingenuity Pathway Analysis ( IPA ) software ( http://www . ingenuity . com ) using IPA spring release 2016 . The genes used for IPA analysis were listed in S5 Table . Differential gene expression was analyzed by comparing RPKMs from each sample and verified using real-time RT-qPCR . 0 . 5 μg of total RNA was reverse-transcribed into cDNA using Oligo-d ( T ) 18 and SuperScript III first-strand synthesis system ( Invitrogen ) . qPCR was performed according to the manufacturer's protocol ( iQ SYBR Green Supermix , Bio-Rad ) . Primer sequences are listed in S6 Table . 293T cells were seeded and transfected using TransFectin ( Bio-Rad , 170–3351 ) . After 24 hours transfection , cells were reseeded on glass coverslips placed in 6-well plates , and fixed by 4% paraformaldehyde for 20 min . Cells were then washed with PBS in 3 times and permeabilized for 15 min using 0 . 5% Triton X-100 in PBS . Coverslips were washed 3 times with PBS , incubated in blocking solution ( 1% BSA in PBS ) , and further incubated with primary antibodies , anti-JMJD2A antibody and anti-H3K9me3 antibody ( Abcam , ab8898 ) diluted in blocking solution for 16 hours . Coverslips were then washed with PBS 3 times and incubated with secondary antibodies ( Abcam , ab181448 ) diluted in blocking solution for 1 hour . Nuclei were stained with Hoechst 33258 ( Invitrogen , H3569 ) in PBS and washed 3 times with PBS . The cells were mounted on glass slides with mounting solution ( 20 mM n-propylgallate , 80% Glycerol , 20% 1XPBS ) . Images were visualized by a Lecia DMI4000B fluorescence microscope and analyzed by MetaMorph ( Molecular Devices , Transflour ) . Reactions with purified WT or mutant JMJD2A were incubated with recombinant K-bZIP WT , KbZIP L75A using the SUMOlink SUMO-1 and SUMO-2/3 kit ( Active Motif , 40120 , 40220 ) for in vitro SUMOylation assays . Reaction products were analyzed by immunoblotting . For demethylase activity assays , purified wild type or mutant JMJD2A were incubated with histone proteins ( Sigma-Aldrich , St . Louis , MO , Sigma H9250 ) for 1 hour at 37°C and reactions were analyzed by immunoblotting . 293T cells were transfected using transfection reagent , TransFectin for specific protein overexpression . After 48 hours transfection , cells were subjected to immunoprecipitation by anti-Flag M2 beads ( Sigma-Aldrich , M8823-1ML ) and immunoblotting by specific antibodies . For demethylase activity assay , overexpressed JMJD2A was immunoprecipitated by anti-Flag M2 beads and incubated with histone proteins for 1 hour at 37°C . Reactions were analyzed by immunoblotting using anti-H3K9me3 antibody . Transfected 293T cells were collected in lysis buffer containing 0 . 5% NP-40 , 1X protease inhibitor cocktail ( Roche , 04 693 132 001 ) , and 40 mM N-Ethylmaleimide ( NEM , Sigma-Aldrich , E3876-5G ) . TCLs were incubated with anti-Flag M2 beads for 1 hour at 4°C . Flag-JMJD2A-WT and -K471R complexes were captured by anti-Flag M2 beads . Beads were washed with 0 . 5% NP-40 for 3 times and the bound proteins were analyzed by immunoblotting . Antibodies used for immunoblotting were anti-α-Tubulin ( Sigma-Aldrich , T6074-200UL ) , anti-PIAS3 ( Cell Signaling , D5F9 ) , anti-H3K9me3 ( Abcam , ab8898 ) , anti-H3 ( GeneTex , GTX122148 ) , anti-SUMO-1 ( Abcam , ab32058 ) , anti-SUMO-2/3 ( Abcam , ab3742 ) To evaluate viral production , supernatants from control and 48 hours Dox-induced TREx-MH-K-Rta-shJMJD2A , -shJMJD2A-Flag-JMJD2A-WT and -K471R BCBL-1 cells were collected . KSHV virion DNA purified from collected supernatants was purified by QIAamp MinElute Virus Spin kits as described previously [54] . Virion productivity was determined by real-time qPCR using a TaqMan probe targeting orf73 ( LANA ) [55] . TREx-MH-K-Rta-shJMJD2A , -shJMJD2A-Flag-JMJD2A-WT and -K471R SLK ( 2 x 103 ) and BCBL-1 ( 5 x 103 ) cells were seeded into 96-well plates . After 24 hours , the cell viability was examined by MTT assay ( Sigma-Aldrich , M5655 ) for continuous 4 days . A final concentration of 0 . 5 mg/ml MTT was added and the formazan crystals were solubilized by 10% SDS . The optical density ( OD ) was determined by a microplate spectrophotometer at a wavelength of 570 and 660 nm . | Epigenetic dysregulation connects genotype to diseases . An understanding of epigenetic regulation holds promise for clinical use . The profound epigenetic changes that occur during the latent-to-lytic switch of the Kaposi’s sarcoma associated herpesvirus ( KSHV ) life cycle make it an attractive model system for studies of epigenetic regulation . Using this model , our recent work showed that the demethylase JMJD2A and SUMO-2/3 specific modifications of viral and host chromatin are associated with epigenetic regulation of transcription during reactivation . However , how SUMO modification and histone modifying enzymes interface to orchestrate epigenetic regulation remains largely unknown . Here , we demonstrate JMJD2A as an example of a histone demethylase that is SUMO-2/3 modified by the KSHV encoded SUMO E3 ligase , K-bZIP . SUMO modification of JMJD2A is essential for stabilizing its chromatin binding and exerting its transcriptional derepression activity . Emerging evidence has implicated JMJD2A as an oncogene involved in the progression of various human tumors . The essential role of SUMO in regulating the biological function of JMJD2A suggests that SUMOylation of JMJD2A may be one of the potential underlying mechanisms responsible for JMJD2A-mediated oncogenesis . In this regard , inhibition of JMJD2A SUMOylation could be a new strategy for anti-cancer therapy . | [
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] | 2017 | SUMO modification of a heterochromatin histone demethylase JMJD2A enables viral gene transactivation and viral replication |
Regulatory networks allow organisms to match adaptive behavior to the complex and dynamic contingencies of their native habitats . Upon a sudden transition to a novel environment , the mismatch between the native behavior and the new niche provides selective pressure for adaptive evolution through mutations in elements that control gene expression . In the case of core components of cellular regulation and metabolism , with broad control over diverse biological processes , such mutations may have substantial pleiotropic consequences . Through extensive phenotypic analyses , we have characterized the systems-level consequences of one such mutation ( rho* ) in the global transcriptional terminator Rho of Escherichia coli . We find that a single amino acid change in Rho results in a massive change in the fitness landscape of the cell , with widely discrepant fitness consequences of identical single locus perturbations in rho* versus rhoWT backgrounds . Our observations reveal the extent to which a single regulatory mutation can transform the entire fitness landscape of the cell , causing a massive change in the interpretation of individual mutations and altering the evolutionary trajectories which may be accessible to a bacterial population .
Rho-dependent termination is a crucial component of transcriptional regulation in bacteria , and is estimated to terminate approximately half of the transcripts present in E . coli [1] , [2] . Recent studies have shown that this type of transcription termination is particularly prevalent in prophage and other horizontally acquired DNA , thus insulating the cell from the deleterious expression of such elements [3] , [4] . Rho has also been shown to safeguard genomic integrity by reducing co-directional collisions between transcriptional and replication machinery [5] , [6] . The rho* allele was initially identified in a set of short-term laboratory evolution experiments as a major modifier of ethanol tolerance in E . coli MG1655 [7] . This allele contains a missense mutation ( F62L ) in the RNA binding domain of Rho , which has been previously shown to cause a 20% higher read-through of the termination site tR1 [8] , and raise the dissociation constant for ( rC ) 10 by a factor of four [8] . The ethanol tolerance caused by rho* can be traced to overexpression of a few loci ( namely the prpBCDE and cadBA operons [9] ) , which are also among the transcriptional units strongly affected by chemical inhibition of Rho-dependent termination [3] . Mutations to rho have also been observed in several other laboratory evolution experiments [10]–[13] , although the nature of their contribution to fitness in those cases is unclear . Given the pervasive effects on transcription throughout the genome caused by short term inhibition of Rho-dependent termination [3] , [4] , we sought to determine the full breadth of effects of rho* , both on cellular phenotype and on secondary mutations at other loci . We found widespread effects from both classes; rho* significantly alters cellular fitness in the presence of a variety of nutrient sources and antibiotics , and shows epistatic interactions with mutations at ∼5% of other loci in the genome . Our results illustrate that mutations to rho* , and presumably other central components of the transcriptional apparatus , facilitate the rapid generation of broad phenotypic diversity in bacteria , with significant consequences for the evolution of populations under stress .
Based on the biological function of Rho , one naturally expects that rho* cells will show increased transcription immediately downstream of Rho-dependent termination sites . Indeed , measurements of transcript abundances [3] and RNA polymerase occupancy [4] have recently shown that after short-term inhibition of Rho-dependent termination using the compound bicyclomycin ( BCM ) , hundreds of transcriptional readthrough events are apparent throughout the E . coli genome , with significant over-representation of recently and horizontally acquired genomic regions . In order to assess the effects of rho* on transcriptional output during balanced growth , we performed transcriptional profiling comparing WT and rho* cells using tiling microarrays ( raw data available at the Gene Expression Omnibus; Accession GSE32022 ) . We then identified genomic regions showing significant differences in transcript levels between the two genetic backgrounds ( Bonferroni-corrected p<0 . 01 and greater than twofold change in representation; see Text S1 , Section 1 . 6 ) . We found a total of 2535 probes ( out of 92794 positions ) showing significant differences , located in 1281 genes and 433 intergenic regions; a few example loci are shown in Figure S1 . We identified the most significantly perturbed genes in rho* by flagging all cases for which the median WT:rho* expression ratio for all sense-stranded probes in a given gene indicated a greater than 1 . 5-fold change in expression level; using this threshold , 155 genes were overexpressed and 44 underexpressed in rho* . The presence of such a substantial underexpressed fraction again illustrates the presence of indirect effects of rho* on the genetic regulatory network , whereas the overexpressed fraction likely represents a combination of genes overexpressed directly due to transcriptional readthrough and those altered due to regulatory perturbations . Consistent with this interpretation , probes which are significantly overexpressed in rho* cells relative to WT show 1 . 3-fold enriched overlap with a set of prophages , insertion sequences , and K-12 specific elements ( the MDS42 deletion sites [14]; p = 0 . 011 by random permutation of site locations ) . Probes overexpressed in WT cells , in contrast , show no significant correlation with MDS42 deletion sites ( 1 . 2-fold depletion; p = 0 . 198 ) . It is also notable that of the probes identified as significantly overexpressed in rho* cells relative to rhoWT , 82% of those overlapping genes were on the antisense strand ( cf . 55% for those underexpressed in rho*; see Figure S2A ) . A recent RNA-seq study showed the presence of pervasive antisense transcription throughout the E . coli genome [15] , which the authors presume to be limited in extent primarily by Rho-dependent termination [15] . In addition , Peters and coworkers identified 24 novel antisense transcripts appearing in BCM-treated cells [4] , more directly illustrating a role for Rho-dependent termination in at least some cases . In order to assess the effects of rho* on previously identified antisense transcripts , we compared the log-ratios of transcript levels in rho* vs rhoWT cells along a series of windows centered at 50 bp increments downstream of the 1 , 005 antisense transcription start sites identified by Dornenburg et al . [15] . As seen in Figure S2B , a significant increase in transcription is apparent in rho* cells along the first several hundred bp of these antisense transcripts , illustrating a major mechanism through which rho* likely alters cellular physiology . Furthermore , this analysis does not capture the effects on antisense transcripts which are at undetectable levels in rhoWT cells ( and thus would have been missed from the Dornenburg study ) . In order to obtain a pathway-level view of the changes in gene expression caused by rho* , we applied iPAGE [16] to identify gene ontology ( GO ) pathways which share significant mutual information with the log-ratio of rho* vs . WT RNA from microarray experiments ( see Text S1 , Section 1 . 7 for details ) . In all , 19 non-redundant GO terms show significant mutual information with the expression profile for sense-strand RNA and 10 non-redundant GO terms for the antisense profile ( out of 1340 present in the annotation set [17] , using a threshold of p<0 . 0001 ) . The changes in expression patterns for a few example pathways of particular interest are shown in Figure 1A , and the full set of significantly perturbed GO terms is shown in Figure S3 . These changes in expression affect a variety of cellular pathways including diverse aspects of metabolism and regulation; for example , genes involved in transcriptional attenuation and post-transcriptional regulation were over-expressed in the rho* background , which may represent a regulatory coping strategy for minimizing the deleterious effects of transcriptional read-through . For the most part , however , the fitness consequences ( if any ) of these broad-reaching expression modifications were not readily identifiable . In order to measure the extent to which the altered gene expression state of rho* MG1655 cells affects their fitness in different environments , we compared the growth of these cells to that of wild type cells in the presence of a variety of nutrient conditions and antibiotics ( see Text S1 , Section 1 . 2 for details ) . We identified 22 conditions ( shown in Figure 1B and Table 1 ) in which the relative fitness of WT and rho* differed significantly from that in our reference condition ( glucose minimal media ) , with 8 conditions favoring WT and 14 favoring rho* cells ( we use steady-state growth rate as a proxy for fitness unless otherwise noted; see Section 2 of Text S1 , Table S1 , and Figure S4 for a discussion of other relevant quantities ) . The number and nature of these discovered environments show that the regulatory perturbations caused by the rho* mutation functionally modify a variety of pathways in the cell . In some cases the fitness differences between WT and rho* cells can be directly explained by modified gene expression . For example , the pathway-level analysis in Figure 1A shows that pathways involved in oxidative metabolism are under-expressed in the rho* background , which may explain their increased aminoglycoside resistance [18] . Most conditions showing fitness differences , however , defy such simple explanations . The varied , pleiotropic effects of rho* on fitness under different growth conditions suggested that the rho* mutation may also result in global changes in the fitness landscape , altering the effects of any additional mutations . To test for such changes , we used fitness profiling of transposon-mutagenized libraries [19] to create coarse-grained representations of the fitness landscape under four conditions ( a schematic of the procedure is shown in Figure 2A–2B and detailed methods are provided in Text S1 , Section 1 . 5; raw data are available from the Gene Expression Omnibus , Accession GSE32022 ) . For a given condition , a modified fitness landscape implies that there are loci whose fitness consequences are different in rhoWT and rho* backgrounds . These loci , in turn , provide insight into the specific mechanisms through which rho* alters the cell's regulatory and physiological state ( we provide more detailed analysis of several such cases , including follow-up experiments on knockout strains , in Text S1 , Section 3; see also Figures S5 and S6 and Table S2 ) . Similar patterns emerged in all four conditions tested: both the WT and rho* fitness profiles show hundreds of sites at which transposon insertions lead to significant changes in fitness , with the majority unique to one genetic background or the other ( see Table S3 ) . Comparisons of the distributions of selection scores between WT and rho* cells in each condition are shown in Figure 2C; the low correlations between scores of genes in the two genetic backgrounds under all four conditions indicate that the fitness consequences of secondary mutations are heavily dependent on the genotype at the rho locus , whereas correlations between replicates from the same genetic background under each condition are quite high . The overlaps of loci and pathways with significant fitness effects in the two backgrounds are shown in Figure 3; in all four cases , a common core of loci exists which strongly contributes to fitness in both the rhoWT and rho* backgrounds , but the majority ( >70% ) are unique to one background or the other . This indicates that the effects of these mutations are in fact strongly altered by the rho* allele . Consistently , in Figure 4 we show examples of several loci where significant epistasis between rho* and a secondary mutation was observed in defined strains during follow-up experiments ( details of the epistasis experiments and calculations are given in Text S1 , Section 4; see also Tables S4 and S5 ) . The genetic basis of laboratory-evolved ethanol tolerance provides an example of the reshaping of the fitness landscape by rho* . In the course of the experiments reported here , we found that rho* alone is insufficient to confer the levels of ethanol tolerance observed in the evolved strain from [7] . Instead , using global linkage analysis , we found that an epistatic interaction between rho* and rpsL* ( a nonsense mutation in the S12 ribosomal protein RpsL ) provides a substantial portion of the increase in ethanol tolerance ( see Section 5 of Text S1 , Figures S7 and S8 , and Table S6 for further details ) . Relative growth rates for all combinations of wild type and identified mutant alleles of rho* and rpsL* are shown in Figure 5 . Whereas the rpsL*/rho* double mutant showed a maximum growth rate of 1 . 01 doublings/hour in the presence of 5 . 5% ethanol , rho*/rpsLWT cells grew at 0 . 85 doublings/hour , and both rhoWT/rpsLWT and rhoWT/rpsL* cells showed no or negligible growth . Conversely , in LB alone the double mutant was less fit than all other allelic combinations ( despite the beneficial effects of rho* in isolation . ) . Thus , rho* shows a positive epistatic interactions with rpsL* in ethanol-containing media and a negative epistatic interaction in the absence of ethanol .
The wholesale reworking of the cell's fitness landscape due to rho* illustrates its potential to open evolutionary paths that would not otherwise be accessible . rho* provides both direct fitness effects and broadly varying ( and often positive ) epistatic relationships with perturbations at other loci , allowing it to provide benefits early in an evolutionary trajectory while at the same time providing a different , and frequently larger , profile of possible adaptive secondary mutations ( see Tables S3 and S7 ) . The interaction between rho* and rpsL* described above represents one such case: rho* itself provides a beneficial fitness effect in the presence of ethanol , and also exhibits positive epistasis with a mutation at the rpsL locus . A more general schematic is shown in Figure 6: the fitness effects of mutations throughout the genome are strongly influenced by the genotype at rho ( and presumably other core transcriptional proteins as well ) , making some secondary mutations more or less beneficial than they would be otherwise ( Figure 6 , genotype B ) . Mutations such as rho* can also both provide a fitness benefit relative to the wild type under common growth conditions , and reveal higher fitness genotypes upon exposure to stress conditions ( Figure 6 , genotype C ) . rho* is expected to exert its effects simply by altering transcription ( in this case primarily by allowing expression of regions which would not otherwise be transcribed ) ; we thus expect that mutations to other core components of the cell's transcriptional machinery , or to other broadly influential regulators , would show similar levels of evolutionary and phenotypic leverage . In support of this view , mutations to rho [10]–[13] , RNA polymerase [11] , [13] , [20]–[23] and DNA supercoiling proteins [24]–[26] have frequently been observed in a variety of other recent directed evolution experiments . In a few cases , specific epistatic interactions involving these core transcriptional components were found to shape the future adaptive trajectory of populations . For example , Applebee and coworkers [23] found that in a set of E . coli populations evolved to grow efficiently in glycerol minimal media , RNA polymerase mutations arising earlier in the evolutionary trajectories showed positive epistasis with subsequent glpK mutations ( and possibly mutations to dapF and murE as well ) . Similarly , in analyzing populations from an extremely long-term evolution experiment , Woods et al . [26] found the presence of two variant topA alleles in competition; of these , the allele present in the subsequently evolved strain had a less positive direct effect on fitness , but also showed positive epistasis with a secondary mutation at spoT that yielded an overall higher fitness phenotype . In general , these previous studies have not , however , fully explored the full breadth of both direct phenotypic and epistatic effects of the housekeeping mutations that they identified . Because the primary effect of a hypomorphic rho allele such as rho* is to allow expression of regions of the genome that would not typically be expressed ( see above; also [3] , [4] ) , we thus see that the impairment of a system setting baseline boundaries for gene expression can in fact bring forth beneficial , but normally hidden , phenotypes . The concept that robustness to the effects of mutations may facilitate adaptive evolution by allowing the accumulation of genetic diversity that can be subsequently released by a single perturbation , has been proposed repeatedly in the theoretical literature . Wagner [27] discussed the “neutral space" of a biological system – a range of equivalent solutions to a given condition – and notes that the presence of diversity within the neutral space allows variation that may be useful under subsequently encountered conditions . Draghi et al . [28] illustrated precisely this phenomenon more quantitatively using a computational model , showing that intermediate levels of robustness ( modeled as the probability of a given mutation being neutral ) accelerated the adaptation of populations by providing a reservoir of phenotypically neutral genetic diversity , including variants that could be adaptive under changing conditions . More recently , in modeling tradeoffs involved in the regulation of translational readthrough , Rajon and Masel [29] found bistable solutions which required either global regulation to reduce readthrough rates , or a combination of higher readthrough rates but reduced incidence of deleterious products upon readthrough; the high readthrough rate solution was found to be more evolvable by allowing the accumulation of non-deleterious genetic diversity downstream of translational stop sites , which can subsequently be incorporated through a single mutation to the stop codon . The behavior of rho* is also reminiscent of two phenomena related to the core translational machinery of yeast . Jarosz et al . [30] recently showed that the chaperone Hsp90 acts to suppress the effects of genetic variation occurring naturally between yeast strains; temperature stress or chemical inhibition of Hsp90 yielded a wide variety of phenotypic changes among ∼100 different yeast strains under 100 low-level stress conditions , frequently with differing signs of effect on fitness for different strains under the same condition . Furthermore , the authors found that Hsp90 in fact shows epistatic interactions with 20% of naturally occurring genetic variations between the strains under consideration . Similar phenomena have been observed for the yeast prion state [PSI+] [31]–[33] , where ( as with rho* ) an alteration in the behavior of a regulatory protein gives rise to a highly pleiotropic phenotype which may be harmful or beneficial under a variety of conditions , interacts strongly with the precise genetic background of the cell in question , and appears to exert its effects by causing ectopic expression of sequences which are generally silent . The comparison between both mechanisms in yeast and rho* must not be taken too far , as there are also substantial differences , most notably in that Hsp90 and [PSI+] act post-transcriptionally , [PSI+] in particular represents an epigenetic rather than genetic mechanism , and both the prion states and Hsp90 relaxation have been shown to be encouraged by environmental stress [30] , [34] , whereas no similar mechanism would be expected to mutate core housekeeping genes in stressed E . coli cells preferentially . Nevertheless , the effects of both yeast mechanisms , and bacterial rho mutations , illustrate that microorganisms possess the genetic potential to grow under a broader array of conditions than their regulatory logic allows , that some of the hidden potential may be unlocked through perturbations of core regulatory proteins , and that even a single such perturbation may unleash a wide variety of positive or negative effects and interactions with other loci throughout the genome . Taken together , our findings illustrate that a single amino acid substitution in the global transcriptional terminator Rho leads to a wholly different regulatory and phenotypic state , in which gene expression is globally altered and cellular fitness in a broad variety of environments has changed . The same mutation also dramatically alters the fitness landscape with regard to other genetic variations , making accessible a number of beneficial secondary mutations that are otherwise neutral or deleterious . The set of states reachable through rho* or other point mutations of core regulatory proteins comprise a previously underappreciated reservoir of additional phenotypes accessible to bacterial populations under selective conditions . These findings imply a role for mutations to regulators such as rho both as evolutionary catalysts , by making a variety of secondary mutations more favorable than they would be in the parental strain , and as evolutionary capacitors [35] , by allowing silently accumulating genetic diversity to take effect rapidly upon changes in gene regulation . The full extent to which this capacity of core housekeeping and regulatory proteins is used during evolutionary trajectories , and the identity of the complete set of genes showing such broadly influential behavior , are not yet clear . It is also intriguing to speculate that classical global regulators may also show similarly diverse effects , either upon genetic perturbation or as a response to environmental signals , given that the number of genes substantially perturbed by rho* ( ∼200 ) is comparable to the number directly or indirectly affected by each global regulator ( e . g . , CRP , IHF , or FNR ) [36] .
A complete listing of strains in the present study , including abbreviations used throughout the text , is given in Table S8 , and PCR primers are shown in Table S9 . The E . coli K12 strain MG1655 [37] ( ATCC strain 700926 ) provides the genetic background for all experiments reported here . For measurement of transcript abundances , cells were grown to mid-log phase in M9t/glucose , and RNA extracted using total RNA purification kit ( Norgen Biotek , Cat 17200 ) . After poly-A tailing , the extracted WT and rho* RNA were separately labeled , pooled , and then hybridized to Agilent custom arrays tiling the whole genome at 50 bp intervals , alternating between strands . Transposon mutagenized libraries were prepared as described by Girgis et al . [19] . Selections were carried out for 16 hours in 25 mL of either selective or reference media , and genomic DNA isolated using a DNeasy Blood and Tissue Kit ( Qiagen ) . The transposon footprinting and labeling protocol for quantifying relative fitness under different conditions is described by Girgis et al . [18] . Bacterial growth curves were measured in Costar 96-well clear polystyrene plates , using either a Biotek Synergy MX or Powerwave XS2 plate reader ( Biotek; Winooski , VT ) . Plates were incubated at 37°C with continuous shaking , and optical density ( OD ) reads at 600 nm taken every 10 minutes . Abbreviations for nutrient sources and antibiotics are given in Table S10 . Complete methodological details are provided in Section 1 of Text S1 , as well as Figure S9 . | Bacteria rely on complex genetic regulatory networks to respond to hazards or opportunities that they encounter . These networks consist of a series of sensory modules , coupled with various response elements that must be appropriately activated to deal with a given set of environmental conditions; all of these condition-specific elements interact with the cell's core machinery for gene expression . When they encounter a novel environment , populations of bacteria rapidly evolve to adapt to that environment; alterations in gene expression play a major role in this process and , in particular , mutations to the cell's central gene expression machinery are surprisingly common in laboratory evolution experiments . Focusing on one such mutation that had previously been shown to enhance the host cell's ethanol tolerance , we show that the same alteration can in fact aid cellular survival under a wide variety of conditions . In addition , the interactions of this regulatory mutation with other genes throughout the genome cause these mutations to fundamentally reshape the effects of any other genomic changes that occur , and thus alter the overall evolutionary course taken by a population . | [
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] | 2012 | Fitness Landscape Transformation through a Single Amino Acid Change in the Rho Terminator |
Natural killer ( NK ) cells are innate immune cells able to rapidly kill virus-infected and tumor cells . Two NK cell populations are found in the blood; the majority ( 90% ) expresses the CD16 receptor and also express the CD56 protein in intermediate levels ( CD56Dim CD16Pos ) while the remaining 10% are CD16 negative and express CD56 in high levels ( CD56Bright CD16Neg ) . NK cells also reside in some tissues and traffic to various infected organs through the usage of different chemokines and chemokine receptors . Kaposi's sarcoma-associated herpesvirus ( KSHV ) is a human virus that has developed numerous sophisticated and versatile strategies to escape the attack of immune cells such as NK cells . Here , we investigate whether the KSHV derived cytokine ( vIL-6 ) and chemokines ( vMIP-I , vMIP-II , vMIP-III ) affect NK cell activity . Using transwell migration assays , KSHV infected cells , as well as fusion and recombinant proteins , we show that out of the four cytokine/chemokines encoded by KSHV , vMIP-II is the only one that binds to the majority of NK cells , affecting their migration . We demonstrate that vMIP-II binds to two different receptors , CX3CR1 and CCR5 , expressed by naïve CD56Dim CD16Pos NK cells and activated NK cells , respectively . Furthermore , we show that the binding of vMIP-II to CX3CR1 and CCR5 blocks the binding of the natural ligands of these receptors , Fractalkine ( Fck ) and RANTES , respectively . Finally , we show that vMIP-II inhibits the migration of naïve and activated NK cells towards Fck and RANTES . Thus , we present here a novel mechanism in which KSHV uses a unique protein that antagonizes the activity of two distinct chemokine receptors to inhibit the migration of naïve and activated NK cells .
NK cells are innate immune lymphocytes that comprise approximately 10% of peripheral blood lymphocytes and are phenotypically characterized by the presence of CD56 , the expression of NKp46 , and the lack of CD3 expression [1] . The majority ( approximately 90% ) of naïve human NK cells in the peripheral blood express CD56 at intermediate levels ( CD56Dim ) and express high levels of FcγRIII ( CD16 ) , whereas a minor population of naive NK cells ( approximately 10% ) expresses CD56 at high levels and do not express CD16 ( CD56Bright CD16Neg ) [1] , [2] . Although mature NK cells predominantly circulate in the peripheral blood , they also reside in several lymphoid and non-lymphoid organs , such as the spleen , tonsils , lymph nodes , liver , lungs , intestine , and the uterus [3] . In most of these organs the predominant NK cell population is CD56Bright CD16Neg [2] , [4] . NK cells mediate two major functions: recognition and killing of tumor and virus-infected cells , performed primarily by the CD56Dim CD16Pos subset , and production of immuneregulatory cytokines mainly by the CD56Bright CD16Neg subset [5] . This is also reflected by the receptor repertoire expressed by the CD56Dim CD16Pos and CD56Bright CD16Neg NK cells , as the two subsets express a distinct set of inhibitory and activating receptors and display diversity in their adhesion molecules and chemokine receptors profile [1]–[6] . NK cells express several receptors for CC , CXC , C , and CX3C chemokines , with great heterogeneity in the chemokine receptor repertoire among different NK cell populations , among different individuals and between resting versus activated NK cells . Naïve CD56Dim CD16Pos NK cells express high levels of CXCR1 ( IL-8 receptor ) and CX3CR1 ( Fractalkine receptor ) and low levels of CXCR2 and CXCR3 [7] , [8] . This NK subset expresses no detectable levels of CC chemokine receptors on their cell surface [9]–[11] . In contrast , CD56Bright CD16Neg NK cells express high levels of CXCR3 , CCR5 and CCR7 , low levels of CX3CR1 , and are negative for CXCR1 , CXCR2 and CXCR5 [12] . The differences in chemokine receptor expression correlate with differences in the migratory behavior . The CD56Dim CD16Pos NK cells migrate vigorously in response to Fractalkine ( CXC3L1 ) , SDF-1α ( CXCL12 ) and IL-8 ( CXCL8 ) , while the chemotaxis of CD56Bright CD16Neg NK cells is stimulated most potently by CXCL10 and CXCL11 ( CXCR3 ligands ) , CXCL12 ( CXCR4 ligand ) , CCL19 and CCL21 ( CCR7 ligands ) [7] , [8] , [12] . The expression of chemokine receptors and the corresponding NK cell chemotactic response is also modulated upon cytokine mediated activation . IL-2 and IL-15 induce a strong downregulation of CX3CR1 [13] , [14] . Additionally , a significant decrease of CXCR3 expression and chemotaxis to CXCL10 was reported in human NK cells treated for 6 or 24 hours with IL-2 , while longer periods of stimulation has been reported to increase the expression of CCR4 , CCR5 and CCR8 [10] . KSHV , also named human herpesvirus-8 ( HHV-8 ) , belongs to the gamma herpesvirus subfamily . KSHV is the causative agent of Kaposi's sarcoma ( KS ) and two other lymphoproliferations diseases: multicentric Castleman's disease and primary effusion lymphoma [15]–[18] . There are several forms of KS , but the most severe form occurs in HIV patients and immune suppressed transplant recipients , highlighting the importance of a functional immune system in the control of KSHV infection . Like other herpesviruses , infection of KSHV has two distinct phases known as lytic replication and latency [19] . During the latent state only a small number of viral proteins are expressed and the viral genome exists as an episome within the host nucleus , while in the lytic state most of the viral genes are expressed , the genome is replicated and packaged , and viruses can egress from the infected cells [20] , [21] . Herpesviruses , including KSHV , use several different mechanisms in order to evade recognition by the host immune system . In fact , around 25% of the KSHV-derived proteins have been shown to regulate different aspects of the immune system of the host [22] . Among these proteins are MIR1 ( K3 ) and MIR2 ( K5 ) , which downregulate the surface expression of the major histocompatibility complex class I molecules ( MHC-I ) to prevent cytotoxic T lymphocytes ( CTL ) attack [23] , [24] . However , this mechanism may result in increased susceptibility to NK cells recognition , since MHC-I serve as a ligand for many inhibitory receptors expressed by NK cells inhibiting their cytotoxicity [25] . Consequently , it is not surprising that KSHV evolved to deal with NK cell attack as well: it was shown that MIR2 inhibits the expression of the adhesion molecule ICAM1 , which is essential for NK and CTL killing [26] . Additionally , MIR2 downregulates the NK-activating ligands MICA , MICB , and AICL to avoid recognition of the infected cells by the killer receptors NKG2D and NKp80 , respectively [27] . Furthermore , previous work from our lab demonstrated that KSHV uses one of its miRNAs , miR-K12-7 , to downregulate MICB expression and thus avoid the NKG2D-mediated killing of target cells [28] . Collectively these examples emphasize the importance of NK cells in controlling KSHV infection . Viral infections stimulate the production of cytokines and chemokines that have a crucial role in inducing the migration of immune cells to areas of infection , in immune regulation , and in anti-viral defense [29] , [30] . In this regard , KSHV encodes three chemokine homologues: viral macrophage inflammatory protein ( vMIP ) -I ( ORF K6 ) , vMIP-II ( ORF K4 ) and vMIP-III ( ORF K4 . 1 ) . In addition , KSHV encodes one homologues cytokine called vIL-6 ( ORF K2 ) . While it has been suggested that the viral KSHV-encoded chemokines are related to cellular CC-chemokines , they show limited sequence similarity to their cellular counterparts ( ∼40% ) , and direct orthologs are difficult to discern with confidence [31] . Previous papers published since the discovery of the viral chemokines , primarily in the late 90's , focused mainly on identifying the targeted receptors . vMIP-I and vMIP-III were shown to be specific agonists of host CCR8 and CCR4 , respectively , and it was demonstrated that Th2 T cells expressing CCR4 manifested increased migration towards vMIP-III in chemotaxis assays [32] . vMIP-II has been shown to bind the chemokine receptors CCR1 , CCR2 , CCR3 , CCR5 , and CXCR4 . This binding blocked the calcium mobilization elicited by the relevant human chemokines which bind those receptors [31] . In addition , vMIP-II has been reported to bind to a variety of other receptors as a neutral ligand , therefore having the ability to competitively block the actions of cellular chemokines recognizing these same receptors . These receptors include CCR1 , CCR2 , CCR10 , CXCR4 and CX3CR1 [31] , [33] , [34] . Contradicting results were obtained regarding whether vMIP-II functions as an agonist or as an antagonist . Using a rat model , it was shown that vMIP-II efficiently inhibits the chemotactic activity of activated leukocytes towards MCP-1 , MIP-1β , and RANTES [33] . In contrast , vMIP-II was shown to be a selective attractant for Th2 T cells and acts as an agonist for the chemokine receptor CCR8 selectively expressed by this subset [35] . Finally , it has been reported that vMIP-I and vMIP-II can enhance chemotaxis of a monocytic cell line in-vitro [36] . Surprisingly , despite the importance of NK cells in fighting KSHV infection , virtually nothing is known regarding whether the KSHV-derived cytokine and chemokines interact with NK cells , and whether and how they affect NK cell activity . Here we show that KSHV-derived vMIP-II directly binds to two distinct chemokine receptors expressed by naïve and activated NK cells , and that vMIP-II antagonizes the activity of these two receptors to inhibit the migration of naïve and activated NK cells .
It has been previously suggested that the viral chemokines encoded by KSHV bind a wide spectrum of CC and CXC chemokine receptors [31] . However , it is still practically unknown whether all four KSHV chemokines ( including the viral cytokine ) bind directly to all immune cell types and how this binding affects immune cell function . To study this issue , we cloned all four KSHV proteins in frame with the human IgG1 Fc domain ( which includes a mutation preventing its binding to Fc receptors ) and stably expressed them in 293T cells . The proteins were purified on a protein-G column ( purity of all fusion proteins used in this manuscript was around 100% , data not shown ) and then used to stain peripheral blood lymphocytes ( PBLs ) ( Figure 1A ) , purified naïve NK cells ( Figure 1B ) , monocytes ( Figure 1C ) and neutrophils ( Figure 1D ) . As can be seen in figure 1 , only a small subset of the PBLs was recognized by vIL-6-Ig , whereas NK cells , neutrophils and monocytes were essentially not recognized . Little binding of vMIP-I-Ig and vMIP-III-Ig was detected to subsets of all immune cells tested , mainly to the lymphocytes and isolated NK cells ( Figure 1 ) . Interestingly , most of the lymphocytes and the purified NK cells were recognized by vMIP-II-Ig ( Figure 1 ) , while only a subset of monocytes and neutrophils was recognized by this viral chemokine . Thus , of the four KSHV chemokines , vMIP-II was the only one which demonstrated strong binding to the majority of the CD3 positive cells as well as to purified naïve NK cells . The strongest binding of vMIP-II-Ig was observed to purified naïve NK cells ( MFI of around 89 , Figure 1B ) . Since it is still unknown whether and how the KSHV-derived chemokines affect NK cell activity , we continued our research concentrating on the vMIP-II interaction with NK cells . To test whether vMIP-II interacts with the two NK cell subsets found in the blood , we performed double staining of freshly isolated naïve NK cells with anti-CD56 and with vMIP-II-Ig . Interestingly , strong vMIP-II-Ig staining was detected mainly in the CD56Dim CD16Pos NK cells sub-population , whereas the CD56Bright CD16Neg NK cells were much less efficiently recognized ( Figure 2A , right panel ) . The binding was specific as no binding was observed when a control fusion protein ( Control-Ig , left panel ) was used . It was previously suggested that vMIP-II interacts with several different chemokine receptors including CCR1 , CCR2 , CCR3 , CCR5 , and CXCR4 [31] . To test whether the CD56Dim CD16Pos population preferentially expresses these chemokine receptors , we double stained the isolated naïve NK cells with anti CD56 and with anti-CCR1 , CCR2 , CCR3 , CCR5 , or CXCR4 mAbs . As can be seen , expression of CCR1 , CCR2 , CCR3 , CCR5 and CXCR4 was hardly detected on the freshly isolated naïve NK cells ( Figure 2B ) . In some donors we observed that a subset of their NK cells express CCR5 ( Figure 2C ) . However , the expression profile of this chemokine receptor was substantially different from the pattern of vMIP-II-Ig staining ( compare Figures 2C , donor #1 and Figure 2A , right panel ) ; suggesting that this receptor is not the main one recognized by vMIP-II on naïve NK cells . In contrast , CXCR1 and CX3CR1 demonstrated an expression pattern similar to the vMIP-II-Ig staining ( compare Figure 2A to Figures 2D and 2E ) and therefore we considered these as possible targets of vMIP-II on naïve NK cells . Different functions have been suggested for vMIP-II . In some systems it was shown to attract cells , while in other systems it was shown to function as an antagonist [31] , [33] , [35] , [37] . Therefore , we next tested whether naïve NK cells are attracted by the following recombinant chemokines: vMIP-II , IL-8 ( a CXCR1 ligand ) , or Fractalkine ( Fck , the CX3CR1 ligand ) . As can be seen in figure 3 , freshly isolated NK cells migrated towards recombinant human ( rh ) IL-8 and rhFck , while no migration was observed to recombinant viral MIP-II ( rvMIP-II ) . Similar results were obtained with higher concentrations of rvMIP-II or with vMIP-II-Ig ( data not shown ) . Thus , we concluded that vMIP-II might function as an antagonist of NK cells migration . We next investigated whether vMIP-II might interact with CXCR1 , as this receptor , together with CX3CR1 , was expressed predominantly on the entire CD56Dim CD16Pos population ( Figure 2D ) . For this purpose , we initially generated an IL-8-Ig fusion protein ( since IL-8 interacts with CXCR1 ) , used it to stain freshly isolated NK cells , and demonstrated that similar to the vMIP-II-Ig staining and in agreement with the expression pattern of CXCR1 , the IL-8-Ig fusion protein binds primarily to the naïve CD56Dim CD16Pos and much less to the CD56Bright CD16Neg NK cell population ( Figure 4A ) . Next , we generated transfectants of 293T cells that express the CXCR1 chemokine receptor ( Figure 4B ) to examine the potential binding of vMIP-II to this receptor . However , we observed that while the CXCR1 transfectants were recognized by IL-8-Ig , vMIP-II-Ig did not bind these cells ( Figure 4C , left and right histograms respectively ) , suggesting that vMIP-II might not interact with CXCR1 . Despite this , we cannot rule out that vMIP-II did not interact with CXCR1 as a result of the fusion to the Fc portion . Thus , to further investigate whether vMIP-II interacts with CXCR1 , we used the rvMIP-II protein in binding and functional assays . For the binding assay , we incubated the 293T-CXCR1 transfectant cells with rvMIP-II , and then stained the cells with IL-8-Ig . As can be seen in figure 4D , the rvMIP-II protein did not block the binding of the IL-8-Ig to its receptor . We also tested whether vMIP-II could block the NK cell migration towards IL-8 . Naïve NK cells were pre-incubated with and without rhIL-8 , rvMIP-II , or rhRANTES ( used as a negative control ) , and then used in migration assays towards IL-8 . As can be seen in figure 4E , rhRANTES , which is a ligand for the CCR5 , did not block the migration of naïve NK cells towards IL-8 , and similarly , rvMIP-II also did not block the NK cells migration . In contrast , the pre-incubation of the naïve NK cells with rhIL-8 abolished their IL-8-mediated migration . Taken together , we concluded that vMIP-II probably does not bind the chemokine receptor CXCR1 . We next tested whether vMIP-II binds the other chemokine receptor that is expressed on the naïve CD56Dim CD16Pos NK cells , CX3CR1 . For this we generated another fusion protein composed of the CX3CR1 ligand , Fractalkine , fused to human IgG ( Fck-Ig ) . The fusion protein was produced in 293T cells and purified . In agreement with the expression pattern of its receptor CX3CR1 ( Figure 2E ) , the Fck-Ig protein stained primarily the CD56Dim CD16Pos population ( Figure 5A ) . To test whether vMIP-II can directly interact with CX3CR1 , we expressed CX3CR1 in 293T cells ( Figure 5B ) and tested the binding of Fck-Ig and vMIP-II-Ig to the 293T-CX3CR1 transfected cells . As can be seen , we observed that both Fck-Ig and vMIP-II-Ig bind to the 293T-CX3CR1 transfectant cells ( Figure 5C ) . To test whether vMIP-II can compete with Fck for binding to its receptor , we pre-incubated the 293T-CX3CR1 cells with rvMIP-II and then stained the 293T-CX3CR1 transfectant cells with Fck-Ig . As can be seen in figure 5D , pre-incubation of the 293T-CX3CR1 cells with rvMIP-II resulted in reduced Fck-Ig staining indicating that both proteins interact with CX3CR1 at similar sites . Finally , we tested whether vMIP-II affects the Fck-mediated migration of freshly isolated NK cells . Importantly , we observed that pre-incubation of naïve NK cells with vMIP-II completely abolished the Fck-mediated migration of freshly isolated NK cells in a manner similar to that observed with recombinant Fck ( rhFck , Figure 5E ) . Blocking of NK cells migration with the control chemokine rhRANTES had little or no effect ( Figure 5E ) . Blocking of NK cell migration was complete , whereas blocking of Fck-Ig binding to the transfectant cells was partial ( Figure 5D ) , probably because the transfectant cells expresses CX3CR1 at high levels as compared to its expression on naïve NK cells . Thus , we conclude that vMIP-II binds the CX3CR1 chemokine receptor , blocks the binding of Fck to its receptor , and consequently inhibits the migration of naïve NK cells towards Fck . It is well known that the expression of killer receptors , chemokine receptors , and the corresponding chemotactic responses of NK cells are modulated upon cytokine stimulation [10] , [38] , [39] . Therefore , we next tested whether vMIP-II will bind to IL-2-activated NK cells and observed an efficient binding ( Figure 6A ) . Interestingly , binding of vMIP-II to activated NK cells was observed even though the expression of CX3CR1 and CXCR1 almost completely disappeared following activation ( Figure 6B ) . Thus , we concluded that on activated NK cells , vMIP-II interacts with chemokine receptor/s other than CX3CR1 . To identify this receptor/s , we performed double staining FACS assays in which we used an anti-CD56 mAb together with anti CCR1 , CCR2 , CCR3 , CCR5 , or CXCR4 mAbs ( as above , we investigated the expression of these chemokine receptors because they were shown to interact with vMIP-II [31] ) . As can be seen in figure 6C , of the five receptors tested , CCR5 was the only chemokine receptor that was expressed on the majority of the activated NK cells , demonstrating a staining pattern similar to that of vMIP-II ( compare Figures 6A and 6C ) . Furthermore , as can be seen in figure 6D , RANTES induced activated NK cells chemotaxis in a transwell migration assay , while vMIP-II did not produce any detected NK cells migration ( Figure 6E ) . We demonstrated above that the expression of CCR5 on freshly isolated naïve NK cells was donor specific and that it did not expressed on the entire NK cell population ( Figure 2C ) . In contrast , following activation , CCR5 expression was expressed on the entire NK cell population , irrespectively of whether the NK cells were obtained from donors whose naïve NK cells express CCR5 or not . Taken together , these results suggested that similar to freshly isolated naïve NK cells , in activated NK cells vMIP-II might also function as an antagonist . To examine whether vMIP-II can recognize CCR5 , and whether it can block the RANTES binding to CCR5 , we initially generated a RANTES-Ig fusion protein and used this protein to demonstrate that RANTES-Ig interacts with IL-2-activated NK cells ( Figure 7A ) . We then used the CCR5 transfected U87 cells ( Figure 7B , this cell line was available to us from the NIH AIDS reagent program ) to demonstrate that both RANTES-Ig and vMIP-II-Ig bind CCR5 ( Figure 7C ) . Next , we demonstrated that vMIP-II blocks the binding of RANTES to CCR5 as pre-incubation of the CCR5 transfected cells with rvMIP-II significantly reduced the RANTES-Ig binding to the transfectant cells ( Figure 7D ) . Finally , we demonstrated that vMIP-II not only interferes with the binding of RANTES to CCR5 , but also abolished the RANTES mediated migration of activated NK cells . Pre-incubation of the activated NK cells either with rhRANTES or with rvMIP-II completely abolished the migration of activated NK cells towards RANTES , while pre-incubation of the activated NK cells with the control rhIL-8 chemokine had no effect ( Figure 7E ) . The differences between the partial block of binding ( Figure 7D ) and the complete block of migration ( Figure 7E ) might be explained , as above , due to the high expression levels of CCR5 on the transfectants . Next we wanted to investigate whether vMIP-II which is secreted following KSHV infection can block NK cell migration . For that we used the iSLK-KSHV cell line that switches from latent state of infection to lytic replication following doxycycline treatment . Initially we determined the concentration of vMIP-II secreted from the KSHV infected cells by using ELISA assay in which the rvMIP-II protein was used as a standard protein and found it to be 16 ng/ml ( Figure 8A ) . Next we pre-incubated naïve or IL-2 activated NK cells together with either rvMIP-II or with the vMIP-II containing supernatants derived from KSHV infected cells and then performed a transwell migration assay towards rhFck ( for the naïve NK cells , Figure 8B ) or rhRANTES ( for the activated NK cells , Figure 8C ) . The supernatants had to be concentrated 10 fold to achieve efficient inhibition . As can be seen in figure 8B and 8C and similarly to rvMIP-II , the vMIP-II containing supernatants of the KSHV infected cells significantly inhibited the CX3CR1 and CCR5 mediated migration of naïve and activated NK cells , respectively .
In this manuscript , we demonstrate for the first time that the KSHV chemokine vMIP-II antagonizes the activity of two different chemokine receptors expressed by NK cells: CX3CR1 that is expressed on CD56Dim CD16Pos naïve NK cells , and CCR5 that is expressed on activated NK cells . Almost no binding , to any of the cells tested , was observed with vIL6-Ig , with vMIP-I-Ig and with vMIP-III-Ig . Because vMIP-II-Ig did interact with the same tested cells , it is unlikely that the addition of the Ig to vIL6 , vMIP-I and to vMIP-II prevented their binding . However , because it is still possible that the lack of binding of vIL6 , vMIP-I and vMIP-III resulted from the addition of the Ig these results should be taken with caution . Further investigation which is beyond the scope of this manuscript is needed to resolve this issue . On freshly isolated naïve NK cells we show that vMIP-II blocks the binding of Fck to CX3CR1 and inhibits CD56Dim CD16Pos NK cells migration . Previous papers showed that vMIP-II and the other viral chemokines bind various chemokine receptors and act as agonists or antagonists depending on the receptor and on the cell type used [31] . Supporting our results , it was shown that vMIP-II inhibits the chemotactic activity of rat activated leukocytes to MCP-1 , MIP-1β , RANTES , and Fck [33] . Other studies found that vMIP-II antagonizes the action of MIP-1α , MIP-1β , and RANTES on freshly prepared human monocytes [31] , [40] . Fck is a unique molecule that has functional features of both a chemokine ( as a secreted form ) and an adhesion molecule ( as a membrane-bound form ) [41] . It was demonstrated that immobilized Fck induces IFN-γ production by NK cells and that soluble Fck significantly enhances NK cell-mediated killing of target cells [8] , [42] . However , we did not observe any effect of vMIP-II-Ig on the cytotoxicity or IFN-γ secretion of naïve and activated NK cells ( data not shown ) . It was also shown that NK cells migrate towards Fck in a CX3CR1 dependent manner [8] , [43] . As the main cell type permissive for KSHV infection is of endothelial origin , which regularly expresses Fck , it seems reasonable that KSHV developed this vMIP-II-specific mechanism to block NK cell migration to the infection site . KSHV is not the only virus that evades the immune system by interfering with the CX3CR1-Fck axis . Respiratory syncytial virus ( RSV ) G protein includes a CX3C motif that was shown to interact with CX3CR1 and competes with Fck . This competition antagonizes the activities of Fck , thereby decreasing the anti-viral response of CX3CR1Pos cells to RSV infection [44] . In the future , it will be interesting to test whether vMIP-II interferes with the RSV G protein binding to CX3CR1 and vice versa . The CD56Bright CD16Neg NK cells are less cytotoxic; they preferentially release cytokines such as IFN-γ , while CD56Dim CD16Pos NK cells are the principal cytotoxic population . In addition , the CD56Bright CD16Neg subset normally comprises only 10% of the total NK cells in the peripheral blood . Since vMIP-II interacts mainly with the CD56Dim CD16Pos population , it seems as if it is more important for the virus to block NK cell cytotoxicity and not the secretion of cytokines . We observed here that vMIP-II does not interact with CXCR1 . Interestingly , it has been shown that the latency-associated nuclear antigen 1 ( LANA-1 ) protein of KSHV represses IL-8 expression , thus suppressing neutrophils chemotaxis to the infection site [45] . However , there are some reports of several KSHV-encoded genes , expressed during the lytic infection of the virus , that upregulate the expression of IL-8 . Among them are vGPCR [46] , K1 [47] , vFLIP [48] , and K15 [49] , all together underlining the importance of IL-8 for KSHV infection and pathogenesis . Therefore it is reasonable that the virus will not use its vMIP-II for targeting the CXCR1-IL-8 axis . Upon cytokine activation , NK cells become more cytotoxic and the expression profile of killer receptors , adhesion molecules , and chemokine receptors changes [13] , [14] , [50] , [51] . We demonstrate here , in agreement with previous studies [13] , [52] , [53] , that following IL-2 activation , the expression of CXCR1 and CX3CR1 is downregulated . This downregulation is concomitant with an upregulation of a different chemokine receptor , CCR5 . The changes in the chemokine receptors expression between naïve and activated NK cells could be due to different functions of those receptors in the trafficking of NK cells . It was shown that CX3CR1 is involved mainly in the NK cell exit from bone marrow parenchyma under steady-state [54] , while CCR5 regulates the homing of NK cells to inflammatory sites where high concentrations of CCR5 ligands ( MIP-1α , MIP-1β , and RANTES ) have been found [55] . We demonstrated that vMIP-II binds CCR5 on activated NK cells and inhibits the migration of NK cells towards RANTES . Following KSHV infection , there is a significant upregulation of RANTES , the CCR5 ligand , which is involved in immunoregulatory , inflammatory , and cell proliferation pathways [56] , [57] . Therefore , it is important for the virus to target the CCR5-RANTES axis . Additionally , we aimed to delineate the significance of our findings to native infectious settings . However , KSHV does not have a murine viral analogue and the human KSHV cannot productively replicate in mice [58] . Therefore , we adopted the iSLK-KSHV/DOX inducible in-vitro cell based system that is the closest available system to imitate a lytic and latent viral replication environment . We importantly show that KSHV infected cells secrete vMIP-II that efficiently inhibits the migration of both naïve and activated NK cells towards Fck and RANTES , respectively . However to achieve this , the supernatants had to be concentrated 10 times . One must keep in mind that in-vitro cell based systems mimic very poorly the physiological concentrations of endogenously secreted components . While cells that comprise the virally infected tissue in-vivo are densely packed in a three dimensional essentially solid structure , cells grown in culture are only a very thin two dimensional layer in a great body of liquid media that is many folds the volume of liquid surrounding the cells in-vivo . Thus , despite the concentration step , our findings show the importance of vMIP-II in blockade of NK cells during KSHV infection . Our findings have practical consequences which are not directly related to KSHV . CCR5 is one of the two major co-receptors for HIV-1 entry into the host cells [59] . It has been shown that natural ligands of CCR5 can inhibit HIV-1 infection [60] , [61] . As an antagonist of CCR5 , vMIP-II could potentially be used therapeutically against HIV . Another possible application for vMIP-II might be in tumor metastasis therapy . It has been shown that RANTES-CCR5 axis plays a key role in the invasiveness of basal breast cancer cells and that CCR5 antagonists ( such as maraviroc or vicriviroc , both HIV drugs ) blocked tumor invasiveness in-vitro and efficiently reduced metastatic colonization in-vivo [62] . Thus , vMIP-II can be used in therapy for various cancer types to block the metastasis of the tumor cells and inhibit the aggressiveness of the tumor . In summary , we demonstrate here a unique mechanism developed by KSHV in which the virus uses a single protein to block NK cells migration at two different stages through the targeting of two different chemokine receptors . Although chemokines are often able to bind with high affinity to more than one receptor , vMIP-II is a unique example , demonstrating the sophistication of the KSHV virus , as it binds with high affinity to more than one subtype of chemokine receptors ( CC- and CX3C- chemokine receptors ) .
The NK cells and all other blood subsets that were used in this study were obtained from the blood of healthy volunteers . The institutional Helsinki committee of Hadassah approved the study ( Helsinki number 0030-12-HMO ) . All subjects provided a written informed consent . The cell lines used in this paper included 293T and U87 cells . The U87-CCR5 cell line was obtained from the AIDS Research and Reference Reagent Program , Division of AIDS , NIAID , NIH [63] . Monocytes and neutrophils cells isolation was performed as described elsewhere [64] . Monocytes were detected by their physical parameters using a SSC-FSC dot plot , while neutrophils were defined by FACS analysis as CD66b+ CD16+ cells . PBLs and Naïve NK cells isolation from healthy donors and NK cells activation were performed as previously described [65] . NK cells were defined by being positive for anti-CD56-Phycoerythrin ( BD Biosciences ) staining and negative for CD3-Allophycocyanin ( Biolegend ) staining . Staining for the chemokine receptors was performed with conjugated antibodies anti CCR1 , anti CCR2 , anti CCR3 , anti CCR5 , anti CXCR4 , anti CX3CR1 , and anti CXCR1 ( all from Biolegend ) . Alexa Fluor 647 Mouse IgG2b ( Biolegend ) , Alexa Fluor 647 Rat IgG2a ( Biolegend ) , APC Mouse IgG2a ( Biolegend ) , APC Rat IgG2b ( Biolegend ) , APC Mouse IgG2b ( Biolegend ) , and PE Mouse IgG1 ( Dako ) were used as isotype control antibodies . For secondary antibody staining anti-human APC ( Jackson ) were used . Sequences encoding the viral proteins vIL-6 ( Gene ID: 4961449 ) , vMIP-I ( Gene ID: 4961510 ) , vMIP-II ( Gene ID: 4961514 ) or vMIP-III ( Gene ID: 4961436 ) were amplified by PCR from cDNA isolated from the KSHV positive cell line BCBL1 using the following primers: vIL-6-Ig fwd 5′-CCATGCTAGCGCCGCCACCATGCGCTGGTTCAAGTTGTGG-3′ , rev 5′-GGGATCCTTATCGTGGACGTCAGGAGT-3′; vMIP-I-Ig fwd 5′-GGAATTCGCCGCCACCATGGCCCCCGTCCACGTTTTA-3′ , rev 5′- GGGATCCGCTATGGCAGGCAGCCGCTG-3′; vMIP-II-Ig fwd 5′- GGAATTCGCCGCCACCATGGACACCAAGGGCATCCTG-3′ , rev 5′- GGGATCCCGAGCAGTGACTGGTAATTGC-3′; vMIP-III-Ig fwd 5′- CCATGCTAGCGCCGCCACCATGTGGAGCATGTGCTGGGTG-3′ , rev 5′- GGGATCCGGGCATAACCCTTTACCGGC-3′ . These PCR-generated fragments were cloned into the mammalian expression vector containing the Fc portion of human IgG1 ( mutated to abolish the Fc receptor binding site ) , generated in 293T cells and Ig-fusion proteins were purified on a protein G column as described [66] . Sequencing of the constructs revealed that cDNA of all Ig-fusion proteins was in frame with the human Fc genomic DNA and were identical to the reported sequences . All Ig-fusion proteins used in this work migrate as a single band on standard non-reduced SDS-PAGE gels and each was regularly assayed by SDS-PAGE to ensure the proteins had not degraded . Protein purity of all Ig fusion proteins used in this study was around 100% . The CX3CR1 and CXCR1 cDNA was amplified from NK cells and HL60 cell line ( respectively ) and cDNAs were inserted into the pHAGE-DsRED ( − ) -eGFP ( + ) lentiviral vector which also contains GFP . 293T cells were co-transfected with the lentiviral vector encoding CXCR1 or CX3CR1 , a plasmid encoding the lentiviral Gag/Pol , and a plasmid encoding the VSV-G at a 10∶6 . 5∶3 . 5 ratios , respectively . 48 hours after the transfection the supernatant with the viral particles were collected and used to infect 293T cells . The infection percentage was assessed by GFP . NK cells ( 0 . 5×106 , 100 µl ) were placed in the upper well of a transwell filter ( Corning; diameter , 6 . 5 mm; pore size , 5 µm; 24-well cells clusters ) . Filters were then plated in bottom wells containing 600 µL migration medium ( RPMI 1640 with 1% FCS ) supplemented with either rhIL-8 ( 208-IL-050 ) , rhFck ( 365-FR-025 ) , rvMIP-II ( 601-VB-025 ) or rhRANTES ( 278-RN-050 ) ( obtained from R&D Systems ) , as indicated in each figure . At least 3 wells were used for each chemokine . After 3 hours of incubation at 37°C , 5% CO2 , the upper chambers were removed and cells in the bottom chamber were collected and counted using a flow cytometer . For the blocking of migration the NK cells were incubated with the indicated recombinant chemokines for 1 hour in 4°C and then loaded into the upper chamber of the transwell . Migration fold increase ( FI ) was calculated by dividing the number of cells migrating in the presence of chemokines by those migrating toward medium only ( control ) . 293T-CXCR1 transfected cells were incubated with rvMIP-II for 1 hour in 4°C and then stained with IL-8-Ig . The 293T-CX3CR1 cells were incubated with rvMIP-II in the same conditions and then stained with Fck-Ig . U87-CCR5 transfectants were stained with RANTES-Ig following 1 hour incubation with rvMIP-II in 4°C . iSLK-KSHV cells were kindly obtained from Prof . Rolf Renne . This cell line is doxycycline inducible and can undergo lytic replication after doxycycline treatment . The supernatant from the KSHV infected cells has been collected 72 hours post doxycycline induction and then concentrated by spin filtration using a centricon filter of 3 , 000 MWCO ( Millipore ) . vMIP-II levels in the supernatant of KSHV infected cells were determined using mouse anti vMIP-II mAb ( R&D systems ) . | NK cells belong to the innate immune system , able to rapidly kill tumors and various pathogens . They reside in the blood and in various tissues and traffic to different infected organs through the usage of different chemokines and chemokine receptors . KSHV is a master of immune evasion , and around a quarter of the KSHV encoded genes are dedicated to interfere with immune cell recognition . Here , we investigate the role played by the KSHV derived cytokine and chemokines ( vIL-6 , vMIP-I , vMIP-II , vMIP-III ) in modulating NK cell activity . We show that vMIP-II binds and inhibits the activity of two different receptors , CX3CR1 and CCR5 , expressed by naïve NK cells and by activated NK cells , respectively . Thus , we demonstrate here a novel mechanism in which KSHV uses a unique protein that antagonizes the activity of two distinct chemokine receptors to inhibit the migration of naïve and activated NK cells . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"medicine",
"infectious",
"diseases",
"immune",
"cells",
"nk",
"cells",
"virology",
"immunology",
"biology",
"microbiology",
"viral",
"diseases"
] | 2013 | The Viral KSHV Chemokine vMIP-II Inhibits the Migration of Naive and Activated Human NK Cells by Antagonizing Two Distinct Chemokine Receptors |
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